Module 1: Programming for Non Programmers

Introduction to Programming

What is Programming?
Need to learn to program
Front-end vs Backend developer

Understanding Different Programming Tools

Which Programming language
High/low-level programming
Java, Python, Cpp, Ruby, C#
Machine Coding and JQuery
PHP vs Ruby vs Python

HTML and CSS (web)

Sublime
Introduction and Basics of HTML
Introduction and Basics of CSS
Basics of JavaScript

CPP

Introduction to C++
Installation and Basics of C++
Data Structures and Variables
Operators in C++
Loop and If-Else Statements
Pointers and their use

Module 2: Statistics for Data Science

Introduction to Statistics and Data Science

Introduction to Statistics
Introduction to Data Science

Fundamentals of Descriptive Statistics

Measures of Central Tendency, Asymmetry, and Variability
Practical Example: Descriptive Statistics
Distributions

Advanced Studies

Estimators and Estimates
Hypothesis Testing: Introduction
Practical Examples for Hypothesis Testing

Regression Analysis

Fundamentals of Regression Analysis
Assumptions of Regression Analysis
Dealing with Categorical Data
Practical Examples for Regression Analysis

Module 3: Data Science with R

Introduction to Business Analytics

Analytics Definition and Applications
Data Science, Data Mining, Statistics
Supervised vs Unsupervised Learning

Introduction to R Programming

About R: R Installation
Data Import and Export
Operators in R

Data Structures

Data Types
Data Structures

Data Management in R

Apply Family in R
Aggregate and Table Commands
Data Manipulation in R
Managing Missing values in R

Advanced Data Visualization

Introduction to basic graphs
Introduction to GGPlot Library
Plots: Scatter Plot, Histogram, Bar Plot, Box Plot, Heatmap, etc

Descriptive Statistics in R

Applying function of Statistics in R

Regression Analysis

Introduction to Regression analysis
Building models for analysis
Linear Regression
Multi-Linear Regression
Logistic Regression
Assumptions of the model

Decision Tree: Classification

Introduction to Decision Tree
Building models for analysis
CART Approach

Clustering: K-means and Hierarchical

Introduction to Cluster Analysis
Building models for analysis
K-means Clustering
Hierarchical Clustering

Association Rule Analysis

Introduction to Association Rule Analysis
Understanding requirements for ARA: Support, Confidence, and Lift
Building models for analysis

Module 4: Data Science with Python

Data Science Overview

Data Science, Data Mining, Statistics
Supervised vs Unsupervised Learning

Data Analytics and Business Application

Analytics Definition and Applications
Why Analytics and Roles (Application and Roles in various domains)
Tools and Techniques in Analytics

Python Environment Setup and Essentials

Anaconda - Download & Setup
IDEs - Jupyter, Spyder, PyCharm
Git - Setup and Configuration with IDEs
Creating and Managing Analytics/ ML Projects

Mathematical Computing with Python

Understanding NumPy Library
Managing and manipulating data

Scientific Computing with Python

Understanding SciPy Library
Managing and manipulating data

Data Manipulation with Pandas

Group Summaries
Crosstab, Pivot and Reshape data
Managing Missing Values
Outliers Detection
Managing indexes in pandas

Data Visualization in Python using Matplotlib

Selection of Graph
Libraries (matplotlib, seaborn, plotnine)
Basic Graphs (histogram, barplot, boxplot, pie, etc)
Managing plot parameters(size, title, axis, legend, etc)
Advanced Graphs (correlation, heatmap, mosaic, etc)
Exporting graphs

Module 5: Natural Language Processing

Introduction to NLP

Introduction to Natural Language Processing
Components of NLP
Applications of NLP
Challenges and scope
Data formats
Text Processing
Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases
Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop
Tweets Cleanup and Analysis Using Regular Expressions

Feature Engineering on Text Data

N-Gram
Bag of Words
Document Term Matrix
TF-IDF
Levenshtein Distance
Word Embedding(Word2Vec)
Doc2vec
PCA
Word Analogies
Dense Encoding
Topic Modelling
Assisted Practice: Word2vec Model Creation
Assisted Practice: Word Analogies Demo
Assisted Practice: Identify Topics from News Items
Build Your Own News Search Engine

