Post Graduate Data Analyst Program

The next step to becoming a data scientist is learning R—the most in-demand open source technology. R is the most powerful Data Science and analytics language, which has a steep learning curve and vigorous community. Data Science with R is becoming the technology of choice for organizations that are adopting the power of analytics for competitive expedience.

Key Learning Objectives 

- Gain a substantial understanding of business analytics

- Install R, R-studio, and workspace setup, and learn about the various R packages

- Master R programming and understand how various statements are executed in R

- Gain an in-depth understanding of data structure used in R and learn to import/export data in R

- Define, understand and use the various apply functions and dplyr functions

- Understand and use the various graphics in R for data visualization

- Gain a basic understanding of various statistical concepts

- Understand and use hypothesis testing method to drive business decisions

- Understand and use linear, non-linear regression models, and classification techniques for data analysis

- Learn and use the various association rules and Apriori algorithm Learn and use clustering methods including K-means, DBSCAN, and hierarchical

clustering

Course curriculum 

Module 1 - Introduction to Business Analytics

Module 2 - Introduction to R Programming

Module 3 - Data Structures

Module 4 - Data Management in R

Module 5 - Advanced Data Visualization

Module 6 - Descriptive Statistics in R

Module 7 - Regression Analysis

Module 8 - Decision Tree: Classification

Module 9 - Clustering: K-means and Hierarchical

Module 10 - Association Rule Analysis

This Data Science with Python course will set up your mastery of Data Science and analytics techniques using Python. In this Python for Data Science course, you will learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing

Key Learning Objectives 

- Learn an in-depth understanding of Data Science processes, data wrangling, data exploration, data

   visualization, hypothesis building, and testing Install the required   

- Understand Python environment and other auxiliary tools and libraries

-  Understand the essential concepts of Python programming such as data types, tuples, lists, dicts,

   basic operators, and functions   

- Perform high-level mathematical computing using the NumPy package and its vast library of

    mathematical functions 

- Carry out scientific and technical computing using the SciPy package and its sub-packages such  

    as Integrate, Optimize, Statistics, IO, and Weave      

-   Carry out data analysis and manipulation using data structures and tools provided in the Pandas

    package 

-   Gain an in-depth understanding of supervised learning and unsupervised learning models such as  

   linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline

-   Use the Matplotlib library of Python for data visualization

-   Extract useful data from websites by performing web scraping using Python

Course curriculum 

Lesson 1 - Data Science Overview

Lesson 2 - Data Analytics Overview

Lesson 3 - Statistical Analysis and Business Applications

Lesson 4 - Python Environment Setup and Essentials

Lesson 5 - Mathematical Computing with Python (NumPy)

Lesson 6 - Scientific Computing with Python (Scipy)

Lesson 7 - Data Manipulation with Pandas

Lesson 8 - Data Visualization in Python using Matplotlib

Gain Substantial Knowledge into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming is a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write R code, learn about R’s data structures, and create your own functions using R. On completion of this course, you will be able to handle and create data analysis

Key Learning Objectives

- Learn about math, variables, and strings, vectors, factors, and vector operations

- Gain fundamental knowledge on arrays and matrices, lists, and data frames

- Get an understanding of conditions and loops, functions in R, objects, classes, and debugging

- Learn how to accurately read the text, CSV, and Excel files. Learn how to write and save data objects in R to a file

- Understand the working of strings and dates in R

Course curriculum 

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

Get ahead with your learning of Python for Data Science with this introductory course and familiarize yourself with programming. Upon completion of this course, you will be able to write your Python scripts, perform fundamental hands-on data analysis using the Spyder/Jupyterbased lab environment.

Key Learning Objectives 

- Start creating the First Python program by implementing concepts of variables, strings, functions, loops, conditions

- Understand the modulation of lists, sets, dictionaries, conditions and branching, objects and classes

- Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas

Course curriculum 

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

The Business Intelligence course curriculum is composed of modules which impart the essential knowledge along with developing interest and curiosity. You will earn a Henry Harvin certificate when you have put your learnings to the test and come out a winner. Once you have completed all requirements about the Business Intelligence course, our trainers would be honored to hand over the Course completion certificate to you, in person!

