Data Science with Python Training in Los Angeles, CA, United States

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries

data-science-with-python-certification-training

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be of advantage.

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

  • Immersive Learning with a Learn-by-Doing approach.
  • Applied Learning to get your teams project-ready.
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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

  • Measures of Central Tendency
  • Measures of Dispersion 
  • Descriptive Statistics 
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (ANOVA)
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on

  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbour Algorithm
  • Case Study: K-Nearest Neighbour Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a real-life Case Study with ARIMA.

Topics

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modelling on Stock Price

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

O

Ong Chu Feng

Data Analyst

4

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

Attended Data Science with Python Certification workshop in January 2020

R

Rubetta Pai

Front End Developer

5

I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.

Attended PMP® Certification workshop in May 2020

J

Jules Furno

Cloud Software and Network Engineer

5

Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.

Attended Certified ScrumMaster (CSM)® workshop in June 2020

S

Steffen Grigoletto

Senior Database Administrator

5

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Attended PMP® Certification workshop in April 2020

I

Issy Basseri

Database Administrator

5

Knowledgehut is the best training institution. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask for feedback every time and clear all the doubts.

Attended PMP® Certification workshop in January 2020

E

Estelle Dowling

Computer Network Architect.

5

I was impressed by the way the trainer explained advanced concepts so well with examples. Everything was well organized. The customer support was very interactive.

Attended Agile and Scrum workshop in February 2020

H

Hillie Takata

Senior Systems Software Enginee

5

The course material was designed very well. It was one of the best workshops I have ever attended in my career. Knowledgehut is a great place to learn new skills. The certificate I received after my course helped me get a great job offer. The training session was really worth investing.

Attended Agile and Scrum workshop in August 2020

F

Felicio Kettenring

Computer Systems Analyst.

5

KnowledgeHut has excellent instructors. The training session gave me a lot of exposure to test my skills and helped me grow in my career. The Trainer was very helpful and completed the syllabus covering each and every concept with examples on time.

Attended PMP® Certification workshop in May 2020

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Data Science with Python

What is Data Science

Data Science is a blend of various tools, algorithms, and machine learning principles to analyze and manipulate data with the goal to discover hidden patterns from the raw data. Data Scientist not only does the exploratory analysis to discover insights from data but also uses various advanced machine learning algorithms to predict the future. The main focus is to turn raw data into valuable business information. Data scientists must possess a combination of analytical, statistical, machine learning, data extracting skills, as well as experience with algorithms and coding. In business, these data-driven decisions drawn by data scientists can ultimately lead to increased profitability and improved operational efficiency, workflows, and business performance. In customer-facing organizations, data science helps identify and refine target audiences and in providing better customer experience. 

Data Scientists are one of the most promising jobs right now. According to the salaries, opportunities, job openings, etc., the market for Data Science is only going to expand. In Los Angeles, CA, organizations like Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, etc. are continuously searching for Data Scientists to help optimize their business process. The reasons for the popularity of Data Science as a career choice are as follows:

  • Huge demand: Data science experts are needed in almost every job sector. Earlier, analysts would use software like Excel to analyze data, while only academics would turn to SPSS, Stata, etc., but now the technology has advanced and we have many tools for this purpose with various applications like the following:
  • Tableau, Sisense, Microsoft Power BI for your business intelligence department.
  • Programming languages such as R and Python, which let you perform very complicated analyses with very simplified coding and algorithms.
  • Complicated ERPs like SAP and Microsoft Dynamics for your business analysts, HR, supply chain management, etc.

Thus, owing to the introduction of new and efficient tools every now and then, it is important to have skilled people to work with these tools. Therefore, there is a huge demand for skilled data scientists. The applications of data science in almost every sector are increasing and are predicted to increase in the future as well. This leads to an increase in the demand and a consequent increase in salary for data scientists.

  • Low supply: There are still very few programs that educate aspiring data scientists since traditional education still has a long way to go. People are still depending on self-preparation through sources like books, research papers, and online courses. Experts have predicted that 40 zettabytes of data will be in existence by 2020. There is a shortage of skilled professionals in a world which is increasingly turning to data for decision making.  There are still not enough people exploiting the opportunities in this industry leading to a low supply of labor. Industries such as finance, insurance, professional services and IT are the ones desperately seeking professionals with data science skills. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. Because the demand is far outpacing the supply of data scientist talent, the global shortage continues to grow. 4 out of 10 companies report their lack of appropriate analytical skills as a key challenge. Data science is a never-fading industry and hence increase in related positions will lead to a surge in salaries as well.
  • Exciting and fun: Apart from knowledge in code, models, algorithms, or building data pipelines, inspiration and creativity are crucial. Surely, 99% perspiration is a must, but without inspiration, no amount of perspiration is going to get you out of your predicament. Creativity is the key. The job includes taking a business request passed down from an executive and finding an optimal solution using analytics. There are some roadmaps that will help you make the transformation from words to your data solution but it’s really up to your experience and creativity and you can experiment with your knowledge and skills. To ensure that your profile and work inspires you, you must take care of the following 2 main points:
  1. Find an industry that you are passionate about
  2. Stay at the top of your field by having command over your skills so that you get interesting work and aren’t stuck with tasks like data cleansing all the time. 

Besides its financial and economic aspects, data science comes with many exciting aspects. It is a good choice for people who are inquisitive about new things for it has a wide scope for creativity and imagination. It offers a huge space for exploration, and the deeper one dives into it, newer opportunities will unravel for him. 

Los Angeles, California is home to Institutes like University of California that offers Master’s in Data Science. This degree can help you understand the basic concepts of Data Science and learn about all the technical skills required to become a Data Scientist. The top skills that are needed to become a data scientist include the following:

  • Basic tools
  • Statistics
  • Software engineering
  • Machine Learning 
  • Data Cleaning
  • Data Munging
  • Data Visualization
  • Unstructured data

1. Basic tools: You must have a knowledge of statistical programming like R, or Python. Solving a problem in data science involves data preprocessing, data preservation, analysis, visualization, and predictions. Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, sci-kit-learn, etc. in order to perform these functions. In addition to these, advanced Python libraries such as Tensorflow, Pytorch, and Keras provide Deep Learning tools for Data Scientists. R ideal for not just statistical analysis but also for neural networks. In order to be a proficient Data Scientist, it is necessary to extract and operate on data from the database. Therefore, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax and variety of implementations. 

