support@edupristine.com1800 200 5835Request a Call
Call Me

Certificate Program in Data Science

Data Science Course

Course Objective

Edupristine has launched a Data Science program with coursework from DeVry University’s Keller Graduate School of Management embedded in the program. The data science course is aimed at delivering the concepts, tools and practical experience needed throughout the entire data science pipeline. Learn how to ask the right questions, make appropriate inference and achieve desirable results. This data science program will enable you to apply the concepts learnt by building a data product using real-world data.

Data Science Course

At the end of the course, you will be awarded with a certificate

  • Global Certification - For successfully completing EduPristne Data Science program embedded with DeVry University's Keller Graduate School of Management(U.S.)
  • Certificate for Excellence - This certificate from EduPristine & D&B (Dun & Bradstreet) will be given to those who have demonstrated excellence throughout the course and procured 60% and above qualifying marks.

Why Data Science

Growing Need:

Presently, there are only 10,000 - 15,000 analytics and data experts in India and there will be a shortage of 2 lakh data scientists in India over the next few years.

Adoption of Big Data Analytics is Growing

New technologies are now making it easier to perform increasingly sophisticated data analytics on a very large and diverse data sets. Per a report by The Data Warehousing Institute (TDWI), more than a third of the respondents are currently using some form of advanced analytics on Big Data, for Business Intelligence, Predictive Analytics and Data Mining tasks.

Analytics - A Key Factor in Decision Making:

Analytics is a key competitive resource for many companies. Most of the businesses out there feel that Analytics is required for better decision-making capabilities and in creating superior key strategic initiatives.

The Rise of Unstructured and Semi- Structured Data Analytics:

The ‘Peer Research – Big Data Analytics’ survey clearly reports that there is a huge growth when it comes to unstructured and semi-structured data analytics. 84% of the respondents mentioned that the organization they work for are currently processing and analysing unstructured data sources, including web blogs, social media, e-mail, photos, and video. The remaining respondents have indicated that steps are being taken to implement them in the next 12 to 18 months.




Pratical Implementation

An Overview of Business Intelligence 1. An Overview of Business Intelligence, Analytics, and Decision Support

2.Foundations and Technologies for Decision Making
Given a business intelligence problem, analyze its major components and how they relate to each other.

Given a business case requiring a forecast of future sales, profits, or expenses, develop a worksheet that can facilitate decision making.
1.Model-Based Decision Making: Optimization and Multi-Criteria Systems

2. Modeling and Analysis: Heuristic Search Methods and Simulation
Internet Analytics and Social Media Analysis
1. Business Reporting, Visual Analytics, and Business Performance Management

2. Knowledge Management and Collaborative Systems
Data Warehousing and Dimensional Modeling Given a business scenario requiring a data warehouse, develop an implementation plan for the organization, and explain the relative costs and benefits that might result.
1. Data Mining for Business Intelligence

2. Techniques for Predictive Modeling
Implementing Business Intelligence and Emerging Trends



Pratical Implementation

Introduction to Big Data Tools and Technologies 1.Introduction to Hadoop/ Spark

2. Good Data Scientist tool kit

3. How modern Big Data technologies & tools provides answers to below problems:
  • Velocity is High - Real Time Analytics
  • Any non-functional parameters like cost, Reliability, fault tolerance
Getting started with fundametals of programming: Python for data processing & unix for CLI Commands
Introduction to Unix & PYTHON 1. Getting started with fundametals of programming

2. Python for data processing

3. Unix for CLI Commands - Getting familiar with Unix and CLI is first priority

4. Map Reduce concept and understanding

5. SQL for Hive
Getting started with fundametals of programming: Python for data processing & unix for CLI Commands
Introduction to HDFS & Map Reduce 1.Distributed Storage HDFS