Learning Objectives

• Master Business Intelligence using Power BI
• Master Business Intelligence using Power BI
• Create Powerful Storylines Presentation to Executives
• Develop Intrinsic Understanding of How Table Calculations Work
• Create Effective Presentations using Stories
• Easily Implement Advanced Mapping Techniques
• Understand Data/KPIs and Importance of Business Intelligence
• Create Highly Interactive Dashboards
• Easily Create Charts of any Type

Course Curriculum

• 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 Modelling 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 Modelling & Visualization

   

The tableau course will help the candidate master the data visualization using tableau and create Powerful storylines presentations to executives. Learn to develop an intrinsic understanding of table calculations work. Understand Data/KPIs and the Importance of Data Visualization and Create Highly Interactive Dashboards. Work on Real Life Business Problems Proficiently

Learning Benefits

• Master Data Visualization using Tableau
• Create Powerful Storylines Presentation to Executives
• Develop Intrinsic Understanding of How Table Calculations Work
• Create Effective Presentations using Stories
• Easily Implement Advanced Mapping Techniques
• Understand Data/KPIs and the Importance of Data Visualization
• Create Highly Interactive Dashboards
• Easily Create Charts of any Type
• Connect Tableau to other Sources effortlessly
• Work on Real Life Business Problems Proficiently

Course Curriculum

• Module 1: Introduction to Data Visualization and Power of Tableau
• Module 2: Architecture of Tableau
• Module 3: Working with Metadata and Data Blending
• Module 4: Creation of Sets
• Module 5: Working with Filters
• Module 6: Organizing Data and Visual Analytics
• Module 7: Working with Mapping Preview
• Module 8: Working with Calculations and Expressions
• Module 9: Working with Parameters Preview
• Module 10: Charts and Graphs
• Module 11: Dashboards and Stories
• Module 12: Tableau Prep
• Module 13: Integration of Tableau with R and Hadoop

SQL is the Leading Programming Language for relational databases. Manage and Code relational databases and database-driven applications. Create MySql database and database design. Master the Core Concepts. American National Standard Institute considers SQL as the most standard database

Learning Objectives

• Learn about SQL and its Installation.
• Gain knowledge about Relational Databases, SQL Commands, and Statements.
• Be Aware of JOIN; Inner, Left, Right, Full Outer, and Cross JOIN.
• Learn about String Functions of SQL
• Develop an understanding of Mathematical Functions of SQL
• Understand Data- Time functions of SQL, inclusive of Date and Time and Age functions

Course Curriculum

• 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

No. 1 Ranked Advanced Excel Course in India. Learn to apply Advanced Formulas, Perform Data analysis & Data Visualization, and create Pivot tables & dashboards. This course will provide comprehensive knowledge of MS Excel and its function. The learner will be equipped with the knowledge about Power query and user-defined functions with skills in Advanced VBA

Key Learning Objectives

• Gain basic knowledge of MS Excel
• Work flawlessly with the Basic Excel Function
• Understand the Advanced Excel Functions and work with it
• Get introduced to Lookup Functions
• Learn the essential functions of Excel and the Pivot Table
• Know the basics of VBA and its analysis
• Acquire knowledge about Power Query and User-Defined Functions
• Get Acquainted with the Basics of Advanced Excel
• Acquire Knowledge of Advanced VBA
• Learn to work with Advanced Excel Functions

Course Curriculum

• Module 1: Introduction to MS Excel
• Module 2 Working with Basic Excel Functions
• Module 3:Understanding Advanced Excel Functions
• Module 4: Introduction to Lookup Functions
• Module 5: Pivot Table and Essential Functions of Excel
• Module 6: Basic of VBA and What-If Analysis
• Module 7: Power Query and User-Defined Functions
• Module 8: Advanced Excel Functions
• Module 9: Advanced VBA
• Module 10: Advanced Excel

Capstone Projects are hands-on projects that let you apply what you’ve learned in a Specialization to a practical question or problem related to the Specialization topic. In this Section, you will be able to apply all the skills and knowledge acquired throughout the course.

