Data Science

Join the program and get the opportunity to learn under the guidance of industry experts in Data Science with real-world project experience.

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Course Duration
3/6 Month

Certificate
Yes

Live Project
Yes

Training Mode
Online / Offline

Data Science Overview

Data Science is one of the fastest-growing fields, helping businesses make data-driven decisions using analytics, machine learning, and visualization.

In this course, you will learn how to:

  • Analyze and interpret complex data
  • Build predictive models
  • Create dashboards and visual reports
  • Work with real-world datasets

This course is designed for beginners as well as professionals who want to become data-driven decision makers.

Here’s what you can expect:

  • Hands-on practical training
  • Real-world datasets
  • Industry case studies
  • Resume & interview preparation

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    Explore Modules of this course

    Build a strong foundation in Python for Data Science.

    Topics Covered:

    • Introduction to Programming
    • Python Basics (Variables, Data Types, Operators)
    • Control Structures (Loops, Conditions)
    • Functions & Modules
    • OOP Concepts
    • File Handling
    • Virtual Environments

    Understand the core concepts behind data analysis.

    Topics Covered:

    • Descriptive Statistics (Mean, Median, Mode)
    • Probability Basics
    • Distributions (Normal Distribution)
    • Variance & Standard Deviation
    • Correlation & Covariance
    • Hypothesis Testing

    Work with data and generate insights.

    Topics Covered:

    • NumPy (Numerical Computing)
    • Pandas (Data Manipulation)
    • Data Cleaning & Preprocessing
    • Exploratory Data Analysis (EDA)
    • Data Visualization:
      • Matplotlib
      • Seaborn
    • Storytelling with Data

    Learn how to handle structured data.

    Topics Covered:

    • SQL Basics (MySQL / PostgreSQL)
    • CRUD Operations
    • Joins, Indexing
    • Query Optimization
    • Data Extraction & Transformation

    Build predictive models.

    Topics Covered:

    • Introduction to Machine Learning
    • Supervised Learning:
      • Linear Regression
      • Logistic Regression
      • Decision Trees
      • Random Forest
    • Unsupervised Learning:
      • K-Means Clustering
    • Model Evaluation Metrics

    Create dashboards and reports.

    Topics Covered:

    • Power BI / Tableau Basics
    • Dashboard Creation
    • Data Reporting
    • Business Insights Generation

    Apply data science in real-world scenarios.

    Topics Covered:

    • Feature Engineering
    • Model Optimization
    • Introduction to Big Data
    • Model Deployment (Flask / APIs)
    • Cloud Basics

    Apply your learning by working on real-world data problems.

    Projects Included:

    • Sales Data Analysis Dashboard
    • Customer Segmentation Model

    What You Will Do:

    • Collect and clean datasets
    • Perform data analysis (EDA)
    • Build machine learning models
    • Create dashboards (Power BI / Tableau)
    • Present insights and reports

    Outcome:

    • Portfolio-ready projects
    • Real business case experience
    • Industry-ready skills

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