Masters Program in Data Science

Why you should take Master’s Program in Data Science course?

• Digitalization across the domains is creating tons of data and the demand for the Data Science professionals who can evaluate and extract meaningful insights is increasing and creating millions of jobs in the space of Data Science. There is a huge void between the demand and supply and thereby creating ample job opportunities and salaries. Data Scientists are considered to be the highest in the job market.
• Data Scientist career path is long-lasting and rewarding as the data generation is increasing by leaps and bounds and the need for the Data Science professionals will increase perpetually.
• 1.4 Lakh jobs are vacant in Data Science, Artificial Intelligence and Big Data roles according to nasscom
• The world will notice a deficit of 2.3 Lakh Data Science professionals by 2021
• The Demand for Data Scientist professionals has increased by 417% in the year 2018, in India, as per the Talent Supply Index
• Data Science is the best job to pursue according to Glassdoor 2018 rankings
• Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century’
• The void between the demand and supply for the Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. Data Scientist career path is long and lucrative as the generation of online data is perpetual and growing in the future.

Who should take Master’s Program in Data Science course?

Professionals who can consider Data Science course as a next logical move to enhance in their careers include:

• Students from any stream
• Professional from any domain who has logical, and analytical skills
• Professionals working on Business intelligence, Data Warehousing, and reporting tools
• Statisticians, Economists, Mathematicians
• Software programmers
• Business analysts
• Six Sigma consultants
• Fresher from any stream with good Analytical and logical skills

Curriculum

• Module 1 - Data Science Project Lifecycle
• Module 2 - Introduction To Basic Statistics Using R And Python
• Module 3 - Probability And Hypothesis Testing
• Module 4 -Exploratory Data Analysis -1
• Module 5 - Linear Regression
• Module 6 - Logistic Regression
• Module 7 - Deployment
• Module 8 - Data Mining Unsupervised Clustering
• Module 9 - Dimension Reduction Techniques
• Module 10 - Association Rules
• Module 11 - Recommender System
• Module 12 - Introduction To Supervised Machine Learning
• Module 13 - Decision Tree
• Module 14 - Exploratory Data Analysis - 2
• Module 15: Feature Engineering
• Module 16: Model Validation Methods
• Module 17:Ensembled Techniques
• Module 18 - KNN And Support Vector Machines
• Module 19 - Regularization Techniques
• Module 20 - Neural Networks
• Module 21 - Text Mining
• Module 22 - Natural Language Processing
• Module 23 - Naive Bayes
• Module 24 - Forecasting
• Module 25 - Survival Analysis
• Module 26 - End To End Project Description With Deployment Assignments/Projects/Placement Support
• Module 27 - Assignments
• Module 28 - Projects
• Module 29 - Resume Prep And Interview Support Value added courses
• Module 30 - Basics Of Hadoop And Spark
• Module 31 - Basics Of R
• Module 32 - Basics Of Python
• Module 33 - Basics Of MYSQL
• Module 34 - Tableau
• Module 35 - Artificial Intelligence (AI)

Tools you will learn in Master’s Program in Data Science course:

• Python
• Shiny App and Python Flask for deployment
• Apache Spark
• Microsoft Azure
• AWS
• Statistical Analysis
• Machine Learning (Supervised)
• R Tool
• Data Visualization using Tableau
• MySQL
• Big Data Hadoop & SAS Base (self-paced learning)