Data Science with Python Training in San Diego, 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.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. 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 Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

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.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

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

Prerequisites

Prerequisites for the Data Science with Python training program

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

Who should attend this course?

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 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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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 & 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.

Frequently Asked Questions

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

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

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 un View More

Attended Data Science with Python Certification workshop in January 2020

Merralee Heiland

Merralee Heiland

Software Developer.

5/5

KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and conce View More

Attended PMP® Certification workshop in April 2020

Lauritz Behan

Lauritz Behan

Computer Network Architect.

5/5

Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suit View More

Attended PMP® Certification workshop in May 2020

Nathaniel Sherman

Nathaniel Sherman

Hardware Engineer.

5/5

The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the works View More

Attended PMP® Certification workshop in April 2020

Astrid Corduas

Astrid Corduas

Senior Web Administrator

5/5

The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development View More

Attended PMP® Certification workshop in April 2020

Anabel Bavaro

Anabel Bavaro

Senior Engineer

5/5

The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

Attended Certified ScrumMaster (CSM)® workshop in August 2020

Matteo Vanderlaan

Matteo Vanderlaan

System Architect

5/5

I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job. View More

Attended Certified ScrumMaster (CSM)® workshop in January 2020

Tilly Grigoletto

Tilly Grigoletto

Solutions Architect.

5/5

I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the sessi View More

Attended Agile and Scrum workshop in February 2020

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Data Science with Python

What is Data Science

The amount of data that is produced every day is just mind-boggling. By 2020, it's estimated that 1.7MB of data will be created every second for every person on earth. Data-driven decision making is increasing in demand. Data scientists help a company take those important marketing decisions based on data. San Diego, California is a hub for genetics and biotechnology. It is home to several leading companies such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and all these companies are looking for skilled data scientists to analyze their data. 

  • Python Coding: Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data. 
  • R Programming: Knowledge of R programming is usually an advantage for data scientists in order to make any data science problem easier to solve. It is broadly used for developing statistical software and data analysis.
  • Hadoop Platform: Knowledge of Hadoop isn’t exactly an essential, but very much preferred by the employers. It is needed when the amount of the data exceeds the existing memory of the system. Hadoop is also used while handling data exploration, filtration, sampling, and summarization.
  • SQL database and coding: SQL is a language that is specifically designed to help data scientists access, communicate as well as work on data. 
  • Machine Learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for a career in Data Science. The knowledge and concepts of Machine Learning and Artificial Intelligence that a potential data scientist must be familiar with include the following:
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.
  • Apache Spark: Apache Spark is a big data computation, not unlike Hadoop. The difference between the two is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.
  • Data Visualization: These tools aid a data scientist in the conversion of complex results obtained as a result of processes performed on a data set and convert them into a format that is easy to understand and comprehend. 
  • Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values. The main responsibility of a data scientist is to proofread, sort, analyze and visualize such data in a structured format.

Below are the behavioral traits employers look for in a Data Scientist in San Diego, CA -

  • Curiosity –  A curious nature is essential to keep your interest in this dynamic field alive. Since you will be dealing with massive amounts of data every single day, you should have an undying hunger for knowledge to keep you going. 
  • Clarity – The field is very vast on its own. Whether cleaning up data or writing code, you should know what you are doing and why you're doing it. 
  • Creativity – You will be required to solve problems and make decisions all day long. For that, you need to be able to figure out what's missing and what needs to be included in order to get results. 
  • Scepticism – Data scientists need scepticism to keep their creativity in check. Scepticism will keep you in the real world rather than letting you get carried away with creativity.

Expect to enjoy the following benefits on the job:

  1. High Pay: Owing to the high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist is $153,169 per year in San Diego, CA, which is 23% above the national average.
  2. Good bonuses: Although it is a part of their pay, data scientists can expect impressive bonuses. 
  3. Education: By the time you become a data scientist, you would either have a Masters or a PhD. You could well receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
  4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
  5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists. 

