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    Diploma in Data Science

    BY
    Henry Harvin

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    Mode

    Online

    Duration

    288 Hours

    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

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

    Module 2: Statistics for Data Science

    • Introduction 
    • Sample or Population Data?
    • The Fundamentals of Descriptive Statistics
    • Measures of Central Tendency, Asymmetry, and Variability
    • Practical Example: Descriptive Statistics
    • Distributions
    • Estimators and Estimates
    • Confidence Intervals: Advanced Topics
    • Practical Example: Inferential Statistics
    • Hypothesis Testing: Introduction
    • Hypothesis Testing: Let’s Start Testing!
    • Practical Example: Hypothesis Testing
    • The Fundamentals of Regression Analysis
    • Subtleties of Regression Analysis
    • Assumptions for Linear Regression Analysis
    • Dealing with Categorical Data
    • Practical Example: Regression Analysis

    Module 3: Data Science with R

    • R Basics 
    • Data Structures in R
    • R Programming Fundamentals
    • Working with Data in R
    • Handling Data in 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

    • Python Basics
    • Python Data Structures
    • Python Programming Fundamentals
    • Working with Data in Python
    • Working with NumPy Arrays
    • Data Science Overview
    • Data Analytics Overview
    • Statistical Analysis and Business Applications
    • Python Environment Setup and Essentials
    • Mathematical Computing with Python (NumPy)
    • Scientific Computing with Python (Scipy)
    • Data Manipulation with Pandas
    • Data Visualization in Python using Matplotlib

    Module 5: Natural Language Processing

    • Introduction to Natural Language Processing
    • Feature Engineering on Text Data
    • Natural Language Understanding Techniques
    • Natural Language Generation
    • Natural Language Processing Libraries
    • Natural Language Processing with Machine Learning and Deep Learning
    • Speech Recognition Technique

    Module 6: Tableau

    • Getting Started with Data Visualization and Tableau
    • Working with Tableau
    • Working on Metadata and Data Blending
    • Deep Diving with Data and Connections
    • Creating Charts
    • Adding Calculations to your Workbook
    • Mapping Data in Tableau
    • Dashboards and Stories
    • Visualizations for an Audience
    • Integration of Tableau with R and Hadoop

    Module 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

    • Neural Network
    • Computer Vision
    • Natural Language Programming (NLP)

    Electives 2: Machine Learning

    Electives 3: Deep Learning with KERA & Tensorflow

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