Data Science with Python Training in Irvine, 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.
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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

E

Elyssa Taber

IT Manager.

3

I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.

Attended Agile and Scrum workshop in June 2020

R

Rosabelle Artuso

.NET Developer

5

The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Attended PMP® Certification workshop in August 2020

Y

York Bollani

Computer Systems Analyst.

5

I had enrolled for the course last week at KnowledgeHut. The course was very well structured. The trainer was really helpful and completed the syllabus on time and also provided real world examples which helped me to remember the concepts.

Attended Agile and Scrum workshop in February 2020

Y

Yancey Rosenkrantz

Senior Network System Administrator

5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

K

Kayne Stewart slavsky

Project Manager

5

The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

Attended PMP® Certification workshop in June 2020

B

Barton Fonseka

Information Security Analyst.

5

This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.

Attended PMP® Certification workshop in July 2020

G

Goldina Wei

Java Developer

5

Knowledgehut is the best platform to gather new skills. Customer support here is very responsive. The trainer was very well experienced and helped me in clearing the doubts clearly with examples.

Attended Agile and Scrum workshop in June 2020

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Data Science with Python

What is Data Science

Investments in the digital enterprise have been drastically increased over the past few years. It has been estimated that by 2020, the IT field will be monitoring 50 times more data than it is today. An interdisciplinary field, data science deals with processes and systems that are used to extract knowledge from large amounts of data. 

Known as the ‘city of innovation’, Irvine has become a big tech hub and home to numerous companies, start-ups and innovative people. Home to several leading companies, Irvine is a great place to kickstart your Data Science career because of the multiple opportunities of growth in the city. Some of the corporations offering jobs to Data Scientists include Amazon Web Services, Ten-X, UST Global, Karma Automotive, BookingPal, Allergan, etc. 

Looking for a job in data science at Irvine, CA? Here are five reasons why it’s the best career choice for you:

  1. Data science helps companies understand their customers better. They can connect with them in a personalized manner, thus helping in better brand power.
  2. It is a new field that’s constantly growing. With the increase in the number of tools, it’s helping organizations solve complex IT and resource management problems in a strategic manner. This implies effective use of resources.
  3. The location of the organization is advantageous as Irvine is filled with innovative companies and this kind of environment promotes team work, collaboration and tight-knit environment.
  4. The results of data science can be applied to almost any sector like travel, healthcare, education etc. Irvine has been flourishing in the fields of healthcare and education, thus allowing data scientists to help analyse their challenges and tackle them effectively.
  5. Organizations in Irvine are looking out for fresh graduates due to their high qualification in data science, making it one of the highest paying jobs.

All these factors teamed up with the need for proper utilization of data will hold the key for achieving the set targets for both companies and individuals.

We have developed from complete randomness to finding patterns, predictions to calculations etc. The enablers of such drastic change, that is, the data scientists keep discovering solutions to the most complex problems, create patterns and filter them to give the best results. The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. While curiosity is the major catalyst for a career choice like this, there are many technical skills that one must possess in order to not just get hired, but also to thrive.

  • C/C++ and Java Coding: These are common coding languages that form the basis of any technical job.
  • Python coding: Its versatility as a coding language allows the smooth functioning of almost all steps in data science. Data scientists have to deal with huge amounts of data (big data). Due to its simplicity of use and a large set of python libraries, it has become the preferred language to handle big data. Also, Python can be easily integrated with other programming languages. The applications built using Python are easily scalable and future-oriented.
  • R Programming: R is a language that is used primarily for data analysis. Knowing how to use R is important because it helps in cleaning complex data sets to ensure convenience and further analysis. It has an extensive library of tools for database manipulation and also makes Machine Learning easier.
  • SQL Database/Coding: SQL is necessary to communicate with the database so as to work with the data. SQL also integrates with the scripting languages and you can connect a client app to your database.
  • Apache Spark: The primary importance of Spark in the Big data industry is because of its in-memory data processing that makes it a high-speed data processing engine. It delivers a better-integrated framework which supports all ranges of Big data formats. Apache Spark allows programmers to write applications using Python, Clojure and Java as it is supported by over 80 high-level operators. 
  • Machine learning and AI: Machine learning techniques like neural networks, reinforcement learning etc. are techniques that data scientists mu7st be well versed in. If you want to stand out, you should know techniques such as supervised machine learning, decision trees etc. These are important in solving different data science problems that are primarily based on the predictions of major outcomes.
  • Data visualization: Data should be in a form that is easy to understand. As a data scientist, it is important for you to visualize data. This can be done using data visualization tools like ggplot, d3.js and Matplottlib. Data visualization gives organizations the opportunity to work with data directly in order to act on future prospects.
  • Understanding of structured data: Unstructured data is the content that isn’t a part of data tables, for example, videos, customer reviews, blog posts etc. Sorting these types of data is difficult. Working with unstructured data helps you to gain a broader perspective for decision making.

