Data Science
Master’s Program in Data Science and Artificial Intelligence
Bachelor Graduate Program in Data Science (BGP-DS) 24+12 Months|Physical Interactive Learning | Live Projects & Case Studies | Career Assistance A comprehensive program in Data Science teaches by most influential industry leaders and world-class faculty.
Module 1 (Basics Fundamentals)
Microsoft Office
- Introduction of Microsoft Office
- MS Word
- MS Excel
- MS Power Point
DSU (Digital Secure User)
- What is Digital Security
- Authentication and Authorization
- Importance of Digital Security
- How to achieve Digital Security
- Potential Threats in Digital Security
Computer Assembling and Installation
- Introduction of Computer Hardware
- Assembling a Computer
- Disassembling a Computer
- Basic Peripheral Devices
Fundamentals of Operating System
- What is Operating System
- Windows
- Linux
- Operating System Shell vs Kernel
- Operating System Services
Computer Networking Basics
- Introduction of Computer Networks
- How Internet Works
- Difference Between Website and Web Application
- IP Address
- DNS Meaning
- What is a Domain Name
Client Server Architecture
- What is Client Server Architecture
- Definition of Client, Server
- Apache, nginx
- Components of CS Architecture
- What is a Protocol
- HTTP vs HTTPS
Web Application Fundamentals
- What is Web Applications
- Components of a Web Application
- Web Application Life Cycle
- Application Routing
- Request and Response
- HTTP Status Codes
Basic Maths & Data Structure
- Basics of Mathematics
- Number Systems
- Algorithm & Pseudocode
- Graph
- Tree
- Sets
- Functions
- Linked Lists
Module 2
(Data Science using Python)
- Introduction to Data Science
- Data Collection and Cleaning
- Python Fundamentals
- Control Flow & Functions
- Array Computations
- Data Manipulation
- Visualizing Data
- Web Scraping
Module 3
(Statistical Foundations)
- Introduction to Statistical Analysis
- Exploratory Data Analysis
- Introduction to Probability
- Probability Distribution Functions
- Random Processes
- Inferential Statistics
Module 4
(Machine Learning and NLP)
- Introduction to Machine Learning
- Supervised Learning - Regression
- Mathematical and Bayesian Models
- Natural Language Processing
- Supervised Learning – Classification
Module 5
(Advanced Machine Learning)
- Dimensionality Reduction
- Unsupervised Learning Using Clustering
- Association Rules Mining & Recommendation Engines
- Time Series Analysis
- Model Evaluation & Hyperparameter Tuning
- Model Boosting & Optimization
Module 6
(AI and Deep Learning)
- Neural Networks with Tensor Flow 2.x
- Deep Learning for Images using CNN
- Deep Learning for Sequences using RNN
- Building Games using RL
Module 7
(Data Mining and Warehousing )
- Data Warehousing
- Data Mining
- Data Integration and ETL
- Mining Frequent Patterns
Module 8
(Big Data Storage and Analytics)
- Introduction to Big Data and Big Data Mining
- Big Data with Hadoop
- Apache HBase and Hive
- Data Ingestion
- Apache Spark
- Big Data Analytics
- In-Class Project
Module 9
(Data Visualization)
- Introduction to Data Visualization
- Working with Data & Visualizations in Tableau
- Advanced Visualizations
- Sharing your Insights
Module 10
(Data Science Capstone Project)
INTERSHIP
- An industry-level project will be a part of your Post-Graduate Certification to consolidate your Learning. This industrial project will ensure that you have accumulated the real-world Experience to start your career as a globally recognized Data Scientist.
- Working with Data & Visualizations in Tableau
- Advanced Visualizations
- Sharing your Insights
For whom
- Working professionals in IT / Analytics / Statistics / Big Data / Machine Learning Fresh graduates from Engineering / Mathematics / IT backgrounds
- Professionals looking to develop skills to do statistical analysis to support decision making
- Final year students completing their graduation on or before December 2020
Eligibility
- 10+2 (PCM) BE / B.Tech / BCA / MCA / B.Sc. (Maths) / M.Sc (Maths) with a minimum of 50% aggregate marks is compulsory.
- Candidates with Mathematics, Statistics background will be given preference.
- A minimum of two years of full-time work experience after graduation or post-graduation is required.
Reason
Top 10 Reason chose Data Science
- 1- High Demand for Data Science Experts. ...
- 2- Good Work-Life Balance. ...
- 3- Opportunity to Work with Top Executives. ...
- 4- A Chance to Empower Management & Company Decision Makers. ...
- 5- Handsome Salary and Perks. ...
- 6- A Good Place to Gain Business Knowledge.
- 7- Leads to a Mindset of Lifelong Learning
- 8- Low Competition for Available Positions
- 9- An Opportunity to Work with Big Brands
- 10- Data Science Is Everywhere
