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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based

Course Overview

Predictive Business Analytics is experiencing a boom in the business intelligence sector and offers a promising future to high-performing skilled professionals. The EduPristine Business Analytics certification course will equip business professionals to improve career opportunities in various industries. Tools like R, Python, Excel, Anaconda, Jupyter, and more have become a prerequisite to entering the business analytics market, and this course covers them all.

The Business Analytics Certification Training is an intensive online course with 100+ hours of instructor-led training. It combines relevant theoretical and experiential learning. The subject matter experts at EduPristine provide real-life case studies and comprehensive study notes through an extensive curriculum of 12 modules. These include building a foundation for business analytics through various analytics tools and working on capstone projects.

Further, the Business Analytics Certification Course by EduPristine also provides soft skills training and hands-on scenario simulation. The programme admits only 30 students in a batch. It goes beyond classroom training as there are dedicated discussion forums, interview skills workshops, and After Course Engagement (ACE).

The Highlights

109 hours of instructor-led training
Self-paced education
Live virtual classes
Access to LMS for one year
Case-studies
Hands-on training
Business Analytics certification
Capstone project
Dedicated discussion forums
After Course Engagement (ACE)
Comprehensive study notes
Interview skills workshop
Soft skill training
Tangible Career Benefits

Programme Offerings

Classroom training
Corporate training
post session case studies
Comprehensive course material
job update
interview preparation
Capstone Projects
discussion forum
business analytics certification

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Edupristine

Eligibility Criteria

The candidates with a professional or academic background in Mathematics, Economics, Business Management, or Engineering are fit to enroll in this course.

Participants must have a basic knowledge of statistics and analytics tools like Excel and R software to make the learning process more comfortable.

What you will learn

Data science knowledgeMachine learningPresentation skillsKnowledge of PythonKnowledge of Excel

After successful completion of the course in Business Analytics, candidates will be adept in the following skills:

Performing data analytics in Excel using data manipulation and statistical functions
Learning to develop supervised, semi-supervised, and unsupervised algorithms to conduct market basket analysis
Knowledge of business analytics via Python, Anaconda, Jupyter, and R
Building a decision tree model and learning about tree-based algorithms
Understanding assumptions, mechanics, and diagnosis in clusters
Familiarity with R and R Studio and understanding their installation, functionality, and capabilities

Who it is for

Data AnalystSales ManagerBusiness AnalystBusiness Development Executive

This Business Analytics Course is meant for everyone who wants to step into the analytics industry and enhance their technical and programming skills. Students and professionals from a business background are the best fit for this course, but even IT professionals and interested candidates from other industries can apply for this course.

No programming knowledge is required before joining the course; however, candidates should have a basic understanding of statistics, excel and R software.

The Syllabus

Introduction to MS Excel

Why MS Excel
Functionalities for a Data Scientist
Using Excel

Data Analysis in Excel

Basic Data Manipulation Functions
Mean, Maximum, Round, Sum etc.

Statical Functions

Filter, Sort, Lookup in Excel

Using Pivots in Excel

Creating Pivot Tables and Charts
Usage of Slicers

Plotting in Excel – Usage of Visualization Capabilities

Introduction to Programming

Introduction to R and RStudio

What is R?
What is Open Source?
Capabilities of R
GUI for R
R IDE - RStudio

Installation and Functioning of R and Rstudio

Using R

R interface
R Session
R Console
Getting Help
Entering and Running Commands/Programs

Programming in R

Data Types
Operators in R
Data Input and Output
R Data Frames
R statistics – Mean, Median, Mode etc.
Data Manipulation in R – Counting, Merging, Append, Sort, Subset, Filter, New Variable Creation etc.
R Logical Statements - If/ else, Loops etc.
Plotting- Graphs and Charts
Packages in R- Details of the most commonly used packages

Functions in R (High Level)

R- Best Practices

What is Statistics

Data Types

Qualitative vs. Quantitative
Basic Operations Based on Data Type

Variables

Measurement Scales

Measures of Variance

Measures of Central Tendency

Correlation vs. Causation (Correlational vs. Experimental Research)

Sampling – Usage of Sampling

Distributions

Normal Distribution

Why the "Normal distribution" is Important
Illustration of How the Normal Distribution is Used in Statistical Reasoning
Characteristics
Standard Normal Distribution

Central Limit Theorem

Hypothesis Testing

What is Hypothesis Testing?
The magic of Hypothesis Testing
Null and Alternate Hypothesis
P Value
Usage of Hypothesis Testing in Business Problems
Explanation of Hypothesis Testing Using Real World Example

Types of Hypothesis Testing

Z test
T test
Chi Square test

Introduction to ANOVA and Basics of Regression/Classification

Introduction to Simple Linear Regression

Graphical Understanding of Regression (Scatter Plot, Box Plot, Density Plot)

Example Problem and Mathematics behind Regression

Assumptions for Linear Regression

Correlation (Linear and Non Linear)

Introduction to Multiple Linear Regression

Building A Regression Model (Steps to Establish a Regression)

Data Preparation – Data Audit, Missing Value and Outliers
Building the model

Linear Regression – Interpretation of Output and Diagnostics

Assessing the Coefficients
P Value - Checking for Statistical Significance
R-Square and Adjusted R Squared
Standard Error and F-Statistic

How to Know if the Model is Best Fit for Your Data?

