- Different types of data
- Data summarization
- Frequency table
- Frequency Distributions
- Histogram
- Measures of central tendency and dispersion
- Skewness and kurtosis
- Basic Probability, Conditional Probability
- Normal Distribution
- Sampling methods
- Point and Interval estimation
- Central Limit Theorem
- Null and alternative hypothesis
- Level of significance
- P value
- Types of errors
- Hypothesis Testing
Linear and Multiple Linear Regression
- Simple and Multiple Linear Regression
- R2 and Adjusted R2
- ANOVA
- Interpretation of coefficients
- Dummy Variables
- Residual Analysis
- Outliers
Logistic Regression
- Assumptions
- Logistic Function
- Chi-Square
- Hosmer Lemeshow test
- Kolmogorov-Smirnov statistic and chart
- Classification Table
- Interpreting Coefficients
- Dependent Variable Prediction
- Principles of Forecasting
- Time Series
- Causal models
- Types of Forecasting Methods and their characteristics
- Moving Average
- Exponential Smoothing
- Trend
- Seasonality
- Cyclicity
- ARIMA
Classification
- Decision Tree Induction
- Bayes Methods
- Rule-Based Classification
- Model Evaluation and Selection
- Ensemble Approaches
- Random forest
Clustering
- Partitioning Methods
- Hierarchical Methods
- Density-Based Methods
- Grid-Based Methods
- Evaluation of Clustering
- K-means Method
Excel
- Formatting of Excel Sheets
- Use of Excel Formula Function
- Data Filter and Sort
- Charts and Graphs
- Table formula and Scenario building
- Lookups
- Pivot tables
Python & R
- Reading and Writing Data
- Data types
- Important Packages
- Data Manipulation
- Building models using learned algorithms
- Evaluating and optimizing models
Tableau – (Data Visualization tool)
- Extracting data into Tableau
- Data Preparation, Dimensions
- Transformation of variables
- Creating Views
- Working with charts
- Exporting visualisations
SQL – Introduction to Databases
- Terminologies – Records, Fields, Tables
- Introduction to database
- Introduction to SQL
- SQL Syntax
- SQL data Types
- SQL Operators
- Table creation in SQL- Create, Insert, Drop, Delete and Update
- Table access & Manipulation
- Select with Where Clause (In between, logical operators, wild cards, order, group by)
- Concepts of Join – Inner, Outer
- Projects let you apply what you’ve learned in Business Analytics course to a practical problem. In project, you will be given a problem statement and the relevant data. You will apply various algorithm techniques to find an optimum solution to the problem.
- Once you’ve completed the project, you’ll be better able to apply analytical techniques to a business case and accordingly prepare a detailed report.
- In case you don’t have any relevant experience in Analytics, this project will enable you to showcase your expertise in a job interview.
- Property Price Prediction using Linear Regression
- Bank Loan Prediction using Logistic Regression
- Wine buyer categorization using Clustering
- Forecasting Demand using Time Series
- Human Activity Recognition using Random Forest
- Predicting Potential Buyer using Decision Tree