Data Science program

Unlock the Power of Data with Comprehensive Training in Analytics, Machine Learning, and Data Visualization.

Exploring the World of Data Science

A Comprehensive Guide

Basics to Advanced Analytics

Comprehensive Training in Machine Learning, Data Visualization, and Predictive Analytics

Transform Data into Actionable Insights

Learn Cutting-Edge Techniques in Big Data, AI, and Statistical Analysis to Drive Business Decisions

Innovate with Data-Driven Solutions

Data Science Tools and Techniques to Solve Complex Problems and Propel Your Career

Why Choose Data Science?

The skills and knowledge needed to turn data into actionable insights

Data science is a fascinating field that involves extracting insights and knowledge from data. It is also the most demanding profession in the IT industry and also helps industries grow and expand their businesses by extracting valuable data insights from raw information.

Several modern methods are used by data scientists to drive profitability and cater to the need for solutions to real-world problems.

High Demand Across Industries

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Ecommerce

Data science enhances customer experience and optimizes supply chain management in the e-commerce industry.

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ML & AI

Data science is the backbone of developing sophisticated ML and AI algorithms for predictive analytics and automation.

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Government

Governments utilize data science to improve public services, policy-making, disaster response, and resource allocation.

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Healthcare

In healthcare, data science aids in patient care, medical research, disease prevention, and operational efficiency.

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Energy

Data science optimizes energy production, distribution, and consumption, promoting sustainability.

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Finance

The finance industry relies on data science for risk management, fraud detection, and investment strategies.

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What you'll learn

  • Basics of SQL Syntax and Commands
  • Retrieving Data Using SELECT Statement
  • Filtering Data Using WHERE Clause
  • Sorting Data Using ORDER BY Clause
  • Joins in SQL: INNER JOIN, LEFT JOIN, RIGHT JOIN
  • Grouping Data Using GROUP BY Clause
  • Subqueries and Common Table Expressions (CTEs)
  • Advanced SQL Concepts: Window Functions
SQL Image
  • Overview of Excel Interface and Basic Functionalities
  • Importing Data into Excel from Various Sources
  • Basic Functions: SUM, AVERAGE, COUNT, etc
  • Creating PivotTables and PivotCharts
  • Data Visualization Using Charts and Graphs
  • Text Functions
  • Date Functions, and Data Manipulation
SQL Image
  • Array
  • Operators
  • Variables
  • Data types
  • Comments
  • Conditions
  • Function
  • Loop
  • Object
  • Regular Expressions
  • File handling
  • Pickling, and many more essential concepts
  • Algorithm thinking
  • Definitions and concepts
  • Importance in Data Science
  • Types of data: Quantitative vs. Qualitative
  • Measures of central tendency: Mean, Median, Mode
  • Measures of variability: Range, Variance, Standard Deviation, Interquartile Range
  • Data visualization: Histograms, Box plots, Bar charts, Scatter plots
  • Basic probability concepts
  • Probability axioms and rules
  • Conditional probability and Bayes’ theorem
  • Independent and dependent events
  • Probability mass function (PMF) and probability density function (PDF)
  • Text Functions
  • Date Functions, and Data Manipulation
  • Introduction to Machine Learning
  • Overview
  • Types of Machine Learning
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Model Evaluation Metrics
  • Support Vector Machines (SVM)
  • Dimensionality Reduction
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequence modeling
  • Generative Adversarial Networks (GANs)
  • Autoencoders
  • Perceptrons and multilayer perceptrons
  • Overview of Data Visualization
  • Types of Data Visualizations
  • Tools and Libraries
  • Data Exploration with Visualizations
  • Handling Missing Values and Outliers
  • Data Distribution and Trends
  • Geospatial Data Visualization
  • Network Visualization
  • Interactive Visualization
  • Color Theory and Palette Selection
  • Layout and Typography
  • Feature Analysis and Importance
  • Model Evaluation Visualization

 

SQL
  • Basics of SQL Syntax and Commands
  • Retrieving Data Using SELECT Statement
  • Filtering Data Using WHERE Clause
  • Sorting Data Using ORDER BY Clause
  • Joins in SQL: INNER JOIN, LEFT JOIN, RIGHT JOIN
  • Grouping Data Using GROUP BY Clause
  • Subqueries and Common Table Expressions (CTEs)
  • Advanced SQL Concepts: Window Functions
Excel
  • Overview of Excel Interface and Basic Functionalities
  • Importing Data into Excel from Various Sources
  • Basic Functions: SUM, AVERAGE, COUNT, etc
  • Creating PivotTables and PivotCharts
  • Data Visualization Using Charts and Graphs
  • Text Functions
  • Date Functions, and Data Manipulation
Python
  • Array
  • Operators
  • Variables
  • Data types
  • Comments
  • Conditions
  • Function
  • Loop
  • Object
  • Regular Expressions
  • File handling
  • Pickling, and many more essential concepts
  • Algorithm thinking
Descriptive Statistics
  • Definitions and concepts
  • Importance in Data Science
  • Types of data: Quantitative vs. Qualitative
  • Measures of central tendency: Mean, Median, Mode
  • Measures of variability: Range, Variance, Standard Deviation, Interquartile Range
  • Data visualization: Histograms, Box plots, Bar charts, Scatter plots
Probability Theory
  • Basic probability concepts
  • Probability axioms and rules
  • Conditional probability and Bayes’ theorem
  • Independent and dependent events
  • Probability mass function (PMF) and probability density function (PDF)
  • Text Functions
  • Date Functions, and Data Manipulation
Machine Learning
  • Introduction to Machine Learning
  • Overview
  • Types of Machine Learning
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Model Evaluation Metrics
  • Support Vector Machines (SVM)
  • Dimensionality Reduction
Deep Learning
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequence modeling
  • Generative Adversarial Networks (GANs)
  • Autoencoders
  • Perceptrons and multilayer perceptrons
Data Visualization
  • Overview of Data Visualization
  • Types of Data Visualizations
  • Tools and Libraries
  • Data Exploration with Visualizations
  • Handling Missing Values and Outliers
  • Data Distribution and Trends
Advanced Visualization
  • Geospatial Data Visualization
  • Network Visualization
  • Interactive Visualization
  • Color Theory and Palette Selection
  • Layout and Typography
  • Feature Analysis and Importance
  • Model Evaluation Visualization

Unlock the Advantages of Course Participation

graduate

Designed for Graduates & Working Professionals

self-disciplined

Self-paced Session

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Assessments

question

Doubt Clearing Session with Experts

money

EMI Option Available

instructor

Instructor-led Session by Experts

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Hands on Exercises

quiz (2)

Practical Assignments

translator

Available in English, தமிழ், Telugu, Malayalam and Kannada

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Frequently Asked Questions

Got Questions? We've Got Answers!

The course starts from 3 month to 6 months.
No prior experience is required, though basic programming knowledge is beneficial.
Yes, a certificate of completion will be awarded upon successful completion of the course.
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