6-Month Program · Python, SQL, ML & Deep Learning
Data Science
The Data Science course at Skill Training Institute, Alpha-1, Greater Noida is a 6-month, 48-module, 180+ hour program that teaches Python, SQL, Machine Learning and Deep Learning for data analysis, visualization, predictive modelling and deployment. No prior programming or statistics experience is required. It runs in offline (Greater Noida) and online live batches, includes weekly practical assessments, a 5-project portfolio, and ends with a Certificate of Completion plus 100% Placement Assist.
48 hands-on modules · 24 tools & platforms · 180+ hours of training · Data analysis, statistics, machine learning and deep learning fundamentals using industry-standard tools. Covers Python programming, SQL querying, data visualization, predictive modelling, neural networks and model deployment, built around real datasets and business problems from day one.
Who this course is for
Built for students, working professionals and career-switchers who want in-depth, dedicated training on Python, SQL, Machine Learning and Deep Learning for data analysis, visualization and predictive modelling — no prior programming or maths background needed, every core concept is taught from the ground up.
By the end you'll clean and analyse data in Python, query and join large datasets in SQL, build and evaluate machine learning and deep learning models, and deploy them the way a data science team expects.
1Program structure
Four phases over 24 weeks — from Python and statistics, through SQL and data visualization, into machine learning, and finally deep learning, NLP and a full end-to-end portfolio.
| Phase | Modules | Focus |
|---|---|---|
| Phase 1 — Python & Statistics Fundamentals | 01–12 | Python programming, data structures and statistics/probability for data analysis |
| Phase 2 — Data Analysis, SQL & Visualization | 13–24 | Advanced Pandas, SQL querying, and dashboards from real data |
| Phase 3 — Machine Learning | 25–36 | Regression, classification, ensembles, clustering and model tuning |
| Phase 4 — Deep Learning, NLP & Capstone | 37–48 | Neural networks, CNNs, RNNs, NLP, deployment and a full end-to-end capstone |
2Full curriculum — all 48 modules
Click any phase to expand its modules, or open a module directly for a detailed description.
Phase 1Python & Statistics FundamentalsModules 01–12Python programming, data structures and statistics/probability for data analysis+
01Introduction to Data Science & Python Setup+
Understanding the data science workflow and setting up Python, Jupyter Notebook and core libraries.
02Python Basics: Variables, Loops & Functions+
Writing Python code using variables, conditionals, loops and functions for everyday data tasks.
03Data Structures: Lists, Dictionaries & Sets+
Working with Python's core data structures to organise and manipulate raw data.
04Functions, Modules & File Handling+
Writing reusable functions and modules, and reading/writing CSV, Excel and text files in Python.
05NumPy for Numerical Computing+
Using NumPy arrays and vectorised operations for fast numerical computation.
06Introduction to Pandas & DataFrames+
Loading, exploring and manipulating tabular data using Pandas DataFrames.
07Data Cleaning & Handling Missing Values+
Cleaning messy datasets — handling missing values, duplicates and inconsistent formats.
08Data Wrangling & Transformation+
Reshaping, pivoting and transforming datasets into the structure needed for analysis.
09Statistics & Probability Fundamentals+
Understanding descriptive statistics, distributions and probability concepts used in data analysis.
10Inferential Statistics & Hypothesis Testing+
Applying hypothesis testing, confidence intervals and p-values to draw conclusions from data.
11Exploratory Data Analysis with Python+
Combining cleaning, statistics and visualization into a structured exploratory data analysis.
12Phase 1 Project: Complete EDA on a Real Dataset+
Combining everything from Phase 1 into a complete exploratory data analysis on a real dataset.
Phase 2Data Analysis, SQL & VisualizationModules 13–24Advanced Pandas, SQL querying, and dashboards from real data+
13Advanced Pandas: Grouping & Merging Data+
Grouping, aggregating and merging multiple datasets together using Pandas.
14Introduction to SQL & Databases+
Understanding relational databases and writing basic SQL SELECT queries.
15SQL Joins, Aggregation & Subqueries+
Writing joins, group-by aggregations and subqueries to answer real business questions.
16Advanced SQL: Window Functions & Optimization+
Using window functions and query optimisation techniques for large, complex datasets.
17Data Visualization with Matplotlib+
Building line, bar, scatter and histogram charts to explore and present data using Matplotlib.
18Advanced Visualization with Seaborn+
Creating statistical visualizations — heatmaps, pair plots and distribution plots — using Seaborn.
19Business Dashboards with Power BI+
Connecting data sources and building interactive dashboards in Power BI.
20Advanced Power BI: DAX & Data Modelling+
Writing DAX measures and modelling relationships between tables for richer dashboards.
21Excel for Data Analysis & Reporting+
Using Excel formulas, pivot tables and charts alongside Python for quick data reporting.
22Web Scraping & API Data Collection+
Collecting real-world data from websites and APIs using Python for custom analysis.
23Working with Large Datasets: Big Data Basics+
Understanding big data concepts and handling larger-than-memory datasets efficiently.
24Phase 2 Project: SQL-Based Analysis & Dashboard+
Combining everything from Phase 2 into a complete SQL-based data analysis and dashboard project.
Phase 3Machine LearningModules 25–36Regression, classification, ensembles, clustering and model tuning+
25Introduction to Machine Learning & Scikit-learn+
Understanding the machine learning workflow and getting comfortable with Scikit-learn.
