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Predictive Business Analytics Program-Chennai

The Predictive Business Analytics Program in Chennai is an intensive, 100+ hours program curated by Industry experts for high-performing individuals who wish to master the tools of predictive analytics and give a boost to their careers. Get practical and experiential learning, along with soft skills and 100% placement assistance.

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The demand for Business analytics is huge in both domestic and international job markets. According to Newscom, India’s analytics market would increase two times to INR 15000 Crores by the end of 2019. There has never been a better time to make a career into Business Analytics. EduPristine will assist you in preparing for a career in the field of data analytics by providing you end-to-end career support, right from skill-based business analytics training to job assistance.

Here is how we bridge the skill gap and prepare you to be job ready


Practical & Experiential Training

  • Industry relevant content
  • Case-study based hands-on training
  • Training by Subject Matter Experts
  • Analytics tools – 'R' Software, Excel
  • Dedicated Discussion Forums
  • After Course Engagement (ACE)
+

Soft Skill Training

  • Business communication
  • Presentation skills
  • People skills
  • Business etiquette
  • Problem solving skills
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Market Ready

  • Frequent job updates
  • Customised career guidance
  • Interview skills workshop
  • Resume preparation assistance
  • 100% job assistance
  • Get Business Analytics Certifications

Program Highlights

Classroom Training

On-boarding Live Session

105 hrs Instructor led Training

Self-paced learning content

Real-life case studies

Post Session practice projects/ case studies

Tools like R and Excel

Experienced and Passionate Trainers

24*7 Learning Management System (LMS) access with a 1 year validity

Dedicated Discussion Forums

After Course Engagement (ACE)

100% Job Assistance

Corporate Training

Customized Training Solutions

Requirement based Training Modules

Foundation to Expert level Curriculum

Industry Expert Trainers

Learning Management System (LMS) access

Training at Your Premises

In Person/ Online Live Virtual Classes (LVC)

Case based Training/Simulations

Comprehensive Study Notes

Single Point of Contact for all communications

Course Curriculum

  1. Introduction to Programming
  2. Introduction to R and RStudio
    1. What is R?
    2. What is Open Source?
    3. Capabilities of R
    4. GUI for R
    5. R IDE -   RStudio
  3. Installation and Functioning of R and Rstudio
  4. Using R
    1. R interface
    2. R Session
    3. R Console
    4. Getting Help
    5. Entering and Running Commands/Programs
  5. Programming in R
    1. Data Types
    2. Operators in R
    3. Data Input and Output
    4. R Data Frames
    5. R statistics – Mean, Median, Mode etc.
    6. Data Manipulation in R – Counting, Merging, Append, Sort, Subset, Filter, New Variable Creation etc.
    7. R Logical Statements - If/ else, Loops etc.
    8. Plotting- Graphs and Charts
    9. Packages in R- Details of the most commonly used packages
  6. Functions in R (High Level)
  7. R- Best Practices

Statistics »

  1. What is Statistics
  2. Data Types
    1. Qualitative vs. Quantitative
    2. Basic Operations Based on Data Type
  3. Variables
  4. Measurement Scales
  5. Measures of Variance
  6. Measures of Central Tendency
  7. Correlation vs. Causation (Correlational vs. Experimental Research)
  8. Sampling – Usage of Sampling
  9. Distributions
  10. Normal Distribution
    1. Why the "Normal distribution" is Important
    2. Illustration of How the Normal Distribution is Used in Statistical Reasoning
    3. Characteristics
    4. Standard Normal Distribution
  11. Central Limit Theorem
  12. Hypothesis Testing
    1. What is Hypothesis Testing?
    2. The magic of Hypothesis Testing
    3. Null and Alternate Hypothesis
    4. P Value
    5. Usage of Hypothesis Testing in Business Problems
    6. Explanation of Hypothesis Testing Using Real World Example
  13. Types of Hypothesis Testing
    1. Z test
    2. T test
    3. Chi Square test
  14. Introduction to ANOVA and Basics of Regression/Classification

Linear Regression Topic Details »