Natural Language Understanding Techniques

Parts of Speech Tagging
Dependency Parsing
Constituency Parsing
Morphological Parsing
Named Entity Recognition
Coreference Resolution
Word Sense Disambiguation
Fuzzy Search
Document and Sentence Similarity
Document Indexing
Sentiment Analysis
Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed
Assisted Practice: Analyzing the Sentiments
Assisted Practice: Extract City and Person Name from Text
Identifying Top Product Feature from User Reviews

Natural Language Generation

Retrieval based model
Generative based model
AIML
Language Modelling
Sentence Correction
Assisted Practice: Create AIML Patterns for QnA on Mental Wellness
Assisted Practice: To Predict the Next Word in a Sentence
Create your Own Spell Checker

NLP Libraries

Spacy
NLTK
Gensim
TextBlob
Stanford NLP
LUIS
Assisted Practice: Simplilearn Review Analysis
Create your Own NLP Module

NLP with Machine Learning & Deep Learning

Neural Machine Translation
Introduction to RNN, LSTM
LSTM Forward Pass
LSTM Backprop through time
Applications of LSTM
Advanced LSTM Structures
Encoder Decoder Attention
Text Classification and Summarization
Document Clustering
Attention Mechanism
Question Answering Engine
Assisted Practice: Target Spam Words and Patterns
Assisted Practice: Summarization of News
Document Clustering for BBC News

Speech Recognition Techniques

Basic concepts for voice/sound
Sequential models
Creating speech model
Saving model
Implementation/use cases
Speech libraries
Assisted Practice: Translation from Speech to Text
Speech to Text: Extract Keywords from Audio Reviews

Module 6: Tableau

Introduction to Data Visualization and Power of Tableau

Comparison and benefits against reading raw numbers
Real use cases from various business domains
Some quick and powerful examples using Tableau without going into the technical details of Tableau
Installing Tableau
Tableau interface
Connecting to Data Source
Tableau data types
Data preparation

Architecture of Tableau

Installation of Tableau
Desktop Architecture of Tableau
Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
How to start with Tableau
The ways to share and export the work done in Tableau

Working with Metadata and Data Blending

Connection to Excel
Cubes and PDFs
Management of metadata and extracts
Data preparation
Joins (Left, Right, Inner, and Outer) and Union
Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.

Creation of Sets

Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
Constant sets
Computed sets, bins, etc.

Working with Filters

Filters (Addition and removal)
Filtering continuous dates, dimensions, and measures
Interactive Filters, marks card, and hierarchies
How to create folders in Tableau
Sorting in Tableau
Types of sorting
Filtering in Tableau
Types of filters
Filtering the order of operations

Organizing Data and Visual Analytics

Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
Formatting data using labels and tooltips
Edit axes and annotations
K-means cluster analysis
Trend and reference lines
Visual analytics in Tableau
Forecasting, confidence interval, reference lines, and bands

Working with Mapping Preview

Working on coordinate points
Plotting longitude and latitude
Editing unrecognized locations
Customizing geocoding, polygon maps, WMS: web mapping services
Working on the background image, including add image
Plotting points on images and generating coordinates from them
Map visualization, custom territories, map box, WMS map
How to create map projects in Tableau
Creating dual axes maps and editing locations

Working with Calculations and Expressions

Calculation syntax and functions in Tableau
Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
LOD expressions, including concept and syntax
Aggregation and replication with LOD expressions
Nested LOD expressions
Levels of details: fixed level, lower level, and higher level
Quick table calculations
The creation of calculated fields
Predefined calculations
How to validate

Working with Parameters Preview

Creating parameters
Parameters in calculations
Using parameters with filters
Column selection parameters
Chart selection parameters
How to use parameters in the filter session
How to use parameters in calculated fields
How to use parameters in the reference line

Charts and Graphs

Dual axes graphs
Histograms
Single and dual axes
Box plot
Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
Maps: tree and heat maps
Market basket analysis (MBA)
Using Show me
Text table and highlighted table