26Regression Models+
Building linear and multiple regression models to predict continuous outcomes.
27Classification Models+
Building classification models like logistic regression and decision trees for predicting categories.
28Decision Trees & Ensemble Methods+
Building Random Forest and boosting models to improve prediction accuracy.
29Support Vector Machines & K-Nearest Neighbours+
Applying SVM and KNN algorithms for classification and regression problems.
30Clustering & Unsupervised Learning+
Grouping data using clustering techniques like K-Means for pattern discovery.
31Dimensionality Reduction with PCA+
Reducing feature dimensions with Principal Component Analysis for cleaner, faster models.
32Model Evaluation & Cross-Validation+
Evaluating model performance with accuracy, precision, recall and cross-validation techniques.
33Feature Engineering & Selection+
Engineering and selecting the right features to improve model accuracy and reliability.
34Hyperparameter Tuning+
Tuning model hyperparameters using grid search and randomised search for best performance.
35Time Series Analysis & Forecasting+
Analysing time-based data and building forecasting models for trends and seasonality.
36Phase 3 Project: Complete ML Model Pipeline+
Combining everything from Phase 3 into a complete, tuned machine learning model pipeline.
Phase 4Deep Learning, NLP & CapstoneModules 37–48Neural networks, CNNs, RNNs, NLP, deployment and a full end-to-end capstone+
37Introduction to Neural Networks+
Understanding how neural networks work — neurons, layers, weights and activation functions.
38Deep Learning with TensorFlow & Keras+
Building and training deep learning models using TensorFlow and Keras.
39Convolutional Neural Networks (CNN) for Image Data+
Building CNNs for image classification tasks using real image datasets.
40Recurrent Neural Networks (RNN) for Sequence Data+
Building RNNs and LSTMs for sequence and time-series prediction tasks.
41Natural Language Processing Basics+
Understanding NLP fundamentals — tokenisation, stemming and text representation.
42Text Preprocessing & Sentiment Analysis+
Cleaning text data and building a sentiment analysis model on real text data.
43Model Deployment with Flask & Streamlit+
Packaging and deploying machine learning models as web apps using Flask and Streamlit.
44Version Control & Git for Data Science+
Using Git and GitHub to track, version and collaborate on data science projects.
45Cloud Basics for Data Science+
Understanding cloud fundamentals and hosting data science apps and models on the cloud.
46Building a Data Science Portfolio & Resume+
Curating projects into a professional portfolio and building a data science-focused resume.
47Capstone Project Planning & Data Collection+
Scoping a real business problem and collecting/cleaning the data for the capstone project.
48Capstone: End-to-End Data Science Project & Deployment+
Delivering a full end-to-end data science project — from raw data and SQL, through machine learning or deep learning, to a deployed and presented final solution.
324 tools & platforms you'll use
Everything is taught on real, industry-standard Python, SQL, Machine Learning and Deep Learning tools you'll use later on the job — not simulations. Swipe or use the arrows to browse every category.
4How each module is taught
Every module follows the same practical, real-brief lens, so you always know what to expect in class.
Concept Walkthrough
Trainer explains the concept with a real dataset or business problem first, then the code or workflow.
Live Along
You write the same code, query or model step by step, right alongside the trainer.
Hands-on Exercise
An independent task applying the concept to a new dataset or problem.
Doubt Clearing
One-on-one help for anyone stuck, before the class moves to the next module.
Weekly Assessment
A short practical Python/SQL test every week so gaps are caught early, not at the end.
5Projects & portfolio
Four practical, portfolio-ready data science projects across the full syllabus — the kind of work you'll show in interviews.
- Project 1. Exploratory data analysis project using Python (Pandas & statistics)
- Project 2. SQL-based data analysis and Power BI dashboard project
- Project 3. Machine learning model project — regression or classification
- Project 4. Capstone: end-to-end data science project from raw data to a deployed model
6Assessment & certification
Weekly practical assessments covering Python coding, SQL queries and data visualization workflows, with instructor review and feedback.
Final practical exam across all three phases, plus the capstone project used as your demonstrable, portfolio-ready proof of skill.
Certificate of completion, plus resume and data science portfolio support through our 100% Placement Assist program.
7Quick answers
Do I need any prior programming or maths background?
No prior experience is needed — every concept, from Python basics to statistics and machine learning, is taught from the ground up.
Do I need my own laptop with Python installed?
A laptop with reasonable specs is strongly recommended since you'll be coding daily and building a portfolio; Python, Jupyter and all required libraries are free and installed together in the first week. Institute computers are also available in class.
Will I work on real datasets, or is it mostly theory?
You'll work on real or realistic datasets from week one. Every module includes hands-on coding practice, ending in a full end-to-end data science capstone project.
Does this course cover machine learning, or only data analysis?
Both — you'll learn data cleaning, SQL and visualization first, then move into machine learning with regression, classification, clustering and model evaluation.
Are classes online, offline, or both?
Both. Offline batches run at our Alpha-1, Greater Noida campus; online live batches follow the same curriculum with the same trainers. Morning, evening and weekend timings are available.
What can I do after completing this course?
You'll be ready for data analyst, junior data scientist and business intelligence roles across industries, with a client-ready portfolio, and can progress into advanced machine learning or deep learning tracks with the same placement support.
Ready to see a class live?
Book a free, no-obligation demo class and sit in on a real Python or SQL data science session before you decide anything.