2. Statistics: Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:

  • Probability distributions
  • Statistical significance
  • Hypothesis testing
  • Regression

3. Software engineering: Data scientists can gain huge benefits by learning concepts from the field of software engineering. It allows them to more easily reutilize their code and algorithms, and share it with collaborators. The important concepts include:

  • Modularity
  • Documentation
  • Automation testing

4. Machine Learning: Machine Learning is a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience that is, the data collected over time and analyzed, without being explicitly programmed. It focuses on making future predictions from data available from past experiences. The data scientist feeds good quality data and then train our machines by building machine learning models using the data and different algorithms which depends on what type of data do we have and what kind of task we are trying to automate. Some machine learning methods are as follows:

  • Supervised machine learning algorithms
  • Unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms
  • Reinforcement machine learning algorithms

5. Data Cleaning: Altering and filtering data in such a way that it makes sense is called data cleaning. The general sequential steps to follow are given below:

  • Remove duplicate and irrelevant information from your dataset
  • Fix structural errors
  • Filter unwanted data
  • Handle missing data
  • Checkpoint back the datasets

The tools available to help you with data cleaning are as follows: 

  • Trifacta, 
  • WinPure
  • Alteryx,
  • OpenRefine,
  • Data Ladder,  
  • Paxata, 

6. Data Munging: Data munging, also called data wrangling is the process of mapping the raw data into another format to make it more appropriate and valuable for a variety of downstream purposes such as analytics. The purpose of data wrangling is as follows:

  •  It should provide brief and workable data to Business Analysts
  • Reduce the time and effort spent on collecting and arranging data
  • Reduce the efforts of Data Scientist so that they can focus mainly on analysis rather than wrangling of data
  • Drive better decisions based on data in a short time span

The tools available to perform data munging are as follows:

  • Tabula
  • DataWrangler
  • OpenRefine
  • Python and Pandas
  • CSVKit
  • “R” packages
  • Mr. Data Converter

7. Data visualization: Data visualization methods are an important part of analytics which helps to quickly understand complex data. The process involves the creation of graphical representations of information by utilizing complex sets of numerical or factual figures. The essential data visualization techniques are as follows:

  • Know Your Audience
  • Set Your Goals
  • Choose The Right Chart Type
  • Number charts
  • Maps
  • Pie charts
  • Gauge charts
  • Take Advantage Of Color Theory
  • Handle Your Big Data
  • Use Ordering, Layout, And Hierarchy To Prioritize
  • Utilize Word Clouds And Network Diagrams
  • Include Comparisons

Common types of data visualization are as follows:

  • Line charts
  • Area charts
  • Bar charts
  • Population pyramids
  • Pie charts
  • Treemap
  • Scatter plot
  • Histograms
  • Box plots
  • Bubble charts
  • Heat maps
  • Choropleth
  • Sankey diagram
  • Network diagram

8. Unstructured Data: Unstructured data refers to data that does not follow any order like spreadsheet pages, database tables or other linear or ordered data sets and hence does not fit into the row and column structure of a relational database. Non-textual unstructured data such as MP3 audio files, JPEG images, and Flash video files, etc. and textual data including Word documents, PowerPoint presentations, instant messages, collaboration software, documents, books, social media posts, and medical records are all examples of unstructured data. This has led to a demand for new skills to handle such data, like NoSQL. Some of the tools for the analysis of unstructured data are as follows:

  • MongoDB
  • Cogito Semantic Technology
  • Microsoft HDInsight

Below are the top 5 behavioral traits of a successful Data Scientist -

  • Analytic problem-solving –  Analytical skills are required for detecting patterns, observing, theorizing, brainstorming, interpreting data, integrating new information, and making decisions based on multiple factors and options available. For better problem-solving, you must incorporate the following skills:
    • Active listening
    • Fact-finding
    • Historical analysis
    • Process analysis
    • Causal analysis
    • Needs identification

A data scientist must generate a set of alternative interventions to achieve the end goal. He/She should evaluate the best solutions and implement a plan accordingly.

  • Team player  – Good data scientists can’t stay in their own domains. They should be willing to work with people who come from different points of view. A team has a group of members with different skills, someone strong in technology with someone strong in analytics, boosted with someone strong in business knowledge and someone with a broader view of the latest research and development in academia and industry. Hence, it is important to uphold the team spirit and professionalism to gain fast success. 
  • Research - Apart from the technological skills required for Data Science, it is also important to have an open mind. It is easy for them to pick up on new programming trends in the ever-changing space. Since this technology is evolving day by day, people should be accepting and always ready to learn and pick up the pace in order to not get left behind.

  • Communication skills – Data scientists should be able to communicate with multiple stakeholders using data. This is a key attribute since he/ she needs to communicate the analyses and conclusions. A data scientist should be an effective communicator so that he can easily explain the patterns he observes in the data. Sometimes, you need to present orally or sometimes through a presentation or maybe you need to write a report in some cases. The key points for communication skills are as follows:
    • Active listening
    • Teamwork
    • Oral communication
    • Surveying
    • Reporting
    • Written communication
    • Presentation

Los Angeles, CA is the hub for several corporations that have now realized the potential of Data Science and are now searching for data scientists to help them harness this potential. Examples of such organizations include Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.

A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:

1. Handsome salary: The trend of salaries of data scientists in Los Angeles, CA is: The median salary for a Data Scientist in Los Angeles is $128,189. The average additional cash compensation for a Data Scientist is $16,000 in Los Angeles. The average total compensation for a Data Scientist in Los Angeles, CA is $144,189. Based on your skill set and experience, you can demand higher salaries. The highest paying companies are Facebook, IBM, Accenture, Facebook, Airbnb, and Capital One. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. Data scientists need to have a versatile skill set in their profile as Data Science is a vast domain. Experts have predicted that 40 zettabytes of data will be in existence by 2020.

2. Abundance of positions: There are very few people who have the required skill-set to qualify as a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. Burning Glass Technologies, Business-Higher Education Forum, and IBM predicted that the number of jobs for all data openings will increase by 364,000 by 2020. The various roles offered for a data scientist are as follows:

  • Business Intelligence Analyst
  • Data Mining Engineer
  • Business Analyst
  • Data Scientist
  • Senior Data Scientist
  • Chief Data Officer

3. Data Science is versatile: Data Science is a very versatile field with numerous applications in health-care, banking, consultancy services, and e-commerce industries. Data Scientists not only analyze the data but also improve its quality. Data Science deals with enriching data and making it better for their company. They help industries to automate redundant tasks. Companies are using historical data trend patterns to train machines in order to perform repetitive tasks. This makes data science a versatile career option and you get to work on various domains. There are different types of Data Scientists working in different fields:

  • Statistician
  • Mathematician
  • Data engineer
  • Actuarial scientists
  • Business practitioner
  • Software programming analyst
  • Digital Analytic consultant
  • Spatial Data Scientist
  • Quality Analyst

4. Freedom to work: Data Science is an ever-evolving technology. You are not bound to work for a particular industry. Data Scientist is something that has huge potential. This leads to multiple openings for various roles in all sorts of industries. Recently, the government has also come up with many openings for Data Scientists. You get to learn a lot like technology advances. There is no hard and fast rule to work, no strict path to follow, you must possess the creativity to think beyond the conventional way. You get the freedom to work your own way and put your knowledge in different ways to come up with a solution. 