Data Scientist Skills and Qualifications

Below is a list of the business skills you need to become a data scientist: 

  1. Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyse what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
  2. Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists. Establishing healthy communication helps in working efficiently.
  3. Intellectual Curiosity – This term is the combination of curiosity and the drive to deliver results that produce value for the desired target group. A Data Scientist must possess this skill to excel further in his field.
  4. Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having a sound knowledge of the industry you work in gives you the right insight about what to overlook and what is required while working.

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

  • Boot camps: Just like the name suggests, these camps are a kind of crash course that help you get theoretical knowledge and hands-on experience in just a few days. 
  • MOOC courses: These are online courses and include some of the latest trends in the industry. They focus on the implementation skills in the form of assignments.
  • Certifications: Certifications brush up the existing knowledge, give you an additional skill and add up to your CV. Some famous data science certifications are:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: The more you work on projects, more refined your thinking and skills will be. You can also work on new projects that can refine your skills
  • Competitions: Lastly, competitions like Kaggle etc help improve your problem-solving skills. You need to satisfy all the requirements and find an optimum solution.

San Diego, California is home to many leading companies, such as Brain Corp, Patient Safe Solutions, Viasat, Illumina, Mitchell International, Inc. and many more. It is a fact that every company needs data.  The data science job offered by companies are determined by what kind of companies they are. Small companies use Google Analytics for their analysis - as they have fewer resources and fewer data to work with. Mid-size companies need someone to apply ML techniques on it to leverage it. Big companies already have teams of data scientists, so they would be needing a new data scientist with specialization. For eg: Visualization, ML expert etc.

We have compiled a list of data sets you can practice on, categorized according to their difficulty level for your ease:

  • Beginner Level
    • Iris Data Set: The Iris Data Set is widely accepted to be the most popular, versatile, resourceful and easy data set in the field of pattern recognition. 4 columns and 50 rows.Practice Problem: Use the parameters to predict the class of a flower.
    • Loan Prediction Data Set: The Loan Prediction data set provides the learner with a taste of working with the concepts that are applicable in the domain of banking and insurance. The Loan prediction data set is a classification problem data set. 13 columns and 615 rows.Practice Problem: Predicting if a certain load will be approved or not.
    • Bigmart Sales Data Set: Operations such as Product Bundling, offer customizations, inventory management etc are efficiently handled with the help of this set. The Big Mart Sales Data Set is used in Regression problems. 12 variables and 8523 rows.Practice Problem: Predicting the sales of the store.
  • Intermediate Level:
    • Black Friday Data Set: The Black Friday Data Set comprises of sales transactions that were captured from a retail store. The Black Friday data set is a regression problem.12 columns and 550,069 rows.
      Practice Problem: The problem is predicting the total purchase amount.
    • Human Activity Recognition Data Set: The Human Activity Data Set has a collection of 30 human subjects. 561 columns and 10,299 rows.
      Practice Problem: The problem is predicting the category of human activity.
    • Text Mining Data Set: This data set consists of aviation safety reports that describe the problems that were encountered on certain flights. The Text Mining Data Set is a high dimensional and multi-classification problem. 30,438 and 21,519 columns.
      Practice Problem: The problem is using the labels to classify the documents.
  • Advanced Level:
    • Urban Sound Classification: It is a data set that consists of 8,732 sound clippings of urban sounds that can be categorized in 10 classes. It includes concepts of audio processing in the real-world scenarios of classification.
      Practice Problem: The problem is identifying particular audio and classifying it to its class.
    • Identify the digits data set: This data set comprises of 7000 images, totalling 31MB, with dimensions of 28X28 each. The main aim is to study the picture.
      Practice Problem: The problem is identifying various elements present in the image.
    • Vox Celebrity Data Set: The Vox Celebrity Data Set is meant for large scale speaker identification. It is a collection of words spoken by celebrities and extracted from YouTube videos.
      Practice Problem: The problem is identifying the voice of the celebrity.

How to Become a Data Scientist in San Diego, California

elow are the steps to becoming a successful data scientist in the city of San Diego:

  1. Getting started: Choose a programming language that you’re comfortable with. We recommend Python or R languages. Python provides a more general approach to data science while R is mainly used for statistical analysis.
  2. Mathematics and statistics: The field of data science is all about dealing with the data, making patterns and relationships between them. For this, knowledge of basic algebra and statistics is required.
  3. Data visualization: You have to make it as simple as possible so that the other non-technical teams are able to grasp its contents as well. It is important to learn data visualization in order to communicate better with the end users.
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you.