What do you think makes a good data scientist? Having the essential technical skills is very important, there are some traits that your employers look for during the hiring process.

  • Data intuition: This is the quality that helps one to identify patterns within sets of structured and unstructured data. The role of data scientists is constantly evolving and they must now understand the needs of customers and their organization. For your interview, you may be asked to create quick data visualization. 
  • Passion: Data Science is not just a science, it is also an art. In a world filled with mediocrity, one must come up with the best solution to a complex problem. A data scientist has to keep pushing to find the solution that will optimize business value. Without passion for the field of study, a data scientist will not be able to find that optimal solution.
  • Curiosity: Data Science is a field in which innovations are being done every year. This is because the best data scientists are always looking for alternative ways to solve problems. This includes searching for new and optimal ways to acquire and merge data, pre-process features, or develop models and improve their run time using a combination of software and hardware optimizations. For this, curiosity is a key factor in determining the extent of innovation.
  • Strong will: Sometimes, problems might be so complex that you might quit trying to find solutions. To be a data scientist, you must possess the ability to get back up every time you fall. Patience and the will to come up with optimal solutions are necessary so as to show your professionalism.
  • Ability to make data-driven decisions: A data scientist cannot conclude, judge or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools to use and how to visualize and communicate it in the most effective way. 

The combination of technical skills and these characteristics is what differentiates a great data scientist from a mediocre one.

As the field of data science is becoming more popular, plenty of job opportunities have opened up in this field. Some of the leading companies offering employment to Data Scientists include Capital Group, Alteryx, Inc., BlackBerry, Edwards Lifesciences, Drybar, iHerb.com, KPMG, Synaptics, Prismatik, etc. The benefits of being a data scientist are:

  • Freedom to work: Any data scientist’s answer to what they like best about their field will be freedom. You won’t be bound to work for a particular industry. You can explore the tech world by working on projects that interest you and you can change lives.
  • Handsome pay: Most of the graduates, especially those in Irvine, have been earning close to $120,000. Thus job holds the highest position among the best jobs, making it such a viable option.
  • Chance to work with big brands: All big companies are on the lookout for data scientists for marketing and selling their products. If you want a chance to work with the big players like Amazon, Facebook, Google, Apple etc., this is the right career for you.
  • Secure career: While people argue that technology is temporary and will wear out, the same doesn’t hold true for data science. It will breed further and the need for scientists will keep growing. Your skills and attitude will keep you for a long time on this field.
  • Building your own business becomes easy: Once you have understood the working of various industries, your relationship with the clients is great and you learn how to solve real problems. Since you’ll have field experience, data science will aid you to set up your own dream business.

Data Scientist Skills & Qualifications

Apart from technical skills, a data scientist has to possess skills that can help in finding solutions for real-industry problems.

  • Statistical thinking: Hypothesis testing, Probability, Descriptive and Inferential Statistics are building blocks for data science. A data scientist should be able to interpret statistical output in business friendly context. Understanding the right algorithms and programming can make you a major player in this field.
  • Good communication skills: You should be able to present analytical solutions in a clear and concise manner. To translate statistical output into recommendations for action and to be able to discern team perception requires you to have excellent verbal and written communication skills.
  • Problem solving ability: Companies look for those who can think out of the box and who are comfortable in creatively solving business related problems. This is the most important skill for a data scientist.
  • Teamwork: Although data scientists are treated individually for their abilities, it is crucial that they work together in a team because it is a sign of all skills aligning on the same page. More creative ideas come up, leading to improved solutions.

Being an all-rounder is an empowering feeling. Here are the five best ways to brush up your skills to become a data scientist:

  • Participate in competitions: When you need to get back into coding, the most difficult thing to do is to think about what problem to solve. There exist a multitude of platforms built for coders. Getting into a coding competition, or a ‘hackathon’, is one of the best ways to brush up your skills after a long period of not coding. You might also learn new methods to code.
  • Be open to learning more: Take online courses from Coursera or any other platform that focus on developing your hold over a particular language or skill. There are many interactive incentive-based platforms too.
  • Freelance and contribute to open source projects: Free and open source software is at the root of many programming languages and of some of the most ambitious projects in the world. You can code in the language of your choice and thus, brush up your skills.
  • Go through professional code samples: Clean coding is an important skill to have, as it greatly increases readability and debug capabilities. One of the best ways to create clean code is to take a look at the code samples which are created by some of the best coders around.
  • Take help from a mentor: Find a mentor who works in the industry or has experience in data science. This includes programmers and developers, data scientists, statisticians, engineers and more. Ask them questions and learn about their experiences.