2. Structured Data Ingestion:Sqoop
Introduction to big data storage, structured data ingestion: sqoop & touching base on parellel programming on scalable machines: map reduce with a hands on case study on the same
Map-Reduce and its assignment 1. Parallel programming on scalable Machines: Map Reduce

2. Mastering Key Value Pairs:Case Study
SPARK + Python & Case Study (LOG ANALYSIS) 1. Lighting Fast In Memory Cluster Computing:Spark

2. Batch Processing Historical data: Log Analysis; Ecommerce Industry
Getting to understand the log analysis, involving SPARK and Python with the help of a business case study to get a hands on experience
HIVE 1. Data warehousing, Management and querying on hadoop:Hive

2. Web Interface for analyzing data: Hadoop User Experience (HUE)
Getting started with data warehousing, management and querying on hadoop: HIVE & web interface for analyzing data: Hadoop User Experience (HUE)
PIG 1. Data Flow ETL Scripting Language : Pig Building the fundamentals for data warehousing, management and querying on hadoop: HIVE & web interface for analyzing data: Hadoop User Experience (HUE)
Oozie 1.Work Flow Management Tool: Oozie Introduction of the work flow management tool with hands on examples
Project I [Retail DOMAIN] Using all above tools 1. MIS Reporing and ELT on Hadoop: Retail Domain Given a retail business scenario, this provides a run-through of the MIS reporting and ELT on Hadoop
HBASE/ MongoDb 1.Random Read and Write Access, OLAP, NoSQL Database: Hbase Introduction to the fundamentals of random read and write Access, OLAP, NoSQL database: Hbase
1. Customer 360 & Genome: Banking sector Given a banking sector scenario, this provides a run-through on Customer 360 & Genome
Project III Twitter Sentiment Analytics 1.Using Flume, Kafaka , Spark Streaming and Batch Processing Using Hive & Impala A run-through on structured data ingestions, semi structured processing
Cloudera CCA-175 Certification Guidance (Online) A session on the exam preparation, pattern and the important topics to be discussed



Pratical Implementation

Data Mining: Overview 1.Data Analysis and Business Analytics with R (Primary)
  • Introduction to Data Mining
  • Processing the Information and Getting to Know Your Data
2.Supplemental Text
  • What Is Data Mining and Why Do It?
  • Data Mining Applications in Marketing and Customer Relationship Management
Given a business case in data mining, inspect the steps necessary to design and implement an appropriate solution.
Data Mining Process and Exploratory Data Analysis 1.Data Analysis and Business Analytics with R (Primary)
  • Standard Linear Regression
  • Local Polynomial Regression: A Nonparametric Regression Approach
2.Supplemental Text
  • The Data Mining Process
  • Statistics 101: What You Should Know About Data
Given a business case in data mining, examine the relevant methodologies that might be applicable to data mining projects.
Profiling and Predictive Modeling and Transforming Data 1.Data Analysis and Business Analytics with R (Primary)
  • Importance of Parsimony in Statistical Modeling
2.Supplemental Text
  • Descriptions and Predictions: Profiling and Predictive Modeling
Given a business case in directed data mining, evaluate methodologies and the necessary components appropriate to the development and implementation of direct data mining.
Decision Trees 1.Data Analysis and Business Analytics with R (Primary)
  • Decision Trees
2.Supplemental Text
  • Decision Trees
Given a business scenario together with a decision tree analysis, interpret the results and describe its impact on the business process that generated it.
Customer Survival Analysis 1.Data Analysis and Business Analytics with R (Primary)
  • Classification Using a Nearest Neighbor Analysis
  • The Na¨ive Bayesian Analysis: A Model for Predicting
  • A Categorical Response from Mostly Categorical
  • Predictor Variables
2.Supplemental Text
  • Knowing When to Worry:Using Survival Analysis to Understand Customer
Given a business scenario describing customer attrition data, analyze and interpret the data using survival analysis.
Market Basket Analysis and Association Rules 1.Data Analysis and Business Analytics with R (Primary)
  • Market Basket Analysis: Association Rules and Lift
2.Supplemental Text
  • Market Basket Analysis and Association Rules
Given a business scenario describing customer purchasing habits, analyze and interpret the data using market basket analysis and association rules.
Text Analytics 1.Data Analysis and Business Analytics with R (Primary)
  • Text as Data: Text Mining and Sentiment Analysis
2.Supplemental Text
  • Too Much of a Good Thing? Techniques for Reducing the Number of Variables, pp. 753–767 (Principal Components)
  • Listen Carefully to What Your Customers Say: Text Mining
Given a business scenario appropriate for a text mining approach, develop a plan for analysis and implementation of a solution using one or more text mining techniques.
Final Project