Here are some key steps and skills that will help you become a successful data scientist:

  1. Degree/certificate: Be it an online or offline classroom course, it is important to start with a basic course that covers the fundamentals. Not only will you learn how to apply cutting-edge tools but also get a boost in your career growth.
  2. Unstructured data: Usually, the data is unstructured and doesn’t fit into a database. Your job is to understand and manipulate this unstructured data.
  3. Software and Frameworks: Due to the huge amount of unstructured data, it is essential that you are comfortable in using some of the most popular and useful software and frameworks to go along with an equally important programming language - preferably R.
  4. Machine learning and Deep Learning: Machine Learning deals with the real-world implications of the theoretical knowledge of the field. It is important to have a basic understanding of both ML and Deep Learning. 
  5. Data visualization: A data scientist’s job is to make sense of huge amount of data given for analysis and provide it to the business in the form of graphs and charts. Some of the tools used for this purpose include matplotlib, ggplot2 etc.

A degree is very important in the field of Data Science because of the following – 

  • Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances. This will help you land a better job in the future.
  • Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  • Internships – Internships are a great way to get acquainted with an office environment and understand your job better.
  • Recognized academic qualifications– A degree from a prestigious institution will not only look good but will also give you a head start in the race for the top jobs.

Below is a scorecard to help you in making this decision.If your total adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.

  • You have a strong STEM background: 0 point
  • You have a weak STEM background (i.e., biochemistry/biology/ economics or similar degree/diploma): 2 points
  • You are from a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that requires you to code on a regular basis: 3 points
  • You fare well at independent learning: 4 points
  • You do not understand when we tell you that this scorecard is a regression algorithm: 1 point

Here are the reasons why knowledge of programming language is a must:

  • Data sets: Data science involves working with large amounts of data sets. They are the lifeblood of data science. Understanding and making sense out of them is essential to your job.
  • Statistics: If a data scientist has knowledge about statistics but has no idea how to implement this knowledge, the knowledge of statistics becomes much less useful in his/her application of data science. Without programming language, you won’t be able to apply statistics in Data Science.
  • Framework: This enables a data scientist to build systems that an organization can make use of in order to create frameworks to automatically analyse experiments, visualize data as well as manage the data pipeline at a large organization so that the data can be accessed by the right person at the right time.

Data Scientist Jobs in San Diego, California

Here is the logical sequence of steps you should follow to get a job as a Data Scientist in San Diego, CA.

  • Getting started: Choose a programming language in which you are comfortable. We suggest Python or R language. Make sure to understand the roles and responsibilities of a data scientist.
  • Mathematics: Data science is all about making sense of raw data, finding patterns and relationships between them and finally representing them. Here are a couple of topics that need special attention:
    • Descriptive statistics
    • Probability
    • Linear algebra
    • Inferential statistics
  • Libraries: Data science process involves various tasks ranging from pre-processing the data given to plotting the structured data and finally to applying ML algorithms as well. For this, certain libraries and packages are used. These libraries are Ggplot2, Matplotlib, NumPy, Pandas, Scikit-learn and SciPy
  • Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. The libraries used for this task are Ggplot2 - R and Matplotlib - Python
  • Data pre-processing: Due to the unstructured form of data, it becomes necessary for data scientists to pre-process this data in order to make it analysis-ready. Pre-processing is done using feature engineering and variable selection. After pre-processing, our data would be in a structured form and ready to be injected into ML tool for analysis.
  • ML and Deep learning: For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. It is recommended you spend a few weeks on topics like neural networks as well.
  • Natural Language processing: Every data scientist should be skilled in NLP as it involves processing of text form of data and its classification as well. 
  • Polishing skills: Other than online competitions, you can keep on experimenting and exploring the field by creating your own projects as well.

