Diploma in Data Science

BY
Henry Harvin

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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