5. Network: Due to the ever-increasing progress in the field of data science, many conferences and workshops are organized around the globe.  These workshops invite notable expertise and top leaders from various industries to share their ideas and knowledge with others on the latest and upcoming innovations. Many researchers get to present their work. It provides an opportunity for a data scientist to connect with other people from this field and create a strong network. They can be found hanging out with C-level executives. This helps you to get a referral for better roles that you might be seeking. Also, you get to learn the operating process and work culture in different industries. It opens a door to variations of roles and work. You also get to understand your interests as well.

Data Scientist Skills & Qualifications

Below is the list of top business skills needed to become a data scientist: 

  • Analytic Problem-Solving
  • Communication Skills
  • Business acumen
  • Curiosity

1. Analytic Problem-Solving – You must be able to apply your problem-solving skills to derive a conclusion. Decision making requires critical thinking and analytical solutions. Problem-solving skills are required for the following:

  • To apply a useful framework to solve a business problem
  • To use linear regression to generate business insights
  • To determine which analytical method to apply given the nature of the problem and available data

2. Communication Skills – Data insights are usually presented in the form of tables, charts, or any other concise forms which should be elaborated and explained. Some of the important areas in Data Science where communication skills are important are Storytelling and Data Visualization. Data Scientists need to convey ideas and solutions to the stakeholders. It is important to have good body language and confident skills. A data scientist should have good communication skills to translate the ideas in a way understandable by other people. The various aspects of good communication skills are as follows:

  • Listening
  • Nonverbal communication
  • Clarity and conclusion
  • Friendliness
  • Confidence
  • Empathy

3. Business acumen  – The main goal of a data scientist is to translate business problems into data science solutions through the implementation of data science skills. Therefore, it is important to understand the business requirements of the organization you are working with. You must understand how your solutions affect the business on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. This lets you categorize the problems on the grounds of priority. 

4. Data inquisitiveness – Curiosity is key towards acquiring mastery of any quantitative field. Data Science requires someone with expertise and knowledge. One must have a curiosity to learn more and experiment with data. A data scientist should always be inquisitive and should know when and where to ask questions. He/ She should always be ready to learn new things and accept challenges. You must update yourself with articles, blogs, new updates in programming languages, tools, etc. 

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Boot camps focus on teaching topics that flow smoothly and complement each other. There are many roles available in data science.  The key advantages that a boot camp can provide you with:
    • Get into a new role
    • Opportunity to step into a new career
    • Opportunity to develop your career
    • Relatively affordable alternative
    • An extremely short period of time
    • Job prospects
  • Online courses: There are many Data Science courses available online to take up at your convenience. Here is a list of few top online courses available:
    • Data Science Certification from Harvard University (edX)
    • Microsoft Professional Program in Data Science (edX)
    • CS109 Data Science — Harvard
    • Data Science and Statistics Certification by MIT (edX)
    • Python for Data Science and Machine Learning Bootcamp — Udemy
    • Data Science Certification from John Hopkins (Coursera)
    • Applied Data Science with Python Certification (University of Michigan)
    • Statistics and Data Science MicroMasters — MIT (edX)
  • Certifications: Most of the certification programs online are aimed at showing some sort of technical proficiency with a tool, or a base understanding of a method of application like statistics. You can take up certification courses to add some skills to your profile. Data science is vital to nearly every company and industry, but the skills that recruiters are looking for will vary across businesses and industries and the requirement. To add specific skills demanded by your desired industry, you can take up the certifications. 
  • Projects and Research papers: Mini or major projects in data science provides you a practical approach to learn how things work in real life. You get to apply your theoretical knowledge in real time and understand how this technology impacts the industry. 
  • Competitions: You can apply your knowledge by participating in the competitions being organized in Data Science. It will help you to judge yourself and understand where you lie in the race. It provides a great opportunity for learning as well. It provides you an exposure to state of the art approaches and datasets. Some of the platforms for competitions on Data Science are as follows:
    •  Kaggle
    • Innocentive
    • TunedIT
    • Codalab
    • Driven Data
    • CrowdANALYTIX

Data has become a very important part of our lives. It is generated every day and this has increased the demand of Data Scientists. It is their responsibility to mine the data to gain insights that can help the organization make crucial marketing decisions and optimize their business processes. In Los Angeles, organizations that are currently hiring Data Scientist are Amazon Web Services, Sony Pictures Entertainment, UCLA Health, Beyond Limits, Meredith Corporation, Cybernetic Search, Meredith Corporation, AvantStay, Munchkin Inc., NEOGOV, Dataiku, Brillio, Internet Brands, Snapchat, Cybernetic Search, Cedars-Sinai, Zest Finance, Riot Games, Ranker, etc.

To start working on Data Sets and to practice your Data Science skills, you can take up projects to work on. You can do your hands on in datasets available online. You can categorize the datasets into the following three levels:

  • Beginner Level: This level comprises of data sets which are simple and are very easy to work with. They don’t require complex data science techniques. These require basic techniques like regression or classification algorithms. You can sufficient tutorials available online to go through these datasets. Here is a list of such data sets:
    • Iris Data
    • Wine Quality Data
    • Bigmart Sales Data
    • Loan Prediction Data
    • Heights and Weights Data
    • Time Series Analysis Data
    • Boston Housing Data
  • Intermediate Level: This level comprises of data sets which are medium to large in size and a more complex than the beginner level. They require more challenging efforts to solve them. You need to have some advanced pattern recognition skills. Here is the list of such datasets:
    • Movie Lens Data
    • Black Friday Data
    • Human Activity Recognition Data
    • Twitter Classification Data
    • Trip History Data
    • Census Income Data
    • Siam Competition Data
    • Million Song Data
  • Advanced Level: You need to have the knowledge in advanced topics like neural networks, deep learning, recommender systems, etc. This level features high dimensional datasets. The datasets included in this level are as follows:
    • Urban Sound Classification
    • Recommendation Engine Data
    • Chicago Crime Data
    • Age Detection of Indian Actors Data
    • ImageNet Data
    • Identify your Digits
    • Vox Celebrity Data

How to Become a Data Scientist in Los Angeles, California

Below are the right steps to becoming a successful data scientist:

1. Choose an academic path: A Bachelor’s degree in Computer Science, Statistics, mathematics, etc., or related field is important. More and more data scientists are opting for a master's degree and Ph.D. Higher degrees helps to gain Proficiency in Data Management Technologies. You can go for online courses to add skills to your profile. Some of the certifications available are as follows:

  • Dell Technologies Data Scientist Associate (DCA-DS)
  • Data Science Council of America (DASCA)
  • HDP Data Science
  • Certified Analytics Professional (CAP)
  • Microsoft Professional Program in Data Science
  • Cloudera Certified Professional: CCP Data Engineer
  • SAS Certified Advanced Analytics Professional
  • Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate
  • Cloudera Certified Associate - Data Analyst
  • IBM Certified Data Architect

