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The syllabus
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Home
Henry Harvin
Courses
Post Graduate Program in Data Science
Post Graduate Program in Data Science
BY
Henry Harvin
Brochure
Enquire
Online
7 Months
Quick Facts
particular
details
Medium of instructions
English
Mode of learning
Self study, Virtual Classroom
Mode of Delivery
Video and Text Based
Course and certificate fees
certificate availability
Yes
certificate providing authority
Henry Harvin
The syllabus
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
Electives 1: Artificial Intelligence
Module 1: Neural Network
Module 2: Computer Vision
Module 3: Natural Language Programming (NLP)
Electives 2: Machine Learning
Electives 3: Deep Learning with KERA & Tensorflow
Complementary Module 1: Soft Skills Development
Business Communication
Preparation for the Interview
Presentation Skills
Complimentary Module 2: Resume Writing
Resume Writing
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