There is a data boom all around the world today. From travel to education to healthcare- data analysis has become extremely crucial. The ‘city of innovation’, Irvine, is flourishing in the travel and healthcare industries. The companies look for different data scientists for different purposes. In Irvine, all corporations including small-sized companies to bir corporations, everyone is looking for data scientists for providing useful insights and helping in making crucial marketing decisions from data to optimize their business. The companies that are currently employing data scientists include Capital Group, Alteryx, Inc., BlackBerry, Edwards Lifesciences, Drybar, iHerb.com, KPMG, Synaptics, Prismatik, Amazon Web Services, Ten-X, UST Global, Karma Automotive, BookingPal, Allergan, etc.

For any goal that one wants to achieve, practicing and working hard are the most important. Find what motivates you to practice what you’ve learned and learn more. This includes personal projects, competitions, online courses, reading research papers or meeting up with experts. To help you decide what kind of data sets you can work with, here are three levels:

Beginner level: This level has data sets that are easy to work with and don’t require complex techniques. They can be solved using basic algorithms. 

  • Heights and Weights Data Set: This is a fairly straightforward problem and is ideal for people who are starting data science. It is a regression problem where there are 25,000 rows and 3 columns (index, height and weight).PROBLEM: Predict the height or weight of a person. 
  • Time Series Analysis Data Set: It is one of the most commonly used techniques in data science. It is used in weather forecasting, predicting sales etc.PROBLEM: Predict the traffic on a new mode of transport.
  • Loan Prediction Data Set: The banking domain has the greatest use of data science methodologies as compared to any other industry. The Loan Prediction data set provides the learner with a taste of working with the concept involved in banking and insurance - the challenges faced, the strategies implemented, the variables that influence the outcomes etc. The Loan prediction data set consists of 13 columns and 615 rows and is a classification problem data set.PROBLEM: Predict if a given loan will be approved by the bank or not.

Intermediate Level: This level has more challenging data sets. It consists of mid and large data sets which require serious pattern recognition skills.

  • Human Activity Recognition Data Set: The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones. These were embedded with inertial sensors. The Human Activity Recognition Data Set consists of 561 columns and 10,299 rows.PROBLEM: Predict the human activity.
  • Trip History Data Set: This comes from a bike sharing service in the US. It requires you to exercise your pro data munging skills. The data is provided quarter wise from 2010 and has 7 columns.
    PROBLEM: Predict the class of the user.
  • Million Songs Data Set: Data science is applicable in the entertainment industry too. This data set is a regression task. It has 515,345 observations and 90 variables.
    PROBLEM: Predict the release year of a song. 

Advanced Level: After getting a hold on the basics, this level will be perfect for people who understand neural networks, deep learning, recommended systems etc. It allows you to get creative.

  • Vox Celebrity Data Set: Audio processing is a challenging problem. This data set is for speaker identification and contains words spoken by celebrities that have been extracted from YouTube. It contains 100,000 words spoken by 1,251 celebrities.
    PROBLEM: Figure out which celebrity this voice belongs to.
  • Recommendation Engine Data Set: You will be given the data of the programmers and questions they have solved, along with the time they took to solve a particular question. The model you build will allow judges to decide the next level of questions to be recommended to the user.
    PROBLEM: Predict the time taken to solve a problem using the current status of the user.

How to Become a Data Scientist in Irvine, California

Ranked as the hottest job on offer in the coming years and coupled with handsome pay-checks, data science has become a top career choice. What will give you the competitive edge? Find out through these steps:

  • Develop skills in Algebra, Statistics and Machine Learning. The perfect balance will make you a top-notch data scientist.
  • Learn to embrace big data. Data scientists deal with large amounts of segregated and unsegregated data which requires big data software and the right skillset. 
  • Learn to code. This is the first and foremost requirement for a data scientist because you cannot deal with data if you don’t know the language in which the data communicates. A great data scientist is always a great coder.
  • Master data munging and visualization. It is important for a data scientist to know how to convert data into a form that is easy to study, analyse and visualize. 
  • Stay updated within the data scientist community. You should remain in sync with what is happening in the world of data science and the types of job openings offered in the field.