2. Mathematics and statistics: A solid understanding of multivariable calculus and linear algebra is a must for a data scientist. Concepts of probability are the backbone of data science. It forms the foundation of many data analysis techniques. It is important for data scientists to be proficient in math since it simplifies writing algorithms. If you want to become a proficient Data Scientist, then you must be proficient in these topics:

  • Linear Algebra
  • Calculus, Discrete Math 
  • Optimization Theory

Data analysis requires descriptive statistics and probability theory which helps to make better business decisions from data. Key concepts include:

  •  probability distributions, 
  • statistical significance, 
  • hypothesis testing, 
  • regression

3. Fundamentals: Before diving into the depths of Data Science, you must start with the fundamental concepts. You must master the basics to build a strong foundation for future learning. Below are some of the ideas that you can incorporate:

  • Start with statistics
  • Assess your assumptions
  • Suitable sampling
  • Data Engineering
  • Solid programming skills

4. Specializations: Many data scientists will be heavily specialized in business, often specific segments of the economy or business-related fields like marketing or pricing. The need of the companies varies according to their objectives. You must choose a career path to follow and get proficient in one or more technologies related to data scientists. You can go for certifications, boot camps, and online courses. Having worked on related projects will help you upgrade your profile.

5. Apply for jobs: There are several openings every now and then by companies for various posts related to data science. You need to keep yourself updated on these notifications and prepare yourselves for the interviews.

  • One must have a good understanding of programming languages and platforms. Data scientists should know how to write codes and algorithms in order to program the computer to analyze the data. 
  • Work on your mathematics and statistics skills. A good data scientist must be able to comprehend what the data is trying to convey. Thus, you must have concrete basic linear algebra, an understanding of algorithms and statistics skills. 
  • Pursue a degree in computer science, computer engineering, mathematics or closely related fields.
  • Learn some basic skills required in the field of data science such as:
    • Machine learning Tools
    • Cloud Tools
    • Data Mining
    • Data Visualization 
    • Effective Communication
  • People can also go for extra certification courses to bolster their resume.

Los Angeles, CA is home to many leading institutes.A degree from University of Southern California and the University of California can help you get expertise in technical skills of Data Science. Also, it will help improve your CV.

About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree. A degree is very important because of the following – 

  • Networking – During the course, you will be able to start building your network by making friends and acquaintances. This will help you a lot during referrals.
  • Structured learning – While pursuing the degree, you need to follow a schedule to keep up with the curriculum.
  • Internships – During the internship, you will get a practical hands-on experience.
  • Recognized academic qualifications for your résumé – A degree from a reputed institution will improve your CV. 

Los Angeles, CA is home to the University of Southern California and is renowned for its data science degree. A Master’s degree is not necessary to become a Data Scientist. But if you possess it, it is an added benefit and increases your chances of getting hired. The advantages of having a Master’s degree are as follows:

  • Gain Proficiency in Data Management Technologies
  • Become More Readily Employable
  • Become Indispensable
  • Earn a More Impressive Salary
  • Gain Credibility

Knowledge of programming is perhaps the most important and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include the following: 

Data sets are basically a collection of data. Algorithms are written to work on these data sets, therefore it is very essential to have a command over one or more programming languages. 

  • Statistics: The most important aspect of Data Science is Statistical Thinking. It is a must for a Data Scientist to have required statistical knowledge. While conventionally, statisticians in Statistics could study Data Science, it is now possible for people without any formal degree to study Data Science. There are various books and resources available online that offer statistical insights about Data Science and teach practical aspects of it. Statistics are divided into two categories –
    • Descriptive Statistics
    • Inferential Statistics     
  • Framework: The most recommended framework for Data Science is Hadoop which is an open-source software framework. The benefits of Hadoop are features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.

It is heavily preferred in several data science projects for processing of large data sets. Tools are evolving for pulling information out of Hadoop clusters:

    • Tableau
    • Revolution R Enterprise
    • Hive

Data Scientist Salary in Los Angeles, California

The average income of Data Scientist in Los Angeles is $98,294.

As compared to the average salary of a Data Scientist of $100,450 in New Jersey, the salary in Los Angeles is $2,156 less. 

In Los Angeles, the average salary of a data scientist is $98,294 as compared to $110,925 in Chicago.

The average income of a data scientist in Los Angeles is $98,294 as compared to $125,310 in Boston.

In California, cities like San Francisco and San Diego have an average pay of $119,953 and $97,183 respectively for data scientists. 

The Data Scientists in California are in high demand right now. 

Here are the benefits of being a Data Scientist in Los Angeles:

  • Handsome salary
  • Chance to grow
  • Be a part of a Data Science team from the start
  • Multiple job opportunities

Being a Data Scientist in Los Angeles offers several perks and advantages. There are opportunities to connect with different people in various conferences, summit, and meetups. Data Scientists play a major role in gathering useful insights after analyzing the raw data. This puts them in connection with top-level executives. Also, they have the luxury to work in the field that a person is interested in.

In Los Angeles, companies hiring Data Scientists include Snap Inc. and Capital Group. 

Data Science Conferences in Los Angeles, California

S.NoConference nameDateVenue
1.Data Science Salon, Los AngelesNovember 7, 2019Red Bull Media House
2.Data Con LAAugust 17, 2019The University of Southern California
3.IDEAS SoCal AI & Data Science Conference 2019, Los AngelesSat, October 26, 2019Los Angeles Convention Center
4.SatRday LA – R Conference
April 6, 2019
UCLA James West Alumni Center
5.Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA)
Friday, June 14, 2019
Loyola Marymount University (LMU) in Playa Vista
6.ACM IUI 2019, Los Angeles
March 16 to March 20, 2019
Marriott Marina Del Re
7.IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles, CA, USA
Dec 9, 2019 - Dec 12, 2019
Los Angeles, CA, USA
8.John Langford @ ZEFR
Saturday, June 15, 2019
ZEFR, 4101 Redwood Ave · Marina Del Rey, ca
9.Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications
Thursday, May 9, 2019
Verizon Digital Media Service
10.ROpenSci Unconference, Los Angeles (USA)
May 25, 2019-26, 2019
Los Angeles (USA)

1. Data Science Salon, Los Angeles

  • About the Los Angeles conference: The conference is to unite the brightest leaders in the media and entertainment in Los Angeles to innovate new solutions.
  • Event Date: November 7, 2019
  • Venue: Red Bull Media House
  • Days of Program: 1
  • Timings: 8:00 AM – 8:00 PM PDT
  • Purpose: Learn how to solve real-world problems by harnessing disruptions in data, artificial intelligence, machine learning, and cutting-edge technologies.
  • How many speakers: 20
  • Registration cost: $175 for Group and $250 for Individuals 

