Module 1: SQL
In this module the candidate will learn about concepts of SQL, which includes understanding JOIN, String functions etc.
- SQL Overview
- SQL Manipulation
- JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
- String Functions
- Mathematical Functions
- Data-Time Functions
- Hunting Tips
Module 2: Power BI
In this module the candidate will learn about Power BI Concepts such as Power Query, Data Modelling, Reporting etc.
- Business Intelligence (BI) Concepts
- Microsoft Power BI (MSPBI) Introduction
- Connecting Power BI with Different Data Sources
- Power Query for Data Transformation
- Data Modeling in Power BI
- Reports in Power BI
- Reports & Visualization Types in Power BI
- Dashboards in Power BI
- Data Refresh in Power BI
- End to End Data Modeling & Visualization
Module 3: Python Programming
In this module the candidate will learn about the major concepts of Python Programming
- Python basics
- Python programming fundamentals
- Python Data Structures
- Working with Data in Python
- Working with NumPy Arrays
Module 4: R Programming
In this module the candidate will learn about the major concepts of R programming
- R Basics
- R Programming Fundamentals
- Data Structures in R
- Working with Data in R
- Handling Data in R
Module 5: CRISP ML(Q)
In this module the candidate will learn about the concept of Project Management using CRISP ML(Q)
- Project Management Methodology
Module 6: Data Types and Data Processing
In this module the candidate will learn about the Data types and its processing.
- Nominal, Ordinal,Interval, Ratio, Data Cleaning techniques
Module 7: Statistics
In this module the candidate will learn about the statistics and its types.
- Descriptive,Inferential, Hypothesis Testing
Module 8: EDA
In this module the candidate will learn about the EDA, Business moments and features about engineering.
- Business moments, Graphical representation, Feature Engineering
Module 9: Mathematical Foundation
In this module the candidate will learn about the Mathematical foundation which includes understanding optimization, linear algebra etc.
- Optimization, Derivatives, Linear Algebra, Matrix Operations
Module 10: Clustering
In this module the candidate will learn about the clustering, its types and function.
- Hierarchical Clustering, K Means Clustering
Module 11: Dimension reduction
In this module the candidate will learn about the concept of Dimension reduction.
- PCA,SVD
Module 12: Association Rules
In this module the candidate will learn about the Association rules, terminologies used, performance measures and many more.
- Market Basket Analysis, Association Rules Intuition, Association Rules Applications, Association Rules Terminology Association Rules Performance Measures
Module 13: Recommendation Engine
In this module the candidate will learn about the Recommendation engine and its functions.
- Intro to personalized strategy, similarity measures, user-based collaborative filtering, item-to-item collaborative filtering, recommendation engine vulnerabilities
Module 14: Text Mining and NL
In this module the candidate will learn about the Text Mining, importance, terminology, BOW, DTM, TDM Etc.
- Text Mining Importance, BOW, Terminology and Preprocessing, Textual Data cleaning, DTM and TDM, Corpus level, positive and negative word clouds, social media web scraping
Module 15: Naive Bayes
In this module the candidate will learn about the Probability concepts, Naive Bayes etc.
- Probability, Joint probability, conditional probability, Naive Bayes formula, Use case
Module 16: KNN
In this module the candidate will learn about the KNN.
- Nearest Neighbour Classifier, 1- Nearest Neighbour classifier, K- Nearest Neighbour Classifier, Controlling complexity in KNN, Euclidean Distance
Module 17: Decision Tree
In this module the candidate will learn about the Decision tree, how to build it, Attribute selection, etc.
- What is a Decision Tree, Building a Decision Tree, Greedy Algorithm, Building the best Decision Tree, Attribute selection- Information gain
Module 18: Ensemble Techniques
In this module the candidate will learn about the Ensemble Techniques, Ensemble Primer, voting, stacking, bagging etc.
- Ensemble Primer, Voting, Stacking, Bagging, and Random Forest, Boosting Models
Module 19: Confidence Interval
In this module the candidate will learn about the Confidence Interval. Normal Distribution, Central Limit Theorem etc.
- Intro to Normal Distribution, Probability Calculation for normally distributed data, Normal QQ plot, Central Limit Theorem, Confidence Interval
Module 20: Hypothesis Testing
In this module the candidate will learn about the Hypothesis testing and different types of tests.
- Hypothesis Testing, Flowchart- Y is continuous, 2 sample T-Test, One Way ANOVA, Flowchart- Y is discrete, 2 proportion Test, Chi-Square Test
Module 21: Regression Techniques
In this module the candidate will learn about the Regression, its types- Simple, multiple etc.
- Simple Linear, Multiple Linear, Logistic Regression, Multinomial Regression, Ordinal Regression, Advance Regression
Module 22: SVM
In this module the candidate will learn about the SVM, Best fit, kernel tricks and many more.
- SVM Hyperplanes, Best fit Hyperplane, Kernel Tricks, Multiclass Classification using SVM
Module 23: Survival Analytics
In this module the candidate will learn about Survival Analytics, its applications and function.
- Intro to Survival Analytics, Applications, Time to event, Censoring, Kaplan Meier Survival Function
Module 24: Forecasting
In this module the candidate will learn about Forecasting, times series, errors in forecasting, methods of forecasting etc.
- TimeSeries vs Cross-Sectional Data, Time Series Dataset, Forecasting Strategy, Time Series Components, Time Series Visualizations, Time Series Partition, Forecasting Methoods, Forecasting Errors, Seasonal Index
Module 25: ANN
In this module the candidate will learn about ANN, Perceptron functions, Error surface, Activation function etc.
- Neural Network Primer, Perceptron and Multi-Layered Perceptron Algorithm, Activation Function, Error Surface, Gradient Descent Algorithm
Module 26: CNN
In this module the candidate will learn about CNN, Net challenges, MLP Filters and more.
- Image Net Challenge, Parameters Explosion and MLP, Convolutional Networks, Convolutional Layers and Filters, Pooling Layer, Practical Issues, Adversaries
Module 27: RNN
In this module the candidate will learn more about RNN, RNN Vs. Deep LSTM Etc.
- Traditional Language Models, Why not MLP, Recurrent Neural Networks,RNN types, CNN+RNN, Bidirectional RNN, Deep Bidirectional RNN, RNN vs LSTM, Deep RNN vs Deep LSTM's
Module 28: Complimentary Module 1: Soft Skills Development
- Business communication
- Preparation for the Interview
- Presentation Skills
Module 29: Complimentary Module 2: Resume Writing
- Resume Writing

Checkout Learner's Experiences on LinkedIn

Pallamreddy
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Ashwani
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Upender
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RAMA SRIVIDYA
★ ★ ★ ★ ★

SUBHASH CHAND
★ ★ ★ ★ ★

AUROBINDA
★ ★ ★ ★ ★

KALLOL
★ ★ ★ ★ ★