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Certificate Program in Data Science

BUSINESS ACCOUNTING AND TAXATION COURSE

Course Objective

The 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 course will enable you to apply the concepts learnt by building a data product using real-world data.

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Data Science Course

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

  • Certificate of completion - from Keller Graduate School of Management (U.S) - On successful completion of Applications of
    Business analytics - I & II Module.
  • Data Scientist certificate from EduPristine

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.

COURSE CURRICULUM

Topics

Readings

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.
Modeling and Analysis 1.Model-Based Decision Making: Optimization and Multi-Criteria Systems

2. Modeling and Analysis: Heuristic Search Methods and Simulation 
Given various case studies, model an appropriate solution using spreadsheet software to analyze statistical, financial, heuristic, and optimization business problems.
Internet Analytics and Social Media Analysis 1. Text Analytics, Text Mining, and Sentiment Analysis

2. Web Analytics, Web Mining, and Social Analytics   
Given a set of data from a social media source, develop a model that explains the impact of the results on a business process.
Business Performance Measurement 1. Business Reporting, Visual Analytics, and Business Performance Management

2. Knowledge Management and Collaborative Systems
Given a large (wide) data set, identify the salient variables, reduce them to summary form, and develop an explanation of the results using a business process model.
Data Warehousing and Dimensional Modeling 1. Data Warehousing

2. Big Data and Analytics 
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.
Data Mining for Business Intelligence 1. Data Mining for Business Intelligence

2. Techniques for Predictive Modeling
Given a business scenario appropriate for a data-mining approach, develop a plan for analysis and implementation of a solution using one or more data-mining techniques.
Implementing Business Intelligence and Emerging Trends 1.Automated Decision Systems and Expert Systems

2.Business Analytics: Emerging Trends and Future Impacts
Given a decision support system proposal, formulate an appropriate implementation plan, define the role of the project participants, and identify applicable costs and management issues, including ethical considerations.

Topics

Readings

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:
  • Volume is large - Batch Analytics
  • Velocity is High - Real Time Analytics
  • Variety in Data - Unstructured or Semi Structured data
  • 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
Live Project II 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

Topics

Readings

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¨ıve 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

Topics

Readings

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

Topics

Readings

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

Why Keller

We recognize the essential value of technology in business, and look to it to inform both what and how we teach. At Keller, we offer a
tech-centric focus throughout the institution because it is central to how our students work, grow and thrive. We teach our students to leverage technology to integrate people, process, data and devices, enabling them with the kind of knowledge and tools that move businesses forward.

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.

The heritage of Keller Graduate School of Management has centered on leadership and innovation since being founded as the CBA Institute by Dennis Keller and Ron Taylor in 1973. Early on, Keller and Taylor saw a real need to serve working adult students and the companies that employed them. So they advanced a practitioner approach based on teaching vital business and management skills that could immediately be applied at work.
Having merged to form DeVry University in 2001, Keller continues to help students learn to solve real-world problems with the power of innovation in a technical world, as they earn their graduate degrees from an accredited university

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. Our specialized graduate programs and convenient online MBA degree programs contain the skills and flexibility you want; our reputation and accreditation status provide the recognition and credentials you need.

DeVry University* is accredited by The Higher Learning Commission (HLC), www.hlcommission.org. The University’s Keller Graduate School of Management is included in this accreditation.The HLC is a regional agency that accredits U.S. colleges and universities at the institutional level; is recognized by both the U.S. Department of Education and the Council for Higher Education Accreditation. Accreditation provides assurance to the public and to prospective students that standards of quality have been met.
DeVry University is a member of the Council for Higher Education Accreditation (CHEA), a national advocate and institutional voice for self-regulation of academic quality through accreditation. CHEA, an association of 3,000 degree-granting colleges and universities, recognizes 60 institutional and programmatic accrediting organizations.

Keller Graduate School of Management ( U.S) - Certificate Program in Data Science
  Price (INR)
Program Offerings Rs.2,25,000 (inclusive of all taxes)
225+ of learning contact hours
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
Buy

TESTIMONIALS

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.

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