2. Data Con LA, Los Angeles

  • About the Los Angeles conference: It is supported by a community of volunteers, sponsors and speakers and is the largest of its kind, data conference in Southern California. 
  • Event Date: August 17, 2019
  • Venue: The University of Southern California
  • Days of Program: 1
  • How many speakers: 7
  • Speakers & Profile:
    • Joe Devon, Founding partner at Diamond and Co-Founder of GAAD
    • Anthony Rose, Lead Data Scientist at SpaceX
    • Keila Banks, International Speaker, Programmer, Entrepreneur
    • Paddy Hannon, CTO, Headspace
    • Ani Okkasian, Associate Director at OMD USA/ Professor at Woodbury University
    • Elizabeth Owen, Director of Learning and Data Science at Age of Learning
    • Ron Galperin, L.A. Controller
  • Registration cost: $40

3. IDEAS SoCal AI & Data Science Conference 2019, Los Angeles

  • About the Los Angeles conference: The conference will cover topics including industry trends, data science applications, open-source software, machine learning, and many others. This is the must-attend event for anyone already in the field or just looking to explore career in Data Science.
  • Event Date: Sat, October 26, 2019
  • Venue: Los Angeles Convention Center
  • Days of Program:
  • Timings:  9:00 AM – 5:00 PM PDT
  • Purpose: The conference will cover a diverse selection of trending topics through multiple tracks, including Artificial Intelligence & Automation, Big Data and Infrastructure, Machine Learning and Deep Learning, Data Visualizations, Data Analytics, Healthcare & IOT, Business Practice, and Data Security, etc
  • Registration cost: $45 – $80

4. SatRday LA – R Conference, Los Angeles

  • About the Los Angeles conference: SatRday is a one-day affordable, inclusive, non-profit R conference organized by local R users. 
  • Event Date: April 6, 2019
  • Venue: UCLA James West Alumni Center
  • Days of Program: 1
  • Registration cost: $35 for professionals, $15 for students

5. Microsoft Reporting & Analytics by Ted Stathakis (#SQLSatLA), Los Angeles

  • About the Los Angeles conference: Spearheaded by Ted Stathakis, a seasoned veteran of SQL Server Reporting Services, this event will provide a deep insight into SQL Server Reporting Services and Power BI
  • Event Date: Friday, June 14, 2019
  • Venue: Loyola Marymount University (LMU) in Playa Vista
  • Days of Program: 1
  • Timings: 8:30 AM to 4:30 PM (PDT)
  • How many speakers:
  • Speakers & Profile: Ted Stathakis, Data Technology Consultant & SSRS Expert

6. ACM IUI 2019, Los Angeles

  • About the Los Angeles conference: ACM IUI 2019 is the 24th annual meeting of the intelligent interfaces community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces.
  • Event Date: March 16th to March 20th
  • Venue: Marriott Marina Del Rey
  • Days of Program:  5
  • How many speakers:
  • Speakers & Profile: 
    • Dr. Michelle Zhou, Co-Founder and CEO of Juji, Inc.
    • David Gunning , DARPA program manager in the Information Innovation Office (I2O)
    • Ashwin Ram, Technical Director of AI in the Office of the CTO for Google Cloud
  • Registration cost: $850-$950 ($450-$550 for students)
  • Who are the major sponsors:
    • IIBM Research
    • Adobe
    • FXPAL
    • Google
    • Tableau
    • Instabase

7. IEEE BigData 2019: IEEE International Conference on Big Data, Los Angeles

  • About the Los Angeles conference: Started in 2013, the conference will cover Big Data Research, Development, and Applications. 
  • Event Date: Dec 9, 2019 - Dec 12, 2019
  • Venue: Los Angeles, CA, USA
  • Days of Program: 4
  • Purpose: The purpose is to disseminate the latest results in Big Data Research, Development, and Applications.
  • How many speakers:
  • Speakers & Profile: 

    • Lise Getoor, Professor, CS Department, Director, D3 Data Science Center, UC Santa Cruz, USA
    • Ramanathan Guha, Founder and Lead, DataCommons.org, Google, USA
    • Ling Liu, Professor, School of Computer Science, Georgia Institute of Technology, USA
    • Yang Qiang, New Bright Professor of Engineering, Chair Professor and Head, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China

8. John Langford @ ZEFR, Los Angeles

  • About the Los Angeles conference: The conference will have a discussion on the applications of reinforcement learning. The discussion will be led by John Langford, he is a Principal Researcher at Microsoft and the primary author of Vowpal Wabbit. 
  • Event Date: Saturday, June 15, 2019
  • Venue: ZEFR, 4101 Redwood Ave · Marina Del Rey, CA
  • Days of Program: 1
  • Timings: 6:30 PM to 8:30 PM

9. Using Apache Cassandra and Apache Kafka to Scale Next Gen Applications, Los Angeles

  • About the Los Angeles conference: The conference will cover topics on how to identify good application candidates for Apache Cassandra and Kafka as well as best practices and common pitfalls.
  • Event Date: Thursday, May 9, 2019
  • Venue: Verizon Digital Media Services
  • Days of Program: 1
  • Timings: 6:30 PM to 8:30 PM
  • How many speakers: 1
  • Speakers & Profile: Adam Zegelin, SVP Engineering and Co-Founder, Instaclustr
  • As Instaclustr's founding software engineer

10. ROpenSci Unconference, Los Angeles (USA)

  • About the Los Angeles conference: This event will bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profit to get together for a few days and hack on various projects.
  • Event Date: May 25-26, 2019
  • Days of Program:
  • Purpose: This is a great gathering focused on the use of R for open science.
S.NoConference nameDateVenue
1.XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries4 April 2017, - 5 April, 2017
2.Southern California Data Science Conference 2017October 22, 2017Visit Pasadena, 300 E Green St, Pasadena, CA 91101, USA
3.Data Science SALON14 December, 2017

4.XLIVE Data & Analytics Summit
3 April, 2018 - 4 April, 2018
Hudson Loft, 1200, S Hope St, Los Angeles, CA 90015
5.Big Data Day LA
11 August, 2018

3670 Trousdale Pkwy, Los, Angeles, CA 90089, United States

6.Data Science Salon
13 September, 2018

Red Bull Media House, 1740 Stewart St, Santa Monica, CA 90404

7.Converge: The Intersection of Data Science, Market Research & Analytics
4 - 5 December, 2018

The Westin Bonaventure Hotel & Suites, 404, S. Figueroa Street, Los Angeles, CA 90071

1. XLIVE Data & Analytics Summit, executive-level forum across music, entertainment, sports, and culinary industries, Los Angeles

  • About the conference: It helped its attendees to understand and learn about various tools and technologies used in festivals and live events for a better experience.
  • Event Date: 4 April, 2017 - 5 April, 2017
  • Days of Program: 2
  • Purpose: The purpose of this conference was to discuss the latest technologies in data science that can be incorporated to provide a better and technologically enhanced experience at events.

2. Southern California Data Science Conference 2017, Los Angeles

  • About the conference: It allowed its attendees to develop knowledge in the latest technologies and future prospects in Data Science and develop a conceptual understanding of AI toolset, machine learning, Python, etc. 
  • Event Date: October 22, 2017
  • Venue: Visit Pasadena, 300 E Green St, Pasadena, CA 91101, USA 
  • Days of Program: 1
  • Timings: 8:00 am – 5:00 pm
  • Purpose: The conference aimed to cover the important topics in data science like Big Data, Data Analytics, Fintech, and Artificial Intelligence.
  • Registration cost: 
    • Machine Learning, Price: $200
    • Business Analytics Corporate Bootcamp, Price: $200
  • Who were the major sponsors:
    • IBM
    • Intel

    3. Data Science SALON, Los Angeles

    1. About the conference: It brought together technical experts and specialists working in data science in the field of media and entertainment to discuss the latest trends and discuss the several challenges in data science technologies. 
    2. Event Date: 14 December, 2017
    3. Days of Program: 1
    4. Purpose: It provided a platform for data science specialists working in the areas of media and entertainment to come together and impart knowledge on best practices, latest trends and discover innovative solutions to the challenges faced by the industry.
    5. How many speakers: 20
    6. Registration cost: 

      • Individual: $250
      • Group: $175

      4. XLIVE Data & Analytics Summit, Los Angeles 

      • About the conference: The conference focussed on latest tools and technologies used in live events for a better experience.
      • Event Date: 3 April, 2018 - 4 April, 2018
      • Venue: Hudson Loft, 1200 S Hope St, Los Angeles, CA 90015
      • Days of Program: 2
      • Purpose: The purpose of the conference was to enhance the experience through data science and analytics to lead to digital transformation in the festival and live events. 
      • Speakers & Profile:
        • Theresa Locklear, Vp – Audience Science Analytics, Viacom
        • Joe Kessler, Global Head, Uta Iq
        • Nick Sakiewicz, Commissioner, National Lacrosse League
        • Sean O’brien, Director of Analytics & Research, Maple Leaf Sports & Entertainment
        • Andrea Bailey, Vice President | Partnership Activations, Aeg Global Partnerships
        • Steve Scebelo, Vice President, Licensing and Business Development, Nfl Players Inc
        • Patrick Ryan, Co-founder, Eventellect
        • Umesh Johari, Senior Manager – Strategy & Analytics, San Francisco 49ers
        • Glenn Peoples, Music Insight and Analytics, Pandora
        • Rachel Noonan, Director of Marketing Communications & Strategy, Toronto International Film Festival
        • Keith Bendes, Vp Marketing & Strategic Partnerships, Float Hybrid
        • Jesse Grushack, Senior Strategy & Product, Consenys
        • Erin Prober, Director of Strategic Partnerships, La Clippers
        • Nicholas Horbaczewski, Ceo, Drone Racing League
        • Jonathan Healey, Vp of Marketing & Digital Strategy, Dayglo Ventures
        • Kyle Burkhardt, Director – Business Intelligence, Los Angeles Kings
        • Charles Truong, Chief Digital Innovation, Pixmob
        • Sohrob Farudi, Ceo, Fan Controlled Football League
        • Sean Kundu, Vp of New Ventures, San Francisco 49ers
        • Frantz Cayo, Senior Director – Programming & Talent, Bet
        • Scott Blackburn, Ceo, Thuzi
        • Jay Tucker, Executive Director, Center for the Management of Enterprise in Media, Entertainment & Sports
        • Blake Lawrence, Ceo, Opendorse
        • Georgia Sapounas, Director of Digital, Canadian Olympic Committee
        • Sam Alpert, Vp of Marketing, Paradigm Talent Agency
        • Michael Parnes, Vp, Research, Adult Swim
        • Stephanie Laichi, Marketing Director, Pop Montreal

        5. Big Data Day LA, Los Angeles

        • About the conference: This conference provided its attendees with a better understanding of trends in data communication including machine learning and the Internet of Things, through presentations, demonstrations, and networking.
        • Event Date: 11 August, 2018
        • Venue: 3670 Trousdale Pkwy, Los Angeles, CA 90089, United States
        • Days of Program: 1
        • Timings: 7 A.M. to 7:30 P.M.
        • Purpose: The purpose of the conference was to give an insight to its attendees on emerging technologies in data science like technologies that support Database(Hadoop, Big Data, RDBMS), AI, Machine Learning, Data Analytics, etc.
        • Speakers & Profile:

          • Aaron Williams - Vp of Global Community at Mapd
          • Alyssa Columbus - Datanaut at Nasa
          • Amanda K Moran - Technical Evangelist at Datastax
          • Amandeep Khurana - Ceo & Co-founder at Okera
          • Andrew Barkett - Vp Engineering at Rex - Real Estate Exchange
          • Brian Gold - Founding Member, Flashblade at Purestorage
          • Charmee Patel - Product Innovation - Data & Analytics at Syntasa
          • Harry Brisson - Director, Media Lab at Nielsen
          • Indrasis Mondal - Director, Big Data Engineering & Data Products at Hulu
          • Joe Franklin - Data Science Curriculum Developer at Qlik
          • Jörg Schad - Technical Lead Community Projects at Mesosphere
          • Jules Damji  - Spark Developer & Community Advocate at Databricks
          • Chia-yui Lee - Data Science at Tibco
          • Chris Calvert - Co-founder & Vp Product Strategy at Respond Software
          • Chris Stephens - Data Engineering Manager at Netflix
          • Ebrahim Fontaine - Director Data Science at Edmunds
          • Gary Nakanelua - Managing Director at Blueprint Technologies
          • Grant Kushida - Managing Director at Blueprint Technologies
          • Justine Cocchi - Technical Evangelist at Microsoft
          • Karthik Ramasamy - Co-founder & Chief Product Officer at Streamlio
          • Kevin Nelson - Architect Advocate at Google Cloud
          • Luis Bitencourt-emilio - Co-founder & Cto @ Novi Finance
          • Mariana Danilovic - Managing Director at Hollywood Portfolio
          • Marie Smith - Cio at Data 360
          • Mark Quinsland - Field Engineer at Neo4j
          • Michael Malgeri - Principal Technologist at Marklogic
          • Miguel Angel Campo-rembado - Vp Data Science & Analytics at Fox
          • Nader Fathi - Ceo at Kiana Analytics
          • Pat Alwell - Solutions Engineer at Hortonworks
          • Russel Ladson - Ceo at Drop Software Inc.
          • Ryan Measel - Co-founder & Cto at Fantasmo.io
          • Sathwik Shirsat - Big Data Engineer at Malwarebytes
          • Seth Stodder - Partner at Holland & Knight Llp
          • Shane Johnson - Senior Director of Product Marketing at Mariadb
          • Shant Hovsepian - Co-founder & Cto at Arcadia Data
          • Shilpa Balan - Assistant Professor at California State University-los Angeles
          • Sooraj Akkammadam - Etl Architect at Core Digital Media
          • Steven Philips - Principal Software Engineer at Dremio
          • Suraj Kulkarni - Manager, Data Engineering - Data & Ai at Malwarebytes
          • Sushree Mishra - Senior Sales Engineer at Syncsort
        • Registration cost: $40
        • Who were the major sponsors:
          • USCMarshall
          • Gilisium
          • BMC
          • Accenture
          • Arcadia Data
          • Couchbase
          • Datastax
          • HortonWorks
          • MariaDB
          • Neo4j
          • Purestorage
          • Qlik
          • Streamlio
          • syncsort