Pratical Implementation

Linear Regression 1.Correlation and Regression.

2. Multivariate Linear Regression Theory.

3. Bivariate Analysis.

4. ANOVA (Analysis of Variance.)

5. Identify and Quantify the factors responsible for loss amount for an Auto Insurance Company.
Given a multivariate linear regression case study, understanding the correlation and regression, ANOVA particularly covering the insurance domain.
Logistic Regression 1.Identifying problems in fitting linear regression on data having "Binary Response" variable

2. Generalized Linear Modeling (GLMs)

3. Logistic Regression Theory/Case
  • Fitting the regression using SAS language
  • Lift/Gains chart and Gini coefficient
  • K-S stat
4. Identify bank customers who will most likely default in making the payment on balance due.
Given a multivariate logistic regression case study, identifying problems in fitting linear regression on data having "Binary Response" variable Generalized Linear Modeling (GLMs) particularly covering the banking domain
Time Series Modeling - ARIMA 1. Models of time series

2. The Box-Jenkins model building process
  • Identify the ARIMA model.
  • Forecasting future sales based on historical data for an automobile company.
3. Identify bank customers who will most likely default in making the payment on balance due.
Given various case studies, understand the the Box-Jenkins model building process, orecasting future sales based on historical data for an automobile company focussing on the automobile industry
Market Mix Modeling 1. Optimization of the promotion expense using Market mix modeling Given a case scenerio, work on the affinity analysis to understand purchase behavior, understanding apriori algorithm, & Analysis of output results to plan store layout, promotions and recommendations
Email Marketing Optimization 1. Developing a system that ensures that the correct campaign reaches the relevant customers with a suitable frequency to further enhance the level of engagement across all email campaigns.
R Integration with Hadoop 1. Real Time Analytics, Unstructured Data Ingestion



Pratical Implementation

Introduction 1.The visualization design methodology.

2. The Data Visualization Process.

3. Working with Single Data Sources.

4. Using Multiple Data Source

5. Using Calculations in Tableau.
An introduction to various data visualisation techinques
and later tying them back to varios scenarios
Case Study 1.Health Care Analysis

2. Telecommunications

3. Web-anaytics

4. Comparing Measures Against a Goal

5. Tableau Geo coding, Advanced Mapping

6. Showing Distributions of Data

7. Statistics and Forecasting

8. Dashboard Best Practices

9. Sharing Your Work
Given various case scenarios, tying various DV techniques to different industry analysis such as as health-care, telecomunications and web-analytics

Faculty Profile

Dr. Russell Walker

Hello and welcome! I'm Professor Russ Walker. I've been a full-time professor at DeVry University in Long Beach, California since 2000. I also have my own consulting practice doing medical software development. Earlier in my career, I held executive and management positions with large and small companies in the health care and aerospace industries. Projects I've worked on include a web-based calculator for bone fracture risk, a radiation probe used in cancer surgery, a digital x-ray analyzer, and the launch control system for the Peacekeeper nuclear missile.

I earned a PhD in Business Administration/Applied Computer Science from Northcentral University, an MS in Applied Physics from the California Institute of Technology, an MBA from California State University, Long Beach, and a BS degree in Physics and Computer Science from Murray State University. My research interests involve e-learning applications of recommended systems (like the ones that suggest movies you might like on Netflix or books you might want to read on Amazon).

