Certificate Program in Data Science, Machine Learning & AI

IANT is World’s No. 1 IT Training & Certification Institute, training students in Infrastructure Management Services (IMS), Software programming, Cyber security, and Disruptive technologies. IANT is also the biggest TechEd Company in the EdTech space.

  • Classroom

  • LMS

  • Self

  • Hybrid

  • Job

Certificate Program in Data Science, Machine Learning & AI is a job-ready career course, designed to transform the careers of 12th, Undergraduates, Graduates (Arts, Commerce), Diploma holders, & Engineering students into globally certified Data Science Specialists. This 360-degree training module starts with Data Science basics and Python Programming. Statistics and Advanced Excel has been added to the course so that the students get the best out of this course. R programming, Data Science with R Programming, Data Science with AI & ML and case studies takes this course to the specialist level.

Introduction to Data and Data Analysis

  • Data analysis terminology
  • Basics of Data Analysis using Excel
  • Complex functions in Excel
  • Pivot tables and charts in Excel
  • Insight generation and summary report preparation in Excel
  • Data Analytics and Its type
  • Data Science Tools and Applications

Python Basics

  • Python: An Introduction
  • Creating Your First Program in Python
  • Data Types: Classifying Data in Python
  • Implementing Input and Output Operations
  • Operators: Performing Logical and Mathematical Operations
  • Statements: Controlling the Flow of Program
  • Strings: A Sequence of Characters
  • Arrays: Arranging Similar Objects Systematically
  • Implementing Functions in Python
  • Lists and Tuples: Managing Data Systematically
  • Dictionaries and Other Data Structures
  • Recursion and Algorithms in Python
  • Handling Files I/O
  • Connecting to a Database in Python
  • Handling Errors and Exceptions
  • Regular Expressions
  • Handling Numbers, Dates and Time

Python Packages and Manipulating data in Python

  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
  • Installing Jupyter notebook for windows, Linux and mac.
  • Installing Numpy, pandas and matplotlib modules.
  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to CSV file
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

Statistics Basics :

  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
  • Probability
  • Types of Probability
  • Data deviation & distribution
  • Variance
  • Underfitting
  • Overfitting
  • Distance metrics
  • Outlier analysis
  • Missing Value treatment
  • Correlation

R Programming Basics:

  • Exploring R Language
  • Setting Up R Environment with RStudio
  • Implementing Expressions
  • Essentials Data Structure in R
  • Implementing Strings in R
  • Performing Statistics with R
  • Visualizing and Analyzing Data in R
  • Object-Oriented Programming in R
  • Implementing Data Interfaces in R
  • Errors Handling
  • Improving the Performance
  • Executing Your Own R Functions

Data Science with R Programming :

  • Introduction to Data Science and Analytics
  • Exploring Big Data and Types of Data
  • The Lifecycle of Data Science
  • Getting Started with R
  • Introduction to Statistics and Probability with R
  • Data Exploration and Data Wrangling
  • Data Visualization and Tools
  • Handling Real World Data
  • Ethics and Law in Data and Analytic
  • Introduction to Machine Learning
  • Linear Regression Techniques
  • Logistic Regression Techniques
  • Decision Trees
  • Time Series Analysis
  • Unsupervised Learning
  • Text Mining and Analytics
  • Exploring Mobile Analytics
  • Using No-SQL and Transact-SQL in Data Science
  • Exploring Data Science with Excel and Knime
  • Recommendation Engines
  • Big Data Frameworks (Hadoop/HIVE/MapReduce/Azure/ Machine Learning)
  • Machine Learning and Hadoop
  • Documentation and Deployment
  • Data Science Tools and Applications

Introduction to Artificial Intelligence and Machine Learning :

  • Fundamentals of AI
  • Problem-solving techniques used in AI
  • Knowledge engineering in AI
  • Using game theory in AI
  • Machine learning in business
  • Neural networks
  • Deep learning in AI
  • Natural language processing (NLP)
  • Influence of AI on social media and GIS
  • Ai in sentiment analysis and gaming
  • Linear Regression Techniques
  • Ai in core business

Data Science with AI & ML :

  • Introducing Artificial Intelligence (AI)
  • Branches of AI
  • Role of Data, Algorithms and Hardware in AI
  • Data Analysis with AI
  • Machine Learning and Hadoop
  • Machine Learning in AI
  • Data Mining and the Web
  • Text Mining and Analytics
  • Pattern Discovery in Data Mining
  • Analyzing Clusters in Data Mining
  • Data Visualization and Tools
  • Exploring Mobile Analytics
  • Exploring Real-world Analytical Organizations
  • Big Data in Different Industries
  • Deep Learning with Tensor Flow
  • Planning with AI
  • Computer Vision, Voice and Natural Language Processing with AI

Case Studies :

  • Case Study 1: Information Technology
  • Case Study 2: Medical
  • Case Study 3: Interacting with Humans
  • Case Study 4: Transportation
  • Case Study 5: Manufacturing & Logistics
  • Case Study 6: Crisis Management

Quick facts

  • 6 months Course
  • 2 hours/day, 6 days a week – Hybrid Model
  • 3 course Participation Certificates
  • 1 IANT Certificate
  • 3 International Certificates
  • 5 Case studies
  • Spoken English
  • Access to LMS (Learning Management System)
  • E-books / Videos