Step 1: Preparation

You can start preparation even before starting your college. Learn Python, Java, and R and rebuild your knowledge in applied math and statistics.

Step 2: Enrol for suitable courses

Try to get enrolled for courses such as data science, mathematics, information technology, computer science, etc. Continue to learn programming languages, database architecture and SQL/MySQL. 

Step 3: Get an entry-level job

Companies are often eager to fill entry-level data science jobs. Look for positions such as Junior Data Analyst or Junior Data Scientist. 

Step 4: Get a Master’s Degree and/or a Ph.D.

Data science is a field where career opportunities are higher for those with advanced degrees. So, get enrolled for master’s or Ph.D.

Step 5: Never Stop Learning

Staying relevant is crucial to the evolving field of data science. Continue to network and learn through boot camps and conferences.

Most of the data scientists today either hold a Master’s degree or a PhD. While possessing the required skills is the most important requirement to be a data scientist, the degree has statistically proven to be important in landing a job. The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. Having a degree has the following advantages:

  • Network: Building professional networks within your college communities kickstarts your career as you can showcase your skills and win competitions.
  • Internships: While pursuing your degree, you can easily bag internships in the field of data science which will not just add to your experience, but also help you develop your skills.
  • Jobs: Most of the jobs will demand an academic qualification in your field which is why a degree plays an important role.
  • Discipline: Studying and following a schedule allows you to develop the discipline which you will carry forward when you become a data scientist.

Candidates having a Master’s/PhD degree may have advantages because they may be able to do some or all of these below:

  • Do research involving programming and large datasets
  • Develop statistical and data intuition 
  • Answer hard questions
  • Critically think about hard problems

The University of California located in Irvine offers a Data Science Certificate, Business Intelligence & Data Warehousing Certificate, Predictive Analytics Certificate Program, and Master of Science in Business Analytics. However, a graduate with field experience need not pursue a Master’s if he is already working. Real experience will always outweigh the Master’s degree.

The demand for data scientists is growing in every industry. To become a data scientist, you require the right tools and skillset to produce better results. Data science deals with humongous amounts of data that these scientists need to work on. This data can be segregated or unsegregated, depending on its type. In a situation like this, the basic requirement would be to understand the language in which data communicates. These languages include Python, R, Java, etc. 

  • This knowledge allows statisticians to perform the most complex analyses without much difficulty.
  • These languages have multiple libraries to perform multiple roles.
  • They help retrieve data from organized data sources.
  • They can be used in conjunction with big data platforms.

Data Scientist Jobs in Irvine, California

Broadly, the learning path to become a data scientist can be divided into the following steps:

  1. Getting Started: The biggest step is the beginning of your data science journey. This stage is all about understanding what data science is and what a data scientist role entails. This is where you should pick up the programming language and tool of your choice. 
  2. Maths and Statistics: These are the core concepts a data scientist must know. Where learning a tool will help you perform quick calculations and generate results, you can’t truly become a data scientist until you have a solid grasp on statistical methods (probability, descriptive and inferential stats) and mathematical fields (linear algebra).
  3. Learning Machine Learning concepts: You should start learning the basics of machine learning. But this isn’t just limited to theoretical concepts. You should apply them too. But ML isn’t limited to just the algorithms; you need to know nifty tricks to improve your models.
  4. Introduction to Deep Learning: Now you know these machine learning concepts, what comes next? Deep learning of course! It’s becoming an essential part of any data scientist’s CV these days. Follow that up with a deep dive into advanced neural network frameworks, namely recurrent neural networks and convolutional neural networks. 
  5. Natural Language Processing (NLP): No data scientist learning path is fully complete without first going over NLP. You should focus on learning the basics at the very least, including text pre-processing and text classification.
  • Find the role you want. Data scientists deal with different problems in different companies. You should decide whether you want to be into analytics, algorithms or inference.
  • Study Statistics, Machine Learning, SQL and Python. It is important that you learn or brush up your skills when it comes to these as they lay the foundation of data science and all interview questions are based on these topics.
  • Study about the company culture, people and business models. If you apply for a job in a company, you should know the workings of all the models, the kind of problems you’ll be finding solutions for and the overall culture around.
  • Be Persistent. Getting a job means dealing with rejection. You should know how to be strong and apply smartly using your connections for jobs.
  • Negotiate and leverage. Keep a track of the current salary for data scientists and the particular role that you want to take up. Express your expectations politely and learn how to negotiate and create leverage.
  • Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data niches. They generally have a foundation in computer science, modelling, statistics, analytics, and math -coupled with a strong business sense.
  • A Data Scientist identifies the data the business should be collecting, develops methods of instrumenting the system in order to extract this information and work with other departments to devise the processes that transform raw data into actionable ones.
  • They are responsible for determining the correct data sets and variables and collecting large sets of structured and unstructured data from disparate sources.
  • They clean and validate the data to ensure accuracy, completeness and uniformity.
  • It’s this merging of esoteric intelligence and practical knowledge that makes the data scientist so valuable to a company.