Dashboards and Stories

Building and formatting a dashboard using size, objects, views, filters, and legends
Best practices for making creative as well as interactive dashboards using the actions
Creating stories, including the intro of story points
Creating as well as updating the story points
Adding catchy visuals in stories
Adding annotations with descriptions; dashboards and stories
What is a dashboard?
Highlight actions, URL actions, and filter actions
Selecting and clearing values
Best practices to create dashboards
Dashboard examples; using Tableau workspace and Tableau interface
Learning about Tableau joins
Types of joins
Tableau field types
Saving as well as publishing data source
Live vs extract connection
Various file types

Tableau Prep

Introduction to Tableau Prep
How Tableau Prep helps quickly combine join, shape, and clean data for analysis
Creation of smart examples with Tableau Prep
Getting deeper insights into the data with great visual experience
Making data preparation simpler and accessible
Integrating Tableau Prep with Tableau analytical workflow
Understanding the seamless process from data preparation to analysis with Tableau Prep

Integration of Tableau with R & Hadoop

Introduction to R language
Applications and use cases of R
Deploying R on the Tableau platform
Learning R functions in Tableau
The integration of Tableau with Hadoop

Course 7: Power BI

Module 1: Business Intelligence (BI) Concepts
Module 2: Microsoft Power BI (MSPBI) Introduction
Module 3: Connecting Power BI with Different Data Sources
Module 4: Power Query for Data Transformation
Module 5: Data Modeling in Power BI
Module 6: Reports in Power BI
Module 7: Reports & Visualization Types in Power BI
Module 8: Dashboards in Power BI
Module 9: Data Refresh in Power BI
Module 10: Projects — End to End Data Modeling & Visualization

Course 8: SQL Developer

Module 1: SQL Overview
Module 2: SQL Manipulation
Module 3: JOIN
Module 4: String Functions
Module 5: Mathematical Functions
Module 6: Data-Time Functions
Module 7: Tuning Tips

Course 9: Simulated Data Science Projects

Retail
E-commerce
Web & Social Media
Banking
Supply Chain
Healthcare
Insurance
Entrepreneurship /Start-Ups
Finance & Accounts

Course 10: Projects — End to End Data Modelling & Visualization

Project 1
Project 2

Course 11: Projects Covered

HR: Analyze the Attrition rate of Employees
Sales: Predicting Department wise Sales
Multi-Domain: Business Analytics Optimization
Marketing: Website Trend Analysis
Financial Analysis: Stock Market Prediction
Finance: Analyze ETF Trends

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Post Graduate Program in Data Science

Online

7 Months

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Course and certificate fees

certificate availability

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The syllabus

Module 1: Programming for Non Programmers

Introduction to Programming

Understanding Different Programming Tools

HTML and CSS (web)

CPP

Module 2: Statistics for Data Science

Introduction to Statistics and Data Science

Fundamentals of Descriptive Statistics

Advanced Studies

Regression Analysis

Module 3: Data Science with R

Introduction to Business Analytics

Introduction to R Programming

Data Structures

Data Management in R

Advanced Data Visualization

Descriptive Statistics in R

Regression Analysis

Decision Tree: Classification

Clustering: K-means and Hierarchical

Association Rule Analysis

Module 4: Data Science with Python

Data Science Overview

Data Analytics and Business Application

Python Environment Setup and Essentials

Mathematical Computing with Python

Scientific Computing with Python

Data Manipulation with Pandas

Data Visualization in Python using Matplotlib

Module 5: Natural Language Processing

Introduction to NLP

Feature Engineering on Text Data

Natural Language Understanding Techniques

Natural Language Generation

NLP Libraries

NLP with Machine Learning & Deep Learning

Speech Recognition Techniques

Module 6: Tableau

Introduction to Data Visualization and Power of Tableau

Architecture of Tableau

Working with Metadata and Data Blending

Creation of Sets

Working with Filters

Organizing Data and Visual Analytics

Working with Mapping Preview

Working with Calculations and Expressions

Working with Parameters Preview

Charts and Graphs

Dashboards and Stories

Tableau Prep

Integration of Tableau with R & Hadoop

Course 7: Power BI

Course 8: SQL Developer

Course 9: Simulated Data Science Projects

Course 10: Projects — End to End Data Modelling & Visualization

Course 11: Projects Covered

Electives 1: Artificial Intelligence

Electives 2: Machine Learning

Electives 3: Deep Learning with KERA & Tensorflow

Complementary Module 1: Soft Skills Development

Complimentary Module 2: Resume Writing

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