DIIT EDUCOM

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Data Science

Master’s Program in Data Science And Artificial Intelligence

 

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.

 

  • Introduction of Microsoft Office
  • MS Word
  • MS Excel
  • MS Power Point
  • What is Digital Security
  • Authentication and Authorization
  • Importance of Digital Security
  • How to achieve Digital Security
  • Potential Threats in Digital Security
  • Introduction of Computer Hardware
  • Assembling a Computer
  • Disassembling a Computer
  • Basic Peripheral Devices
  • What is Operating System
  • Windows
  • Linux
  • Operating System Shell vs Kernel
  • Operating System Services
  • Introduction of Computer Networks
  • How Internet Works
  • Difference Between Website and Web Application
  • IP Address
  • What is a Domain Name
  • What is Client Server Architecture
  • Definition of Client, Server
  • Apache, nginx
  • Components of CS Architecture
  • What is a Protocol
  • HTTP vs HTTPS
  • What is Web Applications
  • Components of a Web Application
  • Web Application Life Cycle
  • Application Routing
  • Request and Response
  • HTTP Status Codes
  • Basics of Mathematics
  • Number Systems
  • Algorithm & Pseudocode
  • Graph
  • Tree
  • Sets
  • Functions
  • Linked Lists
  • Introduction to Data Science
  • Data Collection and Cleaning
  • Python Fundamentals
  • Control Flow & Functions
  • Array Computations
  • Data Manipulation
  • Visualizing Data
  • Web Scraping
  • Introduction to Statistical Analysis
  • Exploratory Data Analysis
  • Introduction to Probability
  • Probability Distribution Functions
  • Random Processes
  • Inferential Statistics
  • Introduction to Machine Learning
  • Supervised Learning - Regression
  • Mathematical and Bayesian Models
  • Natural Language Processing
  • Supervised Learning – Classification
  • Dimensionality Reduction
  • Unsupervised Learning Using Clustering
  • Association Rules Mining & Recommendation Engines
  • Time Series Analysis
  • Model Evaluation & Hyperparameter Tuning
  • Model Boosting & Optimization

 

MID-PROGREM PROJECT

 

  • Neural Networks with Tensor Flow 2.x
  • Deep Learning for Images using CNN
  • Deep Learning for Sequences using RNN
  • Building Games using RL
  • Data Warehousing
  • Data Mining
  • Data Integration and ETL
  • Mining Frequent Patterns
  • 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
  • Introduction to Data Visualization
  • Working with Data & Visualizations in Tableau
  • Advanced Visualizations
  • Sharing your Insights

 

Module 10 (Data Science Capstone Project)

 

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.

 

Main Tools & Languages You Will Learn

 

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  • 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
  • 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.
  • High demands of experts
  • Open opportunity to work with top executives
  • Attractive salary and perks
  • Increase business knowledge
  • Open opportunity to work with big brands
  • Key Factor in Decision making
  • Data Analytics used everywhere
  • Various Job opportunities
  • Flexibility of learning
  • Diverse working exposure

 

 

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