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Business Analytics Course

This program is no longer available

Business Analytics Course

The Predictive Business Analytics Course curated by industry experts for high-performing individuals who want to boost their careers in Data Science and Machine Learning. This intensive 100+ hours course is an amalgamation of a top grade data science capstone project, case-study based machine learning assignments, industry-relevant practical and experiential learning and end-to-end Career Services.

100+ Hrs Training | Industry Trainer | ACE | Placement Assistance

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Fulfil Your Career Goals in a Booming Industry

Spot a Promising Future in Your Favorite Industry!

Biggest Spenders on Data Science & Machine Learning (Sector Wise)

Business Analytics Certification Course

Your Indicative Salary in Analytics!

in India

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The demand for data science and machine learning professionals is huge in both domestic and international job markets. According to Analytics India, Analytics, Data Science and Big Data industry has grown to $3.03 billion in size in 2019 and is expected to double by 2025. This showcase, there has never been a better time to start and make a career in this growing industry. EduPristine with its hallmark methodology of practical and experiential learning assist its students in preparing for a career in the field of data science, machine learning and predictive analytics by providing end-to-end career support, right from skill-based 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
  • Capstone Project
  • Training by subject matter experts
  • Analytics tools – 'R' Software, Excel, Python
  • Dedicated discussion forums
  • After Course Engagement (ACE)
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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
  • Placement assistance
  • Career Services
  • Business Analytics Certifications

Program Highlights

Classroom Training

On-boarding live session

109-hrs instructor led Training

Self-paced learning content

Real-life case studies

Post session practice projects/ case studies

Tools like R, Excel and Python

Experienced and passionate trainers

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

Dedicated discussion forums

After Course Engagement (ACE)

Career Services

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

Data Science & Machine Learning 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

Analysis in R and 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 »

  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)

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

Python for Analytics »

  1. Understanding Python
    1. Installing Python through Anaconda
    2. Jupyter Notebook
    3. Python Functions
    4. Python Data types
      • Tuples
      • Lists
      • Dictionaries
    5. Understanding the Data
    6. Python Packages for Data Science
    7. Importing and Exporting Data
    8. Getting Started Analyzing Data
    9. The DataFrame Data Structure
    10. DataFrame Indexing and Loading
    11. Querying a DataFrame
      • Group by
      • Scales
      • Pivot Tables
    12. Dealing with Missing Values
    13. Data Formatting
    14. Data Normalization
  2. Categorization in Python
    1. Turning categorical variables into quantitative variables
    2. Data Wrangling
    3. Idenifying Correlation
    4. Correlation - Visualization
      • Case Example - Classification
      • Exploratory Data Analysis
      • Usign Logistic and Decision Tree for Model Development
      • Model Evaluation using Visualization
      • Model Evaluation
      • Overfitting, Underfitting and Model Selection

Case Study »

  1. Analysis in R and Statistics
    1. Analyzing and summarizing Credit Card data
    2. Finding out the probability to score marks between two numbers
    3. Feasibility of launching a new school
    4. Identifying Gender discrimination
  2. Linear Regression
    1. Calculating Tree volume
    2. Estimating distance covered based on the speed of different cars
    3. Predicting the number of shares of the article on Social Networks (Popularity)
  3. Logistic Regression
    1. Predict if a customer is likely to default on their debts
    2. Estimate the chance of selling different products to existing bank customers (Cross-Sell) 
  4. Time Series
    1. Predict savings rate base on historical data
    2. Predicting Google stock price
    3. Predicting quarterly cement production in Australia 
    4. Forecast demand of cycles for bike sharing service 
  5. Clustering
    1. Segmenting trains based on their characteristics
  6. Decision Trees
    1. Should a bank give credit or not
    2. Predict the miles per gallon a car will average based on cylinders and horsepower
    3. Predicting the sales price of the house
  7. Analytics in Python
    1. A customer is high income or not
  8. Market Basket Analysis
    1. Chances of buying milk with butter
    2. Providing suggestions to a customer who visits the website

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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The Business Analytics course from EduPristine, really helped me understand the approach to analytics and various tools used for the same. The course is well structured and adequately paced to suit working professionals. The classroom lesson & practice session enabled me to start taking up projects on predictive analytics in my organization almost immediately. This course on Business Analytics is definitely recommended to start your journey in a related career.

