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Data Science Course in San Francisco

Looking to begin a lucrative career in the world of Data Science – the Data Science Using Python and R Programming is the place to start. This program offers the best in industry data science program which is a potent culmination of best in class instructors, groundbreaking course material and an AI-powered LMS platform – AISPRY.

Training duration: 130 hours

Data Science Training in San Francisco

The Data Science Using Python and R is a truly transformative program which takes the students from novices to job-ready candidates in this competitive US market. The defining feature of this program that it aims to blend scientifically rigorous and multidisciplinary knowledge into easily understandable concepts. This course aims to be a primer and will attract learners from diverse domains such as Business Professionals, Programmers, Researchers, Academia, Industry practitioners among others. Students will begin by learning the basics of statistics and slowly progress to learning more complex algorithms in the data science toolkit such as regression, tree-based methods, supervised and unsupervised algorithms.

Course Details

Data Science Training Outcomes in San Francisco

Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply Data-driven, Machine Learning approaches for business decisions
Build models for day-to-day applicability
Perform Forecasting to take proactive business decisions
Use Data Concepts to represent data for easy understanding

Modules for Data Science Course in San Francisco

CRISP-DM stands for Cross-industry standard process for data mining and is the bedrock framework of any data science project. This module will explain in detail the cyclical process and all the phases of this methodology. The phases are:

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

Exploratory Data Analysis is usually where the data scientists tend to spend most of their time in the project. This often involves understanding the dataset, summarizing and describing the data at hand.

This is the phase where the statistical techniques are applied to draw inferences from the data. Typically, there are a lot of statistical techniques that are leveraged during this module and applied to the data.

This module introduces the various visual plots such as the bar plot, histogram, scatter plot, box and whiskers plot, etc., and how they can be leveraged to identify patterns and correlations in the data.

This module introduces the basic concepts of probability distributions and how that knowledge is extensively applied in data science projects. The various types of distributions like Gaussian (Normal), Bernoulli, Poisson, Binomial, Multinomial, etc.

This module introduces the concepts of Hypothesis testing and defines in detail about Null and Alternative Hypothesis and the scenarios where each of them could be proved. It discusses in detail the concepts like parametric tests like 1 sample t-test, 2 sample t-test, ANOVA and nonparametric tests like Chi-Square tests.

This module marks the beginning of predictive analytics and lays the foundation for the rest of the course modules of Machine Learning. The module introduces one of the oldest and simplest supervised learning techniques - the linear regression (ordinary least squares).

As can be easily understood, this is an extension of the OLS Linear Regression, by expanding the input space into multiple linear outputs. We discuss this using one of the most commonly used datasets and learn how it can be implemented in Python and R programming languages.

This module discusses the other advanced and useful types of regression models - Lasso and Ridge regression. We discuss the pros and cons of each of these models with practical examples using Python and R.

Also called the Logit model, this algorithm predicts the probability of the event falling into a certain category - Pass/Fail, Fraud/Not Fraud, Yes/No etc. We also get introduced to the concept of maximum likelihood estimation techniques which form the regression coefficients.

This is an extension of the Logistic Regression - the only difference being the output class could have more than 2 classes.

Count data is a data type in which the observations can only consist of non-negative numbers (0,1,2, 3 …). This module introduces regression models for such data such as Poisson Regression and Negative Binomial Regression and their application in survival analytics.

This module provides a general overview of unsupervised learning before launching into one of the most famous of its techniques - Clustering. Multiple algorithms such as connectivity based (Hierarchical), centroid based (k-Means), Distribution based, etc are discussed and implemented along with real- life scenarios.

This module discusses another great technique called the Principal Component Analysis. It is one of the dimensionality reduction algorithms which answers the question - how do we reduce the feature space without losing a lot of information.

This is a rules based machine learning algorithm where the algorithm learns interesting patterns in data and attempts to answer the question - what goes with what. Different algorithms such as Apriori, FP-growth are discussed and implemented.

This module builds on the concepts of association rule learning and implements a recommendation engine. A great example could be the recommendation engine as seen in Amazon or any other e-commerce platform that suggests items based on your current or previous selections.

This module builds on the fundamentals of Graph Theory and delves into the architecture and implementation of any network with emphasis on Social Networks. The module extensively covers the modelling and visualization of networks and some practical implementations.

This module introduces machine learning algorithms by discussing one of the most popular algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression.

This module introduces the decision tree algorithm which is a popular non-linear tree based algorithm. Furthermore, the discussion then introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm.

This module builds on the Bagging algorithm introduced earlier and also adds another ensemble technique called Boosting. The theory behind both the ensemble techniques is discussed in detail.

This module introduces Gradient Descent and explains how it is combined with boosting to achieve gradient boosting. Then we discuss the advanced implementations of gradient descent such as AdaBoost and Extreme Gradient Boosting.

All the prior modules have been dealing with structured data (data that can be stored as rows and columns and that follow relational requirements). This module introduces the unstructured data in the form of text data and provides some tools and techniques to analyze it.

This module discusses another important algorithm - Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy.

This module discusses the perceptron, which is the fundamental concept of an artificial neural network. The basic version of an ANN called the multi layer perceptron (MLP) is also discussed.

