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Data Science Simplified!

Become a Courses Without Quitting Your Existing Job

Online & Offline

Classes Mode

English/Kannada

Language

Upto 6 months

No Cost EMI Options

Weekday & Weekend Batches

Classes

10+ Modules

Course Syllabus

4 Months

Duration

Tools You Will Learn

Empowering learners with expert guidance, real-world skills, and future-ready education.

Python

My Sql

Power BI

Matplotlib

Tableau

Jupyter

Seaborn

Scikit-Learn

Pondas

NumPy

AWS

GitHub

What You’ll Learn

Empowering learners with expert guidance, real-world skills, and future-ready education.

  • No-Code Data Science Concepts

  • Data Cleaning & Data Pre-Processing

  • Statistical Analysis & Feature Engineering

  • Machine Learning Fundamentals

  • Supervised Learning & Model Evaluation

  • Cross-Validation Techniques

  • Feature Importance & Model Interpretation

  • Unsupervised Learning Overview

  • Model Deployment & Maintenance Basics

  • Introduction to Python & Virtual Environments

  • Python Programming for Data Science

  • File Handling using Pandas

  • Deep Dive into Core Python Libraries (NumPy, Pandas, etc.)

  • Data Pre-Processing Techniques

  • Data Wrangling & Transformation

  • Introduction to Data Visualization

  • Matplotlib Fundamentals

  • Seaborn Fundamentals

  • Exploratory Data Analysis (EDA)

  • Advanced Plots & Customization

  • Understanding Plot Attributes & Styling

  • Advanced Data Pre-Processing

  • Feature Engineering Techniques

  • Dimensionality Reduction (PCA, t-SNE)

  • Pre-Processing Pipelines & ColumnTransformer

  • Case Study 1: End-to-End Feature Engineering

  • Case Study 2: Real-World Dataset Implementation

  • Introduction to Machine Learning

  • Types of Machine Learning

  • Model Basics

  • Model Evaluation Concepts

  • Cross-Validation Techniques

  • Supervised Learning Overview

  • Evaluation Metrics:

    • MSE, RMSE, R²

    • Precision, Recall, F1-Score

    • Confusion Matrix

  • Classification Algorithms

  • Decision Trees & Ensemble Models

  • Regularization Techniques

  • Overfitting & Underfitting

  • Model Optimization & Pruning

  • Clustering Techniques

    • K-Means

    • Hierarchical Clustering

    • Noise Reduction

  • Unsupervised Evaluation Metrics

  • Hyperparameter Tuning

    • Grid Search

    • Random Search

    • Bayesian Optimization

  • Basics of Model Deployment

  • Case Study 3: Complete ML Pipeline Implementation

  • Time Series Analysis

  • Forecasting Techniques

  • Introduction to Deep Learning

  • Neural Networks Fundamentals

  • Introduction to NLP

  • Final Project – Day 1: Project Development & Presentation

  • Final Project – Day 2: Final Presentation & Review

  • Cloud Computing Introduction

  • Power BI Fundamentals

  • Tableau Fundamentals

  • Database Concepts

    • SQL

    • NoSQL

  • CI/CD Tools: Jenkins or Concourse

  • GitHub & Version Control

This Course is Designed For

Empowering learners with expert guidance, real-world skills, and future-ready education.

IT Professionals

Upgrade your career: Master Data Science, analytics, and ML to become the expert every org needs.

Non IT Professionals

Transition smoothly: Data Science bridges your existing domain to high-growth analytics careers.

 
Graduates Seeking for Job

Kickstart your career with hands-on projects &case studies to land your first Data Science role.

College Students

Graduate with the practical knowledge, projects, and confidence to stand out in placements.

Before and After Transformation

Stuck Before

  • Confused between Data Analyst, Data Scientist, ML
  • Resume has theory. Recruiters want skills.
  • Tools are easy. Workflows are hard.
  • Fear of switching careers without a clear plan.
  • You learn alone. No mentor. No feedback
  • Blind to 2026's hiring demands.

Success After

  • Ability to start real projects with confidence.
  • Crystal-clear clarity on the right role for you.
  • A resume strategy tailored for Data Science hiring
  • Confidence to switch careers without guesswork.
  • 1-on-1 mentor support guiding every step.
  • Clear path to in-demand jobs.

What Our Students Say

Quick video reviews from students sharing their learning experience and progress.

Frequently Asked Courses

Answers to common questions about our Data Science and ML

Designed for beginners, students, working professionals, and career switchers. Start learning data science from zero—only basic computer knowledge is needed.

No. This course is beginner-friendly. Whether you're switching careers or just starting out, we begin from the basics and guide you step by step

Yes. You receive a course completion certificate after finishing the capstone project.

Typical formats are 4 to 5 Months.

You get resume help, interview prep, and job guidance. We do not guarantee jobs.

Yes. We teach Python for Data Science starting from scratch. You’ll also learn data wrangling, visualization, and advanced ML techniques

You’ll work with industry-standard tools like Python, Pandas, Seaborn, Scikit-Learn, Power BI, Tableau, SQL, and more—including deployment tools like Jenkins and GitHub

This program blends no-code tools, Python programming, machine learning, and deployment strategies

Yes, EMI is available.

Join with Courses

Become part of a growing community of learners focused on skills, growth, and career success.