Key Learning Objectives

• Learn about Data Processing and Data Cleaning: You will learn how to handle the data set and manage it with analytical tools
• Applying the correct Models for the projects: Applying all the techniques learnt by understanding the data
• Representing Results: You will be able to write report and represent the outcome of your analysis

Retail

Actionable insights for improving sales of a consumer durables retailer using POS data analytics

Techniques used: Market Basket Analysis, RFM (Recency, Frequency, Monetary) Analysis, Time Series Forecasting

E-commerce

Customer engagement and brand perception of Indian ecommerce and social media approach

Techniques used: Text Mining, K Means Clustering, Regression Trees, XGBoost, Neural Network

Web & Social Media

Trapping Social Media exchanges on Twitter

Banking

Customer engagement and brand perception of Indian e commerce- A social media approach

Techniques used: Linear Discriminant Analysis, Logistic Regression, Neural Network, Boosting, Random Forest, CART

Insurance

Personal insurance digital assistant

Techniques used: NLP (Natural Language Processing), Vector Space Model, Latent Semantic Analysis

Entrepreneurship /Startups

Start-up insights through data analysis

Techniques used: Univariate and Bivariate Analysis, Multinomial Logistic Regression, Random Forest

Finance & Accounts

Vendor invoicing grief project

Techniques used: Conditional Inference Tree, Logistic Regression, CART and Random Forest

Industry or ‘Smart Factory’ is moving towards its Mega Trends: “Automation”. The incorporation of Cyber-Physical System (CPS), IIoT, IoT, Cloud Computing, Robotic Process Automation (RPA), and Artificial Intelligence, helps in increasing Human productivity with Cost-Effectiveness. The Arena of Industry 4.0 subjugates through a Centralized Control over almost all Mega Trends in the Industry like Automation in Production, Home, Factories, Vehicles, and Smart Cities. Industry 4.0 enables us with Simulation Tools for live monitoring that defines the Industrial Revolution or Industry 4.0

Key Learning Objectives

• Be well Acquainted with RPA and CPS
• Be Familiar with Technologies linked with IIoT and IoT
• Be Aware of Cyber Security Systems in accordance with Smart Factory
• Learn and Practise Artificial Intelligence using Big Data Analysis
• Get an Insight of Industry 4.0 and be Part of Industrial Development Globally

Module 1: Neural Network

This module will equip the candidate with the knowledge of Nural Networks. Gain Comprehensive knowledge about the Activation Functions and feedforward neural network. Learn about backpropagation and gradient descent. Know about the full connected layer forward and backward pass. Get acquainted with Data Preprocessing, Data Augmentation, weight initialization, working with google collab, and more

Module 2: Computer Vision

This module will help the candidate to gain knowledge of Computer Vision. Learn to work with images. Gain knowledge about Convolutions 2D for images. Know about the CNN architectures. Get acquainted with the knowledge of Semantic segmentation using UNet, and more

Module 3: Natural Language Programming (NLP)

This module will equip the candidate with the knowledge of Natural Language Processing. Learn the Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF. Know about Word embedding, POS Tagging, LSTM application, Encoder-Decoder attention, and more

Statistics is the discipline of allocating a prospect through the classification, collection, and analysis of data. A substructure part of Data Science, this Course helps you in defining the statistical terms. The Course explains measures of central tendency and dispersion and comprehended skewness, correlation, regression, distribution. It will enable you to make data-driven predictions through statistics and essential application of it. Key Learning Objectives

• Learn the fundamentals of statistics
• Collaborate with different types of data
• How to organize different types of data
• Compute the measures of central tendency, asymmetry, and variability
• Evaluate correlation and covariance
• Distinguish different types of distribution and work on it
• Estimate confidence intervals
• Perform and Evaluate hypothesis testing
• Make data-driven decisions
• Know comprehensively the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables