Data Science Course in Greater Noida (Alpha 1)
Statistics, Python for data analysis and machine learning fundamentals for a core data science role. 60 modules covering Python, Pandas and NumPy, SQL, Power BI, machine learning and deep learning, finishing with industry projects and a capstone.
What is the Data Science course?
The Data Science course at Skill Training Institute, Alpha 1, Greater Noida is a 6-month program with 60 modules in 8 phases. You learn Python, NumPy and Pandas, SQL and Power BI, statistics and probability, machine learning with scikit-learn, and an introduction to deep learning, NLP and generative AI, then build industry projects and a capstone for your portfolio.
Last updated:
What's inside the course
Built around one goal: turning data into decisions.
- Python programming from scratch to advanced basics
- NumPy, Pandas and exploratory data analysis
- Matplotlib, Seaborn, Plotly and Power BI dashboards
- SQL with MySQL: joins, window functions and CTEs
- Statistics, probability, hypothesis testing and A/B testing
- Machine learning: regression, classification, boosting and clustering
- Time series forecasting and recommendation basics
- Deep learning, NLP and generative AI introduction
- Model deployment, Git and a capstone project
Full syllabus: 60 modules, 8 phases
Every module has its own focus. Filter or search.
Phase 1: Data Science Foundations & Python Programming Modules 1–8, Weeks 1 to 3
Data Science Landscape, Roles & Workflow Python
See the whole field before writing code.
- Data analyst, data scientist and ML engineer roles
- The data science lifecycle
- Types of data and problems
- Tools and libraries overview
- Learning roadmap for the 6 months
Environment Setup: Python, Jupyter & Colab Python
Get a working data science setup.
- Installing Python and Anaconda
- Jupyter Notebook and VS Code
- Google Colab
- Virtual environments and packages
- Running and organising notebooks
Python Basics Python
Learn the core building blocks of Python.
- Variables and data types
- Operators and expressions
- Input and output
- Type conversion
- Writing clean, readable code
Control Flow & Functions Python
Make programs decide and repeat.
- Conditions: if, elif, else
- for and while loops
- Defining functions
- Arguments, return values and lambda
- Scope and simple recursion
Data Structures: Lists, Tuples, Dictionaries & Sets Python
Store and organise data in Python.
- Lists and tuples
- Dictionaries and sets
- Slicing and indexing
- List and dictionary comprehensions
- Choosing the right structure
Strings, Files & Error Handling Python
Read, write and clean simple data files.
- String methods and formatting
- Reading and writing text, CSV and JSON files
- Exceptions and try/except
- Debugging basics
- Small data-processing scripts
Modules, OOP & Packages Python
Organise code like a professional.
- Importing modules
- Classes and objects
- Inheritance basics
- Writing your own modules
- Standard library highlights
Regex, Dates & Working with APIs Python
Collect and prepare data from real sources.
- Regular expressions basics
- Dates and times with datetime
- Calling REST APIs with requests
- Handling JSON responses
- Web scraping basics and ethics
Phase 2: Data Analysis with NumPy & Pandas Modules 9–16, Weeks 4 to 7
NumPy Arrays & Vectorisation Data analysis
Work with numerical data efficiently.
- Creating arrays
- Indexing and slicing
- Vectorised operations
- Aggregations and axes
- Performance vs Python lists
NumPy Advanced: Broadcasting, Random & Linear Algebra Basics Data analysis
Use NumPy for maths-heavy tasks.
- Broadcasting rules