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
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 is Data Science?
Data Science lifecycle
Roles: Data Analyst vs. Data Scientist vs. ML Engineer
Industry tools & workflows
Python basics: variables, data types, loops, functions
Working with packages (pip, venv)
Jupyter Notebook & Google Colab
Writing clean, modular, reusable code
NumPy: arrays, vectorization
Pandas: data wrangling, merging, grouping
Handling missing data, categorical data
Basic exploratory data analysis (EDA)
Exploratory Data Analysis on a real-world dataset (e.g., sales, healthcare, finance).
Matplotlib, Seaborn, Plotly
Dashboard basics with Power BI or Tableau
Creating reports & insights
Descriptive statistics
Probability distributions
Hypothesis testing & A/B testing
Correlation vs. causation
Relational databases
SELECT, JOIN, GROUP BY
Window functions
Writing optimized SQL queries
Data visualization dashboard + SQL-driven insight report.
ML workflow
Supervised vs. Unsupervised learning
Model evaluation & metrics
Linear & Logistic Regression
kNN
Decision Trees & Random Forest
Gradient Boosting (XGBoost / LightGBM)
Clustering (K-means, DBSCAN)
Dimensionality Reduction (PCA)
Build and compare ML models for a prediction or classification task.
Neural Networks fundamentals
TensorFlow / Keras basics
Simple ANN classification/regression
Saving & loading models (Pickle, Joblib)
Creating API with Flask or FastAPI
Deploying to the cloud (Render, AWS, or HuggingFace Spaces)
Working with real production datasets
Data ethics & privacy
How to build a data science portfolio
Technical interview preparation
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
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
A forward-thinking tech and vocational training institute committed to equipping students with the digital skills required to thrive in today’s innovation-driven world.
© Copyright 2025 Adavi Digital Institute. All Rights Reserved.