Avisek Sarkar 
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EduPristine's Business Analytics Certification course provided a good exposure and training in SAS and R using case studies from varied industries and domains with the skilled professional tutor to know the basic concept of Business Analyst profile.“

Vipul Raj
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The curriculum is neatly structured for a Business Analytics certification course and fee is economical. For working women like me, the flexible online sessions on weekends worked really well. The LMS modules flow nicely and helped me refresh anytime. Faculty Vivek's recordings are commendable in the way he explained each topic, tailored even for a beginner.  The support team, right from the course counsellor up to the backend support team, are very cooperative and prompt in their response. If interested to learn BA and don't have the pertaining subject knowledge, this Business Analytics course is worth the time and money to learn and helped in applying the learnings in real life.  

Saisree Ravindranath
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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
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I have enjoyed the learning in Edupristine tutorials. The courses were well structured, and the instructor was very informative and helpful in getting to know the basics of statistics and analytics.

Shrikant Menon
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We only take 30 students per batch.



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 relevant concepts in class and prepare them 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 $1.2 billion (INR 7560 crores) company that has nine 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, 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.

A large alumni network of 50,000+ students

15+ locations and 35 industries

A faculty pool of 250+ Industry experts

Soft skills training & Career Services

FAQs

Why should one opt for the Analytics program by EduPristine? »

The comprehensive Analytics program by EduPristine has been designed by experts from several leading analytics companies, to create professionals adept at analytics. Our experts are practitioners who have phenomenal work experience with some of the best analytics firms and Fortune 500 clients. The case-based approach to learning is an added plus for you.

What is the eligibility for this program?

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

Who should go for this course?

This course is meant for all students and professionals who are interested in working in the analytics industry and are keen on enhancing their technical skills with exposure to cutting-edge practical learning.

What are the prerequisites of this course?

The participants are expected to have the basic understanding of the following topics:

  • Basic Statistics
  • Various aspects of Excel and R software.

Which tools will I be learning?

You will be learning Excel and R software/ "R Studio" in the classroom session.

Will the program follow classroom instruction sessions?

Yes, there will be classroom sessions that are conducted at five of our centers in metropolitan cities.

Is the program offered across India?

No, at present it is available in select centers in Bengaluru, Delhi, Mumbai, and Pune.

Is the course concept driven or comes with hands-on practices?

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. Hereon, the timely completion of projects and good scores would earn the learners an Excellence certificate as well.

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

The job profiles that feature at the entry level include the likes of Business Analyst, Data Analyst, Data Manager, among others.

What kind of job description do the companies look forward to?

The job descriptions vary from organization to organization. A few of the common points include:

  • To analyze specific business problems and derive solutions using an array of analytic tools.
  • Interpret statistical result and link them to the business context.
  • Improve systems by studying current practices and design modification/s.

Which reputed Analytics companies have operations in India?

The following is only a representative list of the reputed companies that have operations in India:

  • IT – Accenture, Infosys, TCS, Wipro
  • KPO – Genpact, WNS
  • Citibank, Dell, HP, Spencer’s, Target
  • 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 with special reference to present day market scenario. 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 Analytics?

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 practical learning is also imparted?

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

Do I need to know programming to get enrolled in this program?

No. Programming is not required as a pre-requisite. However, those who are familiar with programming will have an advantage.

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

Yes, this program is relevant, because IT experience is not necessary.

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 on your work experience and the training. 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, your best option is to join one of these trainings.

Who are the faculty for this program?

The faculty has a fine mix of academic and industry experts. These people have worked on large analytics projects for Fortune 500 companies in retail, media, telecom, banking, insurance, etc., across the globe.

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