Advanced Certificate Program in Data Science & AI

Henry Harvin , IIT Guwahati (IITG)

Online

192 Hours

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course and certificate fees

certificate availability

Yes

certificate providing authority

Henry Harvin

The syllabus

Module 1: SQL

SQL Overview
SQL Manipulation
JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
String Functions
Mathematical Functions
Date-Time Functions
Hunting Tips

Module 2: Power BI

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

Module 3: Python Programming

Python Basics
Python Programming Fundamentals
Python Data Structures
Working with Data in Python
Working with NumPy Arrays

Module 4: 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)

Project Management Methodology

Module 6: Data Types and Data Processing

Nominal, Ordinal,Interval, Ratio, Data Cleaning techniques

Module 7: Statistics

Descriptive,Inferential, Hypothesis Testing

Module 8: EDA

Business moments, Graphical representation, Feature Engineering

Module 9: Mathematical Foundation

Optimization, Derivatives, Linear Algebra, Matrix Operations

Module 10: Clustering

Hierarchical Clustering, K Means Clustering

Module 11: Dimension reduction

PCA,SVD

Module 12: Association Rules

Market Basket Analysis, Association Rules Intuition, Association Rules Applications, Association Rules Terminology Association Rules Performance Measures

Module 13: Recommendation Engine

Intro to personalized strategy, similarity measures, user-based collaborative filtering, item-to-item collaborative filtering, recommendation engine vulnerabilities

Module 14: Text Mining and NL

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

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

Module 16: KNN

Nearest Neighbour Classifier, 1- Nearest Neighbour classifier, K- Nearest Neighbour Classifier, Controlling complexity in KNN, Euclidean Distance

Module 17: Decision Tree

What is a Decision Tree, Building a Decision Tree, Greedy Algorithm, Building the best Decision Tree, Attribute selection- Information gain

Module 18: Ensemble Techniques

Ensemble Primer, Voting, Stacking, Bagging, and Random Forest, Boosting Model

Module 19: Confidence Interval

Intro to Normal Distribution, Probability Calculation for normally distributed data, Normal QQ plot, Central Limit Theorem, Confidence Interval

Module 20: Hypothesis Testing

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

Simple Linear, Multiple Linear, Logistic Regression, Multinomial Regression, Ordinal Regression, Advance Regression

Module 22: SVM

SVM Hyperplanes, Best fit Hyperplane, Kernel Tricks, Multiclass Classification using SVM

Module 23: Survival Analytics

Intro to Survival Analytics, Applications, Time to event, Censoring, Kaplan Meier Survival Function

Module 24: Forecasting

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

Module 25: ANN

Neural Network Primer, Perceptron and Multi-Layered Perceptron Algorithm, Activation Function, Error Surface, Gradient Descent Algorithm

Module 26: CNN

Image Net Challenge, Parameters Explosion and MLP, Convolutional Networks, Convolutional Layers and Filters, Pooling Layer, Practical Issues, Adversaries

Module 27: RNN

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

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

Articles

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