Dr. Darlene Ringhand

I have 25+ years of experience in corporate business and small business ownership. My areas of expertise and experience include business, software, HR, and management.

My education includes a BS in Business Administration, a MS in Business and Information Systems and a Ph.D. in Business Administration with certifications in security! All three degrees were earn while working through distance education or Online!


Course Date Phone (Toll Free)
keller Data science 3rd July 18002005835

Why EduPristine - Keller

EduPristine is India's leading online and classroom training provider for international certifications in Analytics (Data Science, Business Analytics and Big Data & Hadoop), Accounting (CPA and CMA) and Finance (CFA®, FRM® and PRM®). EduPristine has conducted more than 1 million man hours of quality training for J. P. Morgan, Bank of America, E&Y, ING Vysysa, IIM Calcutta, NUS Singapore, ISB and others. It’s headquartered in Piscataway, New Jersey, USA and we have served professionals in more than 40+ countries all around the world; mostly in Middle East, Africa, Asia and United States. EduPristine has been founded by industry professionals who have worked in the area of private equity and investment banking in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.

When you earn your master’s degree from DeVry University’s Keller Graduate School of Management, you’re not only benefiting from the experience and knowledge of more than 80 years in technology and business-based higher education behind you, you’re earning a degree from an accredited university.

DeVry University* is accredited by The Higher Learning Commission (HLC). Keller's experiential learning approach and flexibility are offered to elevate the educational experience for motivated working professionals, and help you channel your ambition to stand out in today’s ever-evolving marketplace. Here, you can take part in active problem-solving sessions with professors and classmates, and use technology to arrive at innovative answers to pressing challenges in business today.

Certificate Program in Data Science

Program Offerings

Complimentary Live Basic stats session to brush up
your knowledge before classroom sessions starts.

Explore towards International Faculty.


Rs.2,25,000 (inclusive of all taxes)

225+ of learning contact hours

150 Hrs. classroom Session.

180+ of homework contact hours.

114+ hours of live project work

Hands on project execution on CloudLabs


Cloudera (CCA-175) Exam Guidance (Online)

Complimentary course on Java essentials for Hadoop, Python and UNIX session

90 days of access to EduPristine Virtual Lab for MultiNode Cluster



HI,I m vinit, I joined Big Data training In Edupristine in the month of Feb 2015 in Delhi location. It was so good Exp. over their. my Guide Mr. Nitin is such a nice person and expert also in this field. He thought us in depth about the concept of Big Data, Hadoop and all. The support from the side of Edupristine is also appreciable.So according to me this is the best place for Big Data.

I joined EduPristine to understand what Business Analytics subject is. It was great experience to learn "R" & "SPSS" software, how this software actually used. The contain& faculty actually gave us great base in analytics field.

Popular Courses

GARP does not endorse, promote, review or warrant the accuracy of the products or services offered by EduPristine of GARP Exam related information, nor does it endorse any pass rates that may be claimed by the Exam Prep Provider. Further, GARP is not responsible for any fees or costs paid by the user to EduPristine nor is GARP responsible for any fees or costs of any person or entity providing any services to EduPristine. ERP®, FRM®, GARP® and Global Association of Risk Professionals™ are trademarks owned by the Global Association of Risk Professionals, Inc.

CFA® Institute does not endorse, promote, or warrant the accuracy or quality of the products or services offered by EduPristine. CFA® Institute, CFA® Program, CFA® Institute Investment Foundations and Chartered Financial Analyst® are trademarks owned by CFA® Institute.

Utmost care has been taken to ensure that there is no copyright violation or infringement in any of our content. Still, in case you feel that there is any copyright violation of any kind please send a mail to and we will rectify it.

dm_classroom_courses.php Post ID = 100507