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    Diploma in Data Science

    BY
    Henry Harvin

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    Mode

    Online

    Duration

    288 Hours

    Fees

    ₹ 75000

    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
    ₹ 75,000
    certificate availability

    Yes

    certificate providing authority

    Henry Harvin

    The syllabus

    Live Course 1: Programming for Non Programmers

    • Course Introduction
    • Java Foundation
    • C++ Foundation
    • Jira

    Live Course 2: Statistics for Data Science

    • Lesson 1 - Introduction 
    • Lesson 2 - Sample or Population Data?
    • Lesson 3 - The Fundamentals of Descriptive Statistics
    • Lesson 4 - Measures of Central Tendency, Asymmetry, and Variability
    • Lesson 5 - Practical Example: Descriptive Statistics
    • Lesson 6 - Distributions
    • Lesson 7 - Estimators and Estimates
    • Lesson 8 - Confidence Intervals: Advanced Topics
    • Lesson 9 - Practical Example: Inferential Statistics
    • Lesson 10 - Hypothesis Testing: Introduction
    • Lesson 11 - Hypothesis Testing: Let’s Start Testing!
    • Lesson 12 - Practical Example: Hypothesis Testing
    • Lesson 13 - The Fundamentals of Regression Analysis
    • Lesson 14 - Subtleties of Regression Analysis
    • Lesson 15 - Assumptions for Linear Regression Analysis
    • Lesson 16 - Dealing with Categorical Data
    • Lesson 17 - Practical Example: Regression Analysis

    Live Course 3: Data Science with R

    • Lesson 1 - R Basics 
    • Lesson 2 - Data Structures in R
    • Lesson 3 - R Programming Fundamentals
    • Lesson 4 - Working with Data in R
    • Lesson 5 - Handling Data in R
    • Lesson 6 - Introduction to Business Analytics
    • Lesson 7 - Introduction to R Programming
    • Lesson 8- Data Structures
    • Lesson 9 - Data Management in R
    • Lesson 10 - Advanced Data Visualization
    • Lesson 11 - Descriptive Statistics in R
    • Lesson 12 - Regression Analysis
    • Lesson 13 - Decision Tree: Classification
    • Lesson 14 - Clustering: K-means and Hierarchical
    • Lesson 15 - Association Rule Analysis

    Course 4: Data Science with Python

    • Lesson 1 - Python Basics
    • Lesson 2 - Python Data Structures
    • Lesson 3 - Python Programming Fundamentals
    • Lesson 4 - Working with Data in Python
    • Lesson 5 - Working with NumPy Arrays
    • Lesson 6 - Data Science Overview
    • Lesson 7 - Data Analytics Overview
    • Lesson 8 - Statistical Analysis and Business Applications
    • Lesson 9 - Python Environment Setup and Essentials
    • Lesson 10 - Mathematical Computing with Python (NumPy)
    • Lesson 11 - Scientific Computing with Python (Scipy)
    • Lesson 12- Data Manipulation with Pandas
    • Lesson 13 - Data Visualization in Python using Matplotlib

    Course 5: Natural Language Processing

    • Lesson 1 - Introduction to Natural Language Processing
    • Lesson 2 - Feature Engineering on Text Data
    • Lesson 3 - Natural Language Understanding Techniques
    • Lesson 4 - Natural Language Generation
    • Lesson 5 - Natural Language Processing Libraries
    • Lesson 6 - Natural Language Processing with Machine Learning and Deep Learning
    • Lesson 7 - Speech Recognition Technique

    Course 6: Tableau

    • Lesson 1 - Getting Started with Data Visualization and Tableau
    • Lesson 2 - Working with Tableau
    • Lesson 3 - Working on Metadata and Data Blending
    • Lesson 4 - Deep Diving with Data and Connections
    • Lesson 5 - Creating Charts
    • Lesson 6 - Adding Calculations to your Workbook
    • Lesson 7 - Mapping Data in Tableau
    • Lesson 8 - Dashboards and Stories
    • Lesson 9 - Visualizations for an Audience
    • Lesson 10- Integration of Tableau with R and Hadoop

    Course 7: Simulated Data Science Projects

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

    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

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