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Unlock the power of data with our intensive Data Science & Machine Learning program. This course equips learners with cutting-edge analytical, programming, and predictive modeling skills to solve real-world problems. Gain hands-on experience, build industry-ready projects, and prepare for high-demand careers in today’s data-driven world.

#510,000

#850,000

PROGRAM OVERVIEW

The Data Science & Machine Learning Program is a highly practical, industry-aligned training designed to equip learners with the technical expertise and analytical mindset needed to thrive in today’s data-driven world. Over the course of four months, students gain hands-on experience working with real datasets, building predictive models, visualizing insights, and deploying machine learning solutions.

The program begins with a strong foundation in Python programming, data exploration, and statistical thinking—core skills for every aspiring data professional. Learners advance into data visualization, SQL querying, and modern analytics tools, enabling them to interpret trends and drive decision-making with confidence.

As the training progresses, students dive deep into machine learning algorithms, covering supervised and unsupervised techniques, model evaluation, and performance optimization. They also explore the fundamentals of deep learning and learn how to build and deploy intelligent systems using popular frameworks.

By the end of the program, each participant completes a full end-to-end capstone project, building a portfolio that showcases job-ready skills. Whether you’re starting a tech career or transitioning from another field, this program empowers you to become a confident, competent, and industry-ready Data Science & Machine Learning professional.

What You'll Learn

Module 1: Introduction to Data Science (Week 1)
  • What is Data Science?

  • Data Science lifecycle

  • Roles: Data Analyst vs. Data Scientist vs. ML Engineer

  • Industry tools & workflows

Module 2: Python Programming for Data Science (Week 2-3)
  • Python basics: variables, data types, loops, functions

  • Working with packages (pip, venv)

  • Jupyter Notebook & Google Colab

  • Writing clean, modular, reusable code

Module 3: Data Exploration with Python (Week 4-5)
  • NumPy: arrays, vectorization

  • Pandas: data wrangling, merging, grouping

  • Handling missing data, categorical data

  • Basic exploratory data analysis (EDA)

Practical Project 1:

Exploratory Data Analysis on a real-world dataset (e.g., sales, healthcare, finance).

Module 4: Data Visualization (Week 6)
  • Matplotlib, Seaborn, Plotly

  • Dashboard basics with Power BI or Tableau

  • Creating reports & insights

Module 5: Statistics & Probability for Data Science (Week 7)
  • Descriptive statistics

  • Probability distributions

  • Hypothesis testing & A/B testing

  • Correlation vs. causation

Module 6: SQL for Data Analytics (Week 8)
  • Relational databases

  • SELECT, JOIN, GROUP BY

  • Window functions

  • Writing optimized SQL queries

Practical Project 2:

Data visualization dashboard + SQL-driven insight report.

Module 7: Machine Learning Concepts (Week 9)
  • ML workflow

  • Supervised vs. Unsupervised learning

  • Model evaluation & metrics

Module 8: Supervised Learning Algorithms (Week 10)
  • Linear & Logistic Regression

  • kNN

  • Decision Trees & Random Forest

  • Gradient Boosting (XGBoost / LightGBM)

Module 9: Unsupervised Learning Algorithms (Week 11)
  • Clustering (K-means, DBSCAN)

  • Dimensionality Reduction (PCA)

Practical Project 3:

Build and compare ML models for a prediction or classification task.

Module 10: Deep Learning Essentials (Week 12)
  • Neural Networks fundamentals

  • TensorFlow / Keras basics

  • Simple ANN classification/regression

Module 11: Model Deployment & MLOps Basics (Week 13)
  • Saving & loading models (Pickle, Joblib)

  • Creating API with Flask or FastAPI

  • Deploying to the cloud (Render, AWS, or HuggingFace Spaces)

Module 12: Data Science in Practice (Week 14)
  • Working with real production datasets

  • Data ethics & privacy

  • How to build a data science portfolio

  • Technical interview preparation

Final Capstone Project (Week 15-16)

A complete end-to-end Data Science solution:

  • Data extraction (CSV/API/SQL)

  • Cleaning & preprocessing

  • Exploratory analysis

  • Building ML model

  • Deploying a working ML app

  • Presenting insights professionally

Career Path After Completion
  • Data Analyst

  • Business Data Analyst

  • Data Scientist

  • Junior Machine Learning Engineer

  • Quantitative Analyst (Quant)

  • Data Visualization Specialist

  • Research Data Analyst

  • Data Engineer

  • MLOps Engineer

  • Cloud Data Specialist

  • ETL/ELT Developer

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