Using Linear Model for Predictions

Checking Accuracy and Error Rates
Heteroskadisticity

Model Improvement

Over-fitting and Cross Validation
Multicollinearity and VIF

Do it Yourself Case

Flavor of Advanced Regression Models

Why Logistic Regression

Introduction to Classification and Challenges with Linear Regression
Event Rate and Class Bias

Example Problem (Some real world examples of Binary Classification problems),Mechanics and Mathematics behind Logistic Regression

Assumptions for Logistic Regression

Building a Logistic Regression Model

Data Preparation – Data Audit, Missing Value and Outliers
Variable Importance and Feature Extraction
Create WOE for Categorical Variables
Compute Information Value

Multicollinearity (VIF)

Building Logit Models

Predictions

Logistic Regression – Interpretation of Output

Coefficients
Variable Importance

Model Diagnostics

Misclassification Error and Confusion Matrix
ROC Curve
Accuracy
Specificity, Sensitivity and F Score
Lift/Gain Charts and KS Curve

Model Improvement

Over-fitting and Cross Validation

Flavor of Advanced Classification Concepts – Classification of Unstructured Data

Do it Yourself Case

Introduction to Time Series

Difference between Time Series, Cross-Sectional and Pooled Data

Example Problem (Some real world examples of Time Series Problems), Mechanics and Fundamental of Mathematics behind Time series Analysis

Assumptions for Time Series analysis

Understanding Time Series Data

Visualizing Time Series Data
Stationary vs. No Stationary Data
Trend vs Seasonality vs White Noise

Decomposing Time Series Data

Decomposing Non-Seasonal Data
Decomposing Seasonal Data
Seasonally Adjusting

Forecasts using Exponential Smoothing

Simple Exponential Smoothing
Holt’s Exponential Smoothing
Holt-Winters Exponential Smoothing
Challenges with Smoothing

ARIMA Models

Concept of Auto-Correlation and Partial Auto Correlation
Differencing a Time Series
Selecting a Candidate ARIMA Model
Forecasting Using an ARIMA Model
Predictions and Diagnostics

Advanced Time Series Concepts

Do it Yourself Case

Supervised, Unsupervised and Semi-supervised Algorithms

Concept of a Recommendation Engine

Example Problem (Real world examples of MBA applications

MBA Hyper Parameters

Lift
Confidence
Support

Generating output using Association rules

Filtration of Rules
Removal of Redundant Rules
Control the Rules
Finding rules for Particular Entity
Visualizing Rules

Challenges with Association Rules and Ways to Overcome

Advanced Recommendation Engine Concepts

Do it Yourself Case

Type of Classification Algorithms

Fundamentals of Tree bases Systems

Decision Boundary of Tree based Algorithms

Types of Tree Algorithms

C4.5
CHAID
CART

Concept of Impurity Measure

GINI
Chi Square
Entropy

Building a Decision Tree Model

Data Preparation – Data Audit, Missing Value and Outliers
Creating Test and Training Samples
Variable Importance and Feature Extraction

Prediction using Decision Trees

Over fitting and Cross Validation

Flavor of Advanced Concepts in Trees (Random Forests)

Unsupervised Algorithms and Introduction to Clustering

Intro to Distance based Algorithms

Example Problem (Some real world examples of Clustering Applications)

Assumptions for Clustering

Mechanics of Clustering

Type of Measure- Euclidean, Manhattan, Jaccard
How are Clusters Generated
Using Clustering as a Base for Segmentation

Creating Clusters

Standardization of Inputs
Deciding the Number of Clusters – Elbow Curve and Silhouette Distance

Understanding the Output

Cluster Diagnosis
Profiling

Advanced Clustering Concepts

Do it Yourself Case

Understanding Python

Installing Python through Anaconda
Jupyter Notebook
Python Functions
Python Data types
Tuples
Lists
Dictionaries
Understanding the Data
Python Packages for Data Science
Importing and Exporting Data
Getting Started Analyzing Data
The DataFrame Data Structure
DataFrame Indexing and Loading
Querying a DataFrame
Group by
Scales
Pivot Tables
Dealing with Missing Values
Data Formatting
Data Normalization

Categorization in Python

Turning categorical variables into quantitative variables
Data Wrangling
Identifying Correlation
Correlation - Visualization
Case Example - Classification
Exploratory Data Analysis
Using Logistic and Decision Tree for Model Development
Model Evaluation using Visualization
Model Evaluation
Overfitting, Underfitting and Model Selection

Analysis in R and Statistics

Analyzing and summarizing Credit Card data
Finding out the probability to score marks between two numbers
Feasibility of launching a new school
Identifying Gender discrimination

Linear Regression

Calculating Tree volume
Estimating distance covered based on the speed of different cars
Predicting the number of shares of the article on Social Networks (Popularity)

Logistic Regression

Predict if a customer is likely to default on their debts
Estimate the chance of selling different products to existing bank customers (Cross-Sell)

Time Series

Predict savings rate base on historical data
Predicting Google stock price
Predicting quarterly cement production in Australia
Forecast demand of cycles for bike sharing service

Clustering

Segmenting trains based on their characteristics

Decision Trees

Should a bank give credit or not
Predict the miles per gallon a car will average based on cylinders and horsepower
Predicting the sales price of the house

Analytics in Python

A customer is high income or not

Market Basket Analysis

Chances of buying milk with butter
Providing suggestions to a customer who visits the website

Interpersonal Communication

Introduction of Interpersonal Relations
Johari Windows
Personal Effectiveness Inventory
Listening skills
Closure

Presentation skills I and II

Different Stages of Making a Presentation – Analysis, Thinking, Data, Creativity Needed at Each Stage
Plan before Execution
Plan Speech – Structures of Presentation, Numerical Presentation, Visual Aids
Requirements for Impactful Delivery