## Predictive Analytics Course Details

The Predictive Analytics Program is a classroom friendly, 140+ hours program prepared by Industry experts for high-aspiring individuals who wish to master the tools of predictive analytics by giving a boost to their careers and reaching high in the corporate ladder. Get practical and experiential learning, along with soft skills and placement assistance.

The analytics market in India is growing at a fast pace, with more and more companies, start-ups offering analytics jobs and products that cater to various industries. India is witnessing a humongous increase in the revenue being generated across all industries.

Shunya training academy is a premier institute in Delhi-NCR with highly trained professionals that offer an extensive learning platform in predictive analytics to all the aspiring individuals who are eager to learn and make their mark.

## Course Curriculum

#### Introduction to MS Excel

Functionalities for a Data Scientist

#### Data Analysis in Excel

Basic Data Manipulation Functions- Mean, Maximum, Round, Sum etc., Statistical functions, Filter, sort, lookup,Using Pivots and Plotting in Excel – Usage of Visualization Capabilities.

#### Introduction to R

What is R? |What is Open Source? |Capabilities of R |GUI for R| R IDE – Rstudio |Using R

#### 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.

#### Analytics in R and Statistics

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 |Central Limit Theorem |Hypothesis Testing | Types of Hypothesis Testing |Introduction to ANOVA and Basics of Regression/Classification.

#### Linear Regression

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.

#### Logistics Regression

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.

#### Time Series Modeling

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.

#### Market Basket Analysis

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

#### Decision Trees

Type of Classification Algorithms |Fundamentals of Tree bases Systems | Concept of Impurity Measure | Building a Decision Tree Model|Prediction using Decision Trees |Over fitting and Cross Validation |Flavor of Advanced Concepts in Trees (Random Forests) |Decision Boundary of Tree based Algorithms |Types of Tree Algorithms.

#### Clustering

Unsupervised Algorithms and Introduction to Clustering | Example Problem (Some real world examples of Clustering Applications) |Assumptions for Clustering | Mechanics of Clustering| Creating Clusters |Understanding the Output |Advanced Clustering Concepts | Do it your self case.

#### Python for Analytics

Understanding Python | Categorization in Python | Visualization |Model Evaluation

#### 5 Projects

Linear + Logistics + Time Series + Market Basket Analysis + Decision Trees