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- Henry Harvin
- Courses
- Post Graduate Program in Data Science
Online
12 Months
₹ 98,500
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
Fees information
₹ 98,500
certificate availability
Yes
certificate providing authority
Henry Harvin
The syllabus
Module 1: Programming for Non Programmers
Introduction to Programming
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