Diploma in Data Science

Programming is an increasingly important skill. This course will establish your proficiency in handling basic programming concepts. This program will help you to gain basic programming concepts like data types, variables, strings, loops, functions, and software engineering concepts like multithreading and multitasking.

Duration: 8 hours

Key Learning Objectives

- Achieve fundamental programming knowledge

Understanding the basics of data structures, data types, variables, C++, and JAVA

Course Curriculum

- Course Introduction

- Java Foundation

- C++ Foundation

- Jira 

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, and distribution. It will enable you to make data-driven predictions through statistics and the essential applications of it.

Duration: 8 hours

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

Course curriculum 

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

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.

Duration: 32 Hours

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 the 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

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

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

Duration: 40 hours

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 - 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

This Natural Language Processing course will give you a comprehensive detail of the science behind applying Machine Learning algorithms to process large amounts of natural language data. Learn the concepts of statistical machine translation and neural models, deep semantic similarity model (DSSM), neural knowledge base embedding, deep reinforcement learning technique, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK).

Duration: 16 hours

Key Learning Objectives

- Apply Deep Learning models to solve machine translation and conversation problems

- Implement deep structured semantic models (DSSM) to retrieve information

- Understand deep reinforcement learning techniques applied in Natural Language Processing

- Use neural models applied in image captioning and visual question answering

Course curriculum 

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

This Tableau training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. Tableau is an essential asset to those wishing to succeed in Data Science.

Duration: 32 hours

Key Learning Objectives 

- Learn the concepts of Tableau, become proficient with statistics, and build interactive dashboards

- Master data sources and datable blending, create data extracts, and organize and format data

- Master arithmetic, logic, table and LOD calculations, and ad-hoc analytics

- Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto, Gantt chart, and market basket analysis

- Learn to analyze data using Tableau Desktop as well as clustering and forecasting techniques

- Gain command of mapping concepts such as custom geocoding and radial selections

- Master Special Field Types and Tableau Generated Fields and the process of creating and using parameters

- Learn how to build interactive dashboards, and story interfaces and how to share your work

Course Curriculum

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

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

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

The Machine Learning course will make you a Master in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will learn concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

Key Learning Objectives

- Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling

- Acquire practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four

major end-to-end projects and 25+ hands-on exercises

- Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning

- Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-n

means clustering, and more in Python

- Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which

include Boosting & Bagging techniques

- Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

- Gain expertise in Machine Learning using the Scikit-Learn package

- Use the Scikit-Learn package for natural language processing

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.