          6. Data Science Salon, Los Angeles

          • About the conference: It brought together technical experts and specialists working in data science in the field of media and entertainment to discuss the latest trends and discuss the several challenges in data science technologies. 
          • Event Date: 13 September, 2018
          • Venue: Red Bull Media House, 1740 Stewart St, Santa Monica, CA 90404
          • Days of Program: 1
          • Timings: 7:45 AM to 7:00 PM
          • Purpose: It provided a platform for data science specialists working in the areas of media and entertainment to come together and impart knowledge on best practices.
          • Speakers & Profile:
            • Sam Vidal - Cto, Advrtas
            • Scott Edwards - Director of Data Science, Nbcuniversal
            • Natasha Ericta - Vice President, Research and Data Science, National Research Group
            • Jeffrey Rosenberg - Director of Data and Analytics, Hulu
            • Subash D’souza - Director of Big Data & Ops, Data Intelligence, Warner Bros
            • Clara Shin - Business Insights Analyst, Disney Interactive
            • João Fiadeiro - Product Manager, Youtube
            • Garima Garg - Data Scientist, Buzzfeed
            • Robert Parviainen- Head of Data Science, Seriously Digital Entertainment
            • Jeanne Holm - Senior Technology Advisor to the Mayor & Cio, City of La
            • Xavier Kochhar - Founder & Ceo, the Video Genome Project Now Hulu
            • Joe Devon - Tech Entrepreneur and Advisor
            • Jen Walraven - Manager, Science & Analytics, Netflix
            • James Oliphant - Director of Data Science, Simpli.fi
            • Josh Muncke - Director of Data Science, Red Bull North America
            • Jason Brancazio - Senior Software Engineer, Red Bull Media House
            • Piero Cinquegrana - Director of Data Science Pm, Qubole
            • Fernanda Carapinha - Founder and Ceo, 4digital
            • Jasmeet Bhatia - Machine Learning Specialist, Google
            • Lori H. Schwartz - Tech Cat, Storytech™
            • Anuj Saini - Solutions Architect, Sapient
          • Who were the major sponsors:
            • Vertica
            • O’Reilly
            • Qubole
            • Google
            • Redbull Media House
            • Plotly

            7. Converge: The Intersection of Data Science, Market Research & Analytics, Los Angeles

            • About the conference: The attendees learned the application of advanced analytics in traditional marketing research.
            • Event Date: 4 - 5 December, 2018 
            • Venue: The Westin Bonaventure Hotel & Suites, 404 S. Figueroa Street, Los Angeles, CA 90071
            • Days of Program: 2
            • Purpose: The purpose of the conference was to understand the real-time application of machine learning and AI in companies.
            • How many speakers: 5
            • Speakers & Profile:
              • Marian Anderson - director of market research at Microsoft
              • Karin Kricorian - Director, Decision Science and Integration at the Walt Disney Company
              • Camille Nicita - CEO, Gongos, Inc.
              • Dr. A.K. Pradeep - Author, AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales
              • Sarah Tarraf - Statistical Analyst and Statistical Consultant, Gongos Inc.
            • Who were the major sponsors:
              • GreenBook
              • DisQo
              • Research America

            Data Scientist Jobs in Los Angeles, California

            Here is the logical sequence of steps you should follow to get a job as a Data Scientist.

            1. Getting started
            2. Mathematics and statistics
            3. Libraries and datasets
            4. Data visualization and related tools
            5. Data preprocessing
            6. Machine Learning and Deep Learning
            7. Natural Language processing
            8. Polishing skills

            Follow the below steps to increase your chances of success if you are an aspiring data scientist:

            • Study: You should have your concepts cleared and a concrete understanding of related technologies for the interviews. You might be given some algorithms or some problem statement to test your analytical skills. Although the skills required varies according to the demands of the company, but a few common topics are necessary for all data science interviews which include-
            • Probability
            • Statistics
            • Statistical models
            • Machine Learning
            • Understanding neural networks
            • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network. It helps you to expand your professional connections. You get to understand the latest and upcoming trends and the new tools being introduced. Notable researchers from various fields come together to share their work. Not only you gain insights from these conferences, but you can also apply it in your problems. Speakers from top organizations are invited plus other employees from different sectors. You also get to understand their work culture and keep a track of opportunities.
            • Competitions: You can apply your knowledge by participating in the competitions being organized in Data Science. It will help you to judge yourself and understand where you lie in the race. It provides a great opportunity for learning as well. It provides you an exposure to state of the art approaches and datasets. Some of the platforms for competitions on Data Science are as follows:
              •  Kaggle
              • Innocentive
              • TunedIT
              • Codalab
              • Driven Data
              • CrowdANALYTIX
            • Referral: You need to have a strong professional network with people holding designated positions in their institutions to help you get a referral in their companies. Referrals are the primary source of interviews in data science companies. So, make sure your LinkedIn profile is updated.

            • Interview: If you think you are prepared enough and you are confident with your skills, you can go with the interviews. Learn from the questions and make a note of the topics you feel less confident about or where you got stuck in previous interviews. Even if you don’t make through some of the interviews, you will be able to checkpoint your weaknesses and strengths.

            A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs. 

            In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.

            Data Scientist Roles & Responsibilities:

            • Mitigating fraud and risk: Data scientists are trained to identify data and pattern that stands out in some way. They create statistical, algorithms, network, path, and big data methodologies for predictive fraud tendency models and use those to create alerts that help the business to ensure timely responses when unusual data is recognized.
            • Delivering relevant products: One of the advantages of data science is that organizations can determine by analyzing the data and figures, when and where their products sell best and in what methodology. This can help deliver the right products at the right time, identify the demands, identify the potential customers and can help companies develop new products to meet their customers’ needs.
            • Personalized customer experiences: One of the most notable benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level. They can analyze the customer data and trends to understand the thought process and demands of the customers and can suggest options accordingly. By keeping a track of the data, the customized experience is provided to the customers.