  1. Introduction to Simple Linear Regression
  2. Graphical Understanding of Regression (Scatter Plot, Box Plot, Density Plot)
  3. Example Problem and Mathematics behind Regression
  4. Assumptions for Linear Regression
  5. Correlation (Linear and Non Linear
  6. Introduction to Multiple Linear Regression
  7. Building A Regression Model (Steps to Establish a Regression)
    1. Data Preparation – Data Audit, Missing Value and Outliers
    2. Building the model
  8. Linear Regression – Interpretation of  Output and Diagnostics
    1. Assessing the Coefficients
    2. P Value - Checking for Statistical Significance
    3. R-Square and Adjusted R Squared
    4. Standard Error and F-Statistic
  9. How to Know if the Model is Best Fit for Your Data?
  10. Using Linear Model for Predictions
    1. Checking Accuracy and Error Rates
    2. Heteroskadisticity
  11. Model Improvement
    1. Over-fitting and Cross Validation
    2. Multicollinearity and VIF
  12. Do it Yourself Case
  13. Flavor of Advanced Regression Models

Logistic Regression »

  1. Why Logistic Regression
    1. Introduction to Classification and Challenges with Linear Regression
    2. Event Rate and Class Bias
  2. Example Problem (Some real world examples of Binary Classification problems),Mechanics and Mathematics behind Logistic Regression
  3. Assumptions for Logistic Regression
  4. Building a Logistic Regression Model
    1. Data Preparation – Data Audit, Missing Value and Outliers
    2. Variable Importance and Feature Extraction
    3. Create WOE for Categorical Variables
    4. Compute Information Value
  5. Multicollinearity (VIF)
  6. Building Logit Models
  7. Predictions
  8. Logistic  Regression – Interpretation of  Output
    1. Coefficients
    2. Variable Importance
  9. Model Diagnostics
    1. Misclassification Error and Confusion Matrix
    2. ROC Curve
    3. Accuracy
    4. Specificity, Sensitivity and F Score
    5. Lift/Gain Charts and KS Curve
  10. Model Improvement
    1. Over-fitting and Cross Validation
  11. Flavor of Advanced Classification Concepts – Classification of Unstructured Data
  12. Do it Yourself Case

Time Series Modeling »

  1. Introduction to Time Series
    1. Difference between Time Series, Cross-Sectional and Pooled Data
  2. Example Problem (Some real world examples of Time Series Problems), Mechanics and Fundamental of Mathematics behind  Time series Analysis
  3. Assumptions for Time Series analysis
  4. Understanding Time Series Data
    1. Visualizing Time Series Data
    2. Stationary vs. No Stationary Data
    3. Trend vs Seasonality vs White Noise
  5. Decomposing Time Series Data
    1. Decomposing Non-Seasonal Data
    2. Decomposing Seasonal Data
    3. Seasonally Adjusting
  6. Forecasts using Exponential Smoothing
    1. Simple Exponential Smoothing
    2. Holt’s Exponential Smoothing
    3. Holt-Winters Exponential Smoothing
    4. Challenges with Smoothing
  7. ARIMA Models
    1. Concept of Auto-Correlation and Partial Auto Correlation
    2. Differencing a Time Series
    3. Selecting a Candidate ARIMA Model
    4. Forecasting Using an ARIMA Model
    5. Predictions and Diagnostics
  8. Advanced Time Series Concepts
  9. Do it Yourself Case

Market Basket Analysis »

  1. Supervised, Unsupervised and Semi-supervised Algorithms
  2. Concept of a Recommendation Engine
  3. Example Problem (Real world examples of MBA applications
  4. MBA Hyper Parameters
    1. Lift
    2. Confidence
    3. Support
  5. Generating output using Association rules
    1. Filtration of Rules
    2. Removal of Redundant Rules
    3. Control the Rules
    4. Finding rules for Particular Entity
    5. Visualizing Rules
  6. Challenges with Association Rules and Ways to Overcome
  7. Advanced Recommendation Engine Concepts
  8. Do it Yourself Case

Decision Trees »

  1. Type of Classification Algorithms
  2. Fundamentals of Tree bases Systems
  3. Decision Boundary of Tree based Algorithms
  4. Types of Tree Algorithms
    1. C4.5
    2. CHAID
    3. CART
  5. Concept of Impurity Measure
    1. GINI
    2. Chi Square
    3. Entropy
  6. Building a Decision Tree Model
    1. Data Preparation – Data Audit, Missing Value and Outliers
    2. Creating Test and Training Samples
    3. Variable Importance and Feature Extraction
  7. Prediction using Decision Trees
  8. Over fitting and Cross Validation
  9. Flavor of Advanced Concepts in Trees (Random Forests)