If you are thinking to apply for a data science job in San Diego, CA, then follow the below steps to improve your chances:

  • Study: To prepare for an interview, cover all the important topics like-
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding of neural networks
  • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections.
  • Competitions: They not only brush up your skills but also give you exposure to the field in an interesting and creative way. 
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. Make sure that your LinkedIn profile is well-maintained and updated.
  • Interview: Learn from the questions that you were not able to answer in your last interview and study them for the next interview.

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. 

Major Data Scientist Roles & Responsibilities:

  • Fetching data that is relevant to the business.
  • Organize and analyze the data that is extracted from the piles of data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data. 

The average salary for a Data Scientist is $153,169 per year in San Diego, CA.

https://www.indeed.com/career/data-scientist/salaries/San-Diego--CA 

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

Business Intelligence Analyst: A Business Intelligence Analyst does the analysis of data in order to figure out how the business works and how it can be affected by market trends.

Data Mining Engineer: A Data Mining Engineer is an individual who has the job of examining the data for the needs of the business. He needs to create sophisticated algorithms that further aid in the analysis of data.

Data Architect: The role of Data Architect is to work with database administrators and analysts to secure easy access to company data.

Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data.

Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.

Below are the top professional organizations for data scientists in San Diego, CA– 

  • Data Science for Non-Profits
  • San Diego Data Science
  • SD Big Data & Advanced Analytics Meetup
  • Let's GO Data Science
  • San Diego Data Science #ODSC
  • San Diego Regional Data Library

San Diego, California is growing as one of the hubs of Data Science in USA. Some of the ways to network with fellow data scientists are:

  • Data science conferences held in San Diego
  • Online platform like LinkedIn, job portals and forums
  • Social gatherings like Meetup

There are so many career options for a data scientist in San Diego, California as of today-

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager

Here are the tools and software that a data scientist must be an expert in:

  • Education: Data scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  • Programming: You should be good in at least one programming language. Python is the most common as well as easiest of them all.
  • Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  • Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

Data Science with Python San Diego, California

The simplicity and readability of Python makes it popular among data scientists. It is a multi-paradigm programming language and comes with a broad and diverse range of resources that are available to the data scientist. Python is supported by its big, open-source community.  With many developers working on Python every day, it becomes very easy for a developer to get help in resolving his/her problems.

Below are the most commonly used programming languages in Data Science:

  • R: It may be a bit hard to learn but it has various advantages. It comes with big open-source community that provides R with high-quality open source packages. It also includes a lot of statistical functions that help in carrying out complex matrix operations smoothly
  • Python: Though it has fewer packages than R, python is still one of the most sought after languages in the data science field. Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purpose. It has a big open-source community as well.
  • SQL: SQL is a structured query language which works upon relational databases. It is very efficient when it comes to updating, manipulation, and querying of databases.
  • Java: Even though it has less number of libraries for data science purposes and Java's verbosity limiting its potential, it has many advantages as well. Due to already systems coded in Java at backend, it is easier to integrate java data science projects to it. 

  • Scala: Scala runs on JVM and has a complex syntax to it. Still, it is a preferred language in data science domain. As it runs on JVM, any Scala program can run on Java as well. It can be used for high-performance cluster computing when used along with Apache Spark.

These are the steps to install Python 3 on windows:

  • Download and setup: Go to the download page and setup 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 terminal.

  • See 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

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

  • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:

$ xcode-select --install

See if it installed correctly: brew doctor

  • Install python 3: To install the latest version of python, use:

brew install python

Check if it’s installed by typing: python --version

Note: Install virtualenv, as it will help you create isolated places to run different projects and may run even on different python versions.

Data Science with Python Certification Course in San Diego, CA

Its easy to see why this city has self- proclaimed itself as ?America?s Finest City?. Filled with world famous attractions, including monuments and architectural wonders, a bustling economy and locals who are as famous for their sunny dispositions as they are for their sunny beaches, San Diego is a city one can only love. Being one of California?s major cities, it is also the economic centre of the region and hosts several manufacturing, tourism, military, transport, technology, and trade companies including General Atomics, NASSCO, Qualcomm, Nokia, LG, and Websense Inc. It also houses the famous University of California that is famous for its biotechnology related research programs. With world class infrastructure, the university attracts students from all over the globe. KnowledgeHut offers several courses that help you start off your career in San Diego 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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