            The average annual salary of a Data Scientist in Los Angeles, CA is $128,189. A Data analyst earns upto $62,088 per year while a database administrator makes about $73,622 per year.

            The career path in the field of Data Science can be explained in the following ways:

            Business Intelligence Analyst: A business intelligence analyst is responsible for analyzing data that is used by a business or organization that supports it in decision making. They perform tasks such as defining, reporting on or otherwise developing new structures for business intelligence to serve a specific purpose. Report writing can be a vital element for this role. They ensure that the business is always in the most favorable position by comparing data to competitors and observing industrial trends then creating reports and communicating the same to the organization.  

            Data Mining Engineer: Data Mining Engineer creates and boosts statistical and predictive models and algorithms to vast data sets. They will be working as an engineer to productize, facilitate and implement the systems needed for the analysis and must be able to explain and present hypotheses and analysis results to a wide audience in a clear and concise manner.

            A Data Mining Engineer has underlined functions:

            • Automating data reliability and quality checking.
            •  Providing data access via databases and API services
            • Detecting and remediating production issues
            • Tracking data usage and data access performance
            • Creating data flow and transformation pipelines 
            • Generating derived datasets

            Data Architect: A data architect builds and maintains a company’s database by identifying structural and installation solution.

            • Data cleaning
            • Elastic working and functioning.
            • Data warehousing
            • ETL working
            • Data modeling 

            Data Scientist: The data scientist is an analytical data expert having the technical skills required to solve complex problems. Data scientists are responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy.  The various roles include the following:

            • NoSQL
            • Advanced predictive algorithm
            • ETL Logic
            • Big Data processing
            • Data advanced algorithm

            Chief Data Officer: The chief data officer is a senior executive who surveys a wide range of data related functions through data processing, data mining,  analysis and other measures for entity-wide governance and utilization information as a resource.

            Below are the top professional organizations for data scientists in Los Angeles, CA – 

            • Customer Data Science – Los Angeles
            • SoCal Data Science
            • LA Data Science & Artificial Intelligence Community
            • Data Science Professional Development Los Angeles
            • Data Science LA

            Referrals are the most effective way to get hired. Some of the other ways to network with data scientists in Los Angeles, CA are:

            • Attend Data science conferences and workshops
            • Paper presentations
            • Use an online platform like LinkedIn
            • Follow influential data scientists
            • Participate in Data Science competitions

            There are several career options for a data scientist – 

            • Data Scientist
            • Machine Learning Scientist
            • Business Intelligence Developer
            • Data Analyst
            • Decision Scientist
            • Statistician
            • IoT Specialist
            • Business Analyst

            We have compiled the key points, which the employers generally look for while hiring data scientists:

            • Education: A Bachelor’s degree is a must in Computer Science or related fields. Many institutes are also introducing special courses on data science. Having a Master’s or a Ph.D. in Data Science will boost up your profile and will help you get recognition among various candidates. The advantages of having higher degrees are as follows:
              • Gain Proficiency in Data Management Technologies
              • Become More Readily Employable
              • Become Indispensable
              • Earn a More Impressive Salary
            • Programming: There are no prerequisites for learning a programming language like Python, aside from basic computer skills. Python is a great programming language for data scientists. It is the preferred choice for data scientists. Apart from that SQL is a must no matter what you specialize in..
            • Machine Learning: Machine Learning is a fast emerging technology. It will be beneficial or you to gain some hands-on experiences on this. It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:
              • Random Forest
              • Clustering (for example K-means)
              • Logistic Regression
              • K Nearest Neighbor
              • Linear Regression.

            • Projects: Many projects are available to take up online. You can refine your search by choosing your level of difficulty, starting from beginner to proficient, based on your knowledge and confidence with the tools and technology. Such projects will boost your profile and help you get closer to your desired job.

            Data Science with Python Los Angeles, California

            • Python is a multi-paradigm programming language - this means to say that the various facets of Python are most suited for the field of Data Science. It is a structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. 
            • The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. The huge number of dedicated analytical libraries and packages that are tailor-made for use in data science are some of the main reasons why data scientists prefer the use of Python for Data Science projects, as opposed to any other programming language.
            • Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point or problem while developing a Python program or model for Data Science. 

            • The vast Python community is another big advantage that Python has over other programming languages. Since there are millions of developers working on the same problems with the same programming language every day, it is very easy for a developer to get help in resolving his/her problems because the chances are that someone else had been stuck at the same problem in the past and its resolution has already been found. If no one else has encountered a similar problem, the Python community is quite helpful and tries its best to help their fellow Data Science in Python developers.

            As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.

            • R: The features of R that makes it suitable for data science programming are as follows:
              • It supports matrix arithmetic.
              • R is an interpreted language.
              • Supports object-oriented programming with generic functions.
              • Supports procedural programming with functions.
            • Python: Python makes it easier for the user to implement solutions in data science while following the standards of required algorithms. The top features of Python which make it desirable are as follows:
              •  Supports OOPS.
              • Memory management.
              • Highly readable
              • Free and open source
              • High performance
              • Clean visual layout
            • SQL:  In order to be a proficient Data Scientist, it is necessary to extract and operate on data from the database. Therefore, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax and variety of implementations. SQL is for manipulating, updating, and querying databases.
            • Scala: It is a general-purpose programming language operating on JVM. The features of Scala are as follows:
              • Statically typed
              • Object-oriented
              • Type inference
              • Concurrency control
              • String interpolation
              • immutability

            Follow these steps to successfully install Python 3 on windows:

            • Download and setup: Go to the download page and set up your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from the terminal.

            Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:

            python --version

            • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

            python -m pip install -U pip

            Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.

            To install python 3 on Mac OS X, just follow the below steps:

            1. You should install GCC first which can be obtained by downloading Xcode, the smaller Command Line Tools (must have an Apple account) or the even smaller OSX-GCC-Installer packageInstall.
            2. While OS X comes with a large number of Unix utilities, a package manager is a key component. 
            3. To install Homebrew, open Terminal or your favorite OS X terminal emulator and run

            $ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

            1. Insert the Homebrew directory at the top of your PATH environment variable. For this, add the following line at the bottom of your ~/.profile file

            export PATH="/usr/local/opt/python/libexec/bin:$PATH"

            1. You can install Python 3 by writing the following code

            $ brew install python

            Data Science with Python Certification Course in Los Angeles, CA

            The land of dreams - that?s LA for you. And not simple black and white dreams, but colourful, three dimensional celluloid dreams. This is the land of Hollywood, of pristine white beaches, beautiful people, rock stars, great writers, directors, and actors. While entertainment is a huge economic driver, the city has seen exceptional growth in the international trade, aerospace, technology, petroleum, retail, and real estate sectors too. The city is also home to the University of Southern California (USC) which attracts students from all over the world. KnowledgeHut offers several courses that help you start off your career in LA including, PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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