K-Means Clustering »

  1. Unsupervised Algorithms and Introduction to Clustering
    1. Intro to Distance based Algorithms
  2. Example Problem (Some real world examples of Clustering Applications
  3. Assumptions for Clustering
  4. Mechanics of Clustering
    1. Type of Measure- Euclidean, Manhattan, Jaccard
    2. How are Clusters Generated
    3. Using Clustering as a Base for Segmentation
  5. Creating Clusters
    1. Standardization of Inputs
    2. Deciding the Number of Clusters – Elbow Curve and Silhouette Distance
  6. Understanding the Output
    1. Cluster Diagnosis
    2. Profiling
  7. Advanced Clustering Concepts
  8. Do it Yourself Case

Soft Skill Training »

  1. Interpersonal Communication
    1. Introduction of Interpersonal Relations
    2. Johari Windows
    3. Personal Effectiveness Inventory
    4. Listening skills  
    5. Closure
  2. Presentation skills I and II
    1. Different Stages of Making a Presentation –  Analysis, Thinking, Data, Creativity Needed at Each Stage
    2. Plan before Execution
    3. Plan Speech – Structures of Presentation, Numerical Prsentation, Visual Aids
    4. Requirements for Impactful Delivery
  3. Email and virtual communication
    1. Intent in Virtual Communication – NTPL Activity
    2. Basics of Email Etiquette + Case Sudies 
    3. Styles of Writing + Case Study + Activity of Reading 
    4. Virtual – Key Issues + Cross-cultural Email 
    5. Digital Empathy
    6. Closure
  4. Assertiveness & Communication
    1. Introduction to Assertiveness
    2. Assertive, Non-assertive, and Aggressive (Inner Dynamics and Outer Behavior)
    3. Reflection Sheet 
    4. Assertiveness Technique and Role Plays 
    5. Recap 
  5. Interview Skills

Must Know Business Analytics Tools that You will Learn in Class


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Our Alumni Speak

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It's been a great learning experience that I got it from EduPristine. I pursued Business Analytics course in Chennai with Edupristine and the course content is on par with the industry standards. Some of the faculties from top notch companies shared their experience on the real time projects which helps us to understand the protocol of the Business Analytics and MS Excel as well. Great Learning experience! Happy Learning.

Santhosh Kumar 
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EduPristine institute provides a good platform for learning Business Analytics profile. I'm happy with the study material and classes they take, are taken in good study environment with skilled professional tutor to know the basic concept of BA profile.“

Vinayak Karsale
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With respect to the Analytics course curriculum followed at the Institute, I would like to commend the sheer guidance and professionalism of the institute and its employees who have guided me for the Course in Business Analytics as well as Big Data and Hadoop course over a period of a year. This is the first and only Institute where I have seen motivation and positive thinking at all times. I would like to Thank Counsellor, support team and the trainer who has guided and mentored in the due time period. I would recommend this institute to my known ones who are interested in the same field.

Abhijeet Panda

Batch Schedule


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Location Start Date Batch Type Class Timing
Location Start Date Batch Type Class Timing
Location Start Date Batch Type Class Timing

Apply for the Predictive Business Analytics

We only take 30 students per batch.

Note: The regular decision deadline for the enrollment is 2 weeks before the batch start.



Why EduPristine


Our Training Methodology:

Our students can expect a mix of case studies, analytical tools, and the hallmark pedagogy of learning through a plethora of hands-on and real-life scenarios. Finishing the course with our state-of-the-art Capstone project helps the participants to season their skills as a Business Analytics professional; thus, making them ready for the job role. The methodology offers real-life challenges, compelling learners to apply concepts in the class and prepare for career realities.

What EduPristine is known for:

EduPristine is a member of Adtalem Global Education (NYSE: ATGE), a global education provider headquartered in the United States. Adtalem is a $3 billion ( INR 20,000 crores) company that has about 9 institutions and companies with more than 16,000 employees spread across 145 locations. Adtalem takes pride in training 142,000 degree-seeking students all over the world.The organization's purpose is to empower students to achieve their goals, find success and make inspiring contributions to our global community. EduPristine is one of India's leading training providers in Analytics, Accounting, Finance, Healthcare, and Marketing. Founded in 2008, EduPristine has a strong online platform and network of classrooms across India and caters to self-paced learning and online learning, in addition to classroom learning.

