Post Graduate Program in Data Science Course

Name: Introduction To Post Graduate Program in Data Science Course | Henry Harvin
Uploaded: 2021-02-09 08:32:11
Description: Expedite your Career with Prestigious Post Graduate Program in Data Science | Learn Data Science with India’s #1 course and Get Ahead in Your Career | Learn Extensive use of Tools & Technologies and Analytics Techniques

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 their 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 of 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 clustering, its types, and functions.
- 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 Text Mining, its 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 Hypothesis testing and different types of tests.
- Hypothesis Testing, Flowchart- Y is continuous
- 2 sample T-Test
- One Way ANOVA
- Flowchart- Y is discrete
- proportion Test, Chi-Square Test
Module 21: Regression Techniques
In this module, the candidate will learn about Regression, and 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 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 its 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 Methods
- 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
- Wny not MLP
- Recurrent Neural Networks
- RNN types, CNN+RNN
- Bidirectional RNN
- Deep Bidirectional RNN
- RNN vs LSTM
- Deep RNN vs Deep LSTM's
Complimentary Module 1: Soft Skills Development
- Business communication
- Preparation for the Interview
- Presentation Skills
Complimentary Module 2: Resume Writing
- Resume Writing

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