Data scientists are earning much more as compared to other jobs, especially in the US. Irvine is known for all its start-ups. The start-ups pay the highest, other companies pay lesser and public institutions pay the least. According to the roles:

  • Data Scientist- $120,179 p.a
  • Data Analyst- $71, 274 p.a
  • Senior Data Scientist- $140,000 p.a

The ability to manipulate and understand data is extremely critical in innovation. As a result, we are witnessing data science as a field that focuses on the processes and systems that enable us to extract knowledge and transform them into action. But as a discipline, it is in an infancy stage. All tech companies are driven towards data and hence, this is becoming a career with a lot of diversity. The career path in detail is as follows:

  1. Data scientistsHe/she will be able to create predictive models, discuss the findings after understanding the business problems. The role entails solving a data science problem after applying their theoretical knowledge of statistics and algorithms. 
  2. Data engineersThey use their software engineering experience to handle large amounts of data. They usually focus on coding, cleaning up data sets, and implementing requests that come from data scientists.
  3. Data architectsThey focus on structuring the technology that manages data models.
  4. Data administrators They focus on managing data storage solutions and fall in the category of data engineers.
  5. Data analystsData analysts look through the data and provide reports and visualizations to explain what insights are hidden in the data. 

The top professional associations and groups in Irvine for Data Scientists include:

  • OC Data Science
  • Fullerton Data Science and Artificial Community
  • Data Driven Insights
  • UIUC-MCS Data Science
  • Cerritos Data Visulaization BI Meetup

Connections are the best option to network with data scientists in Irvine. This can be done through:

  • Conferences
  • LinkedIn groups
  • Boot camps
  • MeetUp and other social gatherings
  • Data Scientist
  • Business Analyst
  • Business Intelligence Developer
  • Data Engineer
  • Machine Learning Engineer
  • Data Analyst
  • Data Architect

They look for:

  • Basic programming languages like Python and R.
  • Statistics (Hypothesis, probability etc.)
  • Machine learning 
  • Data wrangling(cleaning up data)
  • Data visualization

Data Science with Python Irvine, California

Python 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. 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.

R Programming: R is one of the most frequently used programming tools for data science. It allows users to compute huge data sets, get statistical insights, create custom graphics and more. 

Python: Python is a very popular, dynamic and versatile data tool for analyzing, arranging and integrating data into complicated data sets and creating advanced algorithms. It is among the easiest programming languages and hence the most sought after platform by most data scientists.  

SQL: It is used for editing, customizing and arranging information in relational databases.

Java: Java is an extreamely compatible and comprehensive platform which runs on OOPS framework and hence is easy to customize.

The Python download requires about 25 MB of disk space; keep it on your machine, in case you need to re-install Python. When installed, Python requires about an additional 90 MB of disk space.

  • Click Python Download.

The following page will appear in your browser

  • Click the Download Python 3.7.0 button.

The file named python-3.7.0.exe should start downloading into your standard download folder. This file is about 30 MB so it might take a while to download fully if you are on a slow internet connection.

The file should appear as:

 Move this file to a more permanent location, so that you can install Python.

  • If you want to just continue the installation, you can terminate the tab browsing this webpage.
  • Start the Installing instructions directly below.
  • Double-click the icon labelling the file python-3.7.0.exe.

An Open File - Security Warning pop-up window will appear.

  • . Click Run.

A Python 3.7.0 (32-bit) Setup pop-up window will appear.

Ensure that the Install launcher for all users (recommended) and the Add Python 3.7 to PATH checkboxes at the bottom are checked.

If the Python Installer finds an earlier version of Python installed on your computer, the Install Now message may instead appear as Upgrade Now (and the checkboxes will not appear).

  • Highlight the Install Now (or Upgrade Now) message, and then click it.

A User Account Control pop-up window will appear, posing the question Do you want to allow the following program to make changes to this computer?

  • Click the Yes button

A new Python 3.7.0 (32-bit) Setup pop-up window will appear with a Setup Progress message and a progress bar.

During installation, it will show the various components it is installing and move the progress bar towards completion. Soon, a new Python 3.7.0 (32-bit) Setup pop-up window will appear with a Setup was successfully message.

  • Click the Close button.