Chennai is the capital city of the Indian state of Tamil Nadu. Located on theCoromandel Coast off the Bay of Bengal, it is the biggest industrial and commercial centre in South India, and a major cultural, economic and educational centre. Chennai is known as the "Detroit of India" for its automobile industry.The city is host to the third largest expatriate population in India after Mumbai and Delhi, with 35,000 in 2009 and steadily climbing to 82,790 in 2011.

A large alumni network of 50,000+ students

15+ locations and 35 industries

A faculty pool of 600+ Industry experts

Soft skill training & 100% Job assistance

FAQs WHY?

What's special about Edupristine program on Analytics?

The comprehensive Analytics program by EduPristine has been designed to create complete analytics professionals. It has been designed by experts with inputs from several leading analytics companies. It is delivered by practitioners who have rich experience of having worked for some of the best analytics firms and Fortune 500 clients. And the program uses a case-based approach to learning.

What is the eligibility for this program?

Learners with a background in business management, engineering, mathematics, statistics, or economics are best placed to undertake this program.

Who should go for this course?

This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices.

What are the prerequisites of this course?

The participants are expected to have the basic understanding of the following topic: 1)Basic Statistics* 2)To benefit from this course, you should be familiar with various aspects of Excel (preferably Excel 2007 or 2010).

Which Tools I will be learning?

You will be learning "R Studio" in classroom session, additionally in Online Instructor based training you will learn Interpretation on "SAS Language".

Is this classroom session?

Yes, this will be purely classroom session we are conducting at 4 metro cities. Participants who cannot attend physically we have online Instructor based training also.

Is the program offered India wide?

No. Currently it is available in selected centres in Bengaluru, Mumbai, Pune, Delhi. It may be extended to other Edupristine centers in future.

Is the course conceptual or hands-on?

The program has been designed based on the inputs of some of the leading analytics firms and is a balance between concepts and hands-on practice. There are intensive assignments, case-studies and projects.

What are some of the job profiles at the entry level in Analytics?

Business Analyst, Data Analyst, Data Manager

What kind of job description companies look forward?

To Analyse specific business problems and derive solutions using an array of analytic tools.
Interpret statistical result and link them to the business context.
Improving Systems by studying current practices, designing modification.

Which are the some of the big Analytics companies with operations in India?

The following is only a representative list: 1. IT Accenture, Infosys ,TCS,Wipro; 2. KPO - Genpact, WNS; 3. Citibank, Dell, HP, Spencers, Target; and 4. Niche: Brainmatics, Fractal Analytics, Mu Sigma.

Why would one go for this field? What is the future scope in this domain?

This is one of the most exciting fields. The Harvard Business Review named it as one of the 'sexiest jobs of the 21st century.

Can and should professionals with experience in some other fields switch to?

This is one of those fields that people are switching to even very late in their careers because it is exciting and extremely well-paid with exceptionally bright growth prospects. The data revolution is just beginning!

Is this a theory oriented program or are will there also be practicals?

This program has been designed using inputs from the industry and includes a balance between concepts and practical, hands-on skills. Throughout the program there are extensive assignments, cases and projects which ensure that learners pick up the requisite concepts and skills.

Do I need to know programming to enroll into this program?

No. Programming is not required as a pre-requisite. However, those who are familiar with programming will be able to use it to their advantage

I have no IT experience. Is this program for me?

IT experience is not necessary for this program.

What kind of jobs am I likely to get after this training?

Almost all the big corporations need analytics professionals. You can be hired in your own industry at entry/mid/senior level depending upon your experience and the training you have had. IT companies and Analytics KPOs hire people to work on client projects which cut across functional and vertical domains. If you have no prior work experience, you best option is to join one of these.

Who will be teaching the programs?

A mix of academic and industry experts, people who have worked on large analytics projects for Fortune 500 companies in retail, media, telecom, banking, insurance etc across the globe and taught at some of the premier institutes in India and abroad.

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