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
• Date-Time Functions
• Hunting Tips

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 Modelling 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 Modelling & Visualization

• 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

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

In this module the candidate will learn about the concept of Project Management using CRISP ML(Q)

• Project Management Methodology

In this module the candidate will learn about the Data types and its processing.

• Nominal, Ordinal,Interval, Ratio, Data Cleaning techniques

In this module the candidate will learn about the statistics and its types.

• Descriptive,Inferential, Hypothesis Testing

In this module the candidate will learn about the EDA, Business moments and features about engineering.

• Business moments, Graphical representation, Feature Engineering

In this module the candidate will learn about the Mathematical foundation which includes understanding optimization, linear algebra etc. 

• Optimization, Derivatives, Linear Algebra, Matrix Operations

In this module the candidate will learn about the clustering,  its types and function.

• Hierarchical Clustering, K Means Clustering

In this module the candidate will learn about the concept of Dimension reduction.

• PCA,SVD

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

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

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

In this module the candidate will learn about the Probability concepts, Naive Bayes etc.

• Probability, Joint probability, conditional probability, Naive Bayes formula, Use case

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

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

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

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

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

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

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

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

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 Methods, Forecasting Errors, Seasonal Index

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

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

In this module the candidate will learn more about RNN, RNN Vs. Deep LSTM Etc.

• Traditional Language Models, Wny not MLP, Recurrent Neural Networks,RNN types, CNN+RNN, Bidirectional RNN, Deep Bidirectional RNN, RNN vs LSTM, Deep RNN vs Deep LSTM's

• Business communication
• Preparation for the Interview 
• Presentation Skills

Resume Writing