This module discusses the various building blocks of neural networks - perceptron, backpropagation, activation functions, dropout, dense vs sparse layers ,etc.

The difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. The architecture is explored and some guidelines are laid out.

This module discusses another Black Box technique - Support Vector Machines.

This module builds on the concepts from Module 12 and discusses how survival analysis is performed in practical and real-life scenarios.

This module introduces the concept of a time series. All the previous modules (except text data) were longitudinal or cross-sectional datasets. There is no temporal component in the data (or it is ignored for the purposes of algorithm). This module introduces techniques to deal with time-series data such as AR, ARMA and ARIMA models.

This module builds on the concepts previously discussed and delves into some of the data-driven algorithms available to address the time-series problems such as MA, EMA and Econometric Models.

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With hundreds of companies hiring for the role of Data Scientist, 12 million new jobs will be created in the field of Data Science by the year 2026.

(Source: https://www.forbes.com)

Block Your Time

Data Science Course in San Francisco

130 hours

Classroom Sessions

Data Science Course in San Francisco

140 hours

Assignments &

Data Science Course in San Francisco

140 hours

Live Projects

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

Data Science

Data Science Course Duration in San Francisco

Total Duration

4 Months

Data Science Course Pre-requisites in San Francisco


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

Tools Covered

Data Science Course with Python in San Francisco Data Science Course with R in San Francisco Data Science Course with R Studio in San Francisco

Being the home of the Silicon Valley and the birthplace of many a technological innovation this city is also one of the hottest markets for data science jobs. The San Francisco Chronicle is reporting that Data Scientists are winning the popularity contests which augurs well for the many silicon valley employers who are lamenting the lack of properly trained resources. It is no secret that the Silicon Valley is the hotbed if venture capital investment, especially when it comes to emerging technologies like artificial intelligence as evidenced by this article where there were investments to the tune of 100s of millions of dollars.

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Data Science Course Module in San Francisco

Data Science Panel of Coaches in San Francisco

Data Scientist Trainer - Bharani

Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 14+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv Alshaya, Synchrony Financials, Deloitte
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
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Data Scientist Trainer - Sharath

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 14+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
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Data Scientist Trainer - Nithin

Nitin Mishra

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
  • Over 20+ years of industry experience in data science and business intelligence
  • Trained professionals from Fortune 500 companies and students at prestigious colleges
  • Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL
  • Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more
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Data Science Certification Course in San Francisco


Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.

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FAQs for Data Science Course in San Francisco

The data science profession has given rise to a multitude of sub-domains although most of the responsibilities overlap there are subtle and pertinent differences in each of the roles. See below for a short description of what each of the roles represent. Please be wary that depending on the organizational structure and the industry, the roles may have different meaning but this should serve as a basic guideline.


A Data Analyst is tasked with Data Cleansing, Exploratory Data Analysis and Data Visualization, among other functions. These responsibilities pertain more to the use and analysis of historical data for understanding the current state. So simply put, a Data Analyst can answer the question ‘what happened?’


A Data Scientist on the other hand will go beyond a traditional analyst and build models and algorithms to solve business problems using statistical tools such as Python, R, Spark, Cloud technologies, Tableau etc. The data scientist has an understanding of ‘what happened’ but will typically go a bit further to answer ‘how we can prevent/predict that from happening?’


A Data Engineer is the messenger that carries or moves data around. He is responsible for the data ingestion process, building data pipelines to make it flow seamlessly across source, target systems and also responsible for building the CI/CD (continuous integration, continuous development) pipelines.


A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organization to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.

There is a huge disparity in how these terms are used, sometimes DS, DA and BA are used interchangeably. Although, the gap is narrowing now, BA is strictly dealing with advanced analytics but DS is more about bringing predictive power using machine learning techniques. One thing is clear, Data Modelling typically means designing the scheman etc. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organization.

The US market is currently going through an unprecedented economy and the job growth has also been the best in recent times. Multiple reputed sources are documenting the acute shortage of data science professionals. Our program aims to address this by preparing the candidates not only by providing theoretical concepts, but helping them learn by doing. You will also greatly benefit from doing a Live project through Innodatatics, a leading Data Analytics company which will prepare you in dealing with implementing a data science project end-to-end.

It has been well documented that there is a startling shortage of data science professionals worldwide and for the US market in particular. Now the onus is on you, the candidate and if you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.


To help you achieve that 360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. You will be involved in executing a project end to end and this will help you with gaining the job training to help you in this career path.

There are numerous jobs available for data science professionals. Once you finish the training, assignments and the live projects successfully, we will circulate your resume to the organizations with whom we have formal agreements on job placements. We also conduct regular webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.

After every classroom session, you will receive assignments through the online Learning Management System. Our LMS is a state-of-the-art system which facilitates learning at your convenience. We do impose a strict condition – you will need need to complete the assignments in order to obtain your data scientist certificate.

Since this course is a blended program, you will be exposed to a total of 80 hours of instructor-led live training. On top of that you will also be given assignments which could have a total duration running into 60-80 hours. In addition to this, you will be working on a live project for a month. All of our assignments are carried out online and the datasets, code, recorded videos are all accessed via our LMS.

We understand that despite our best efforts, sometimes life happens. In such scenarios you can access all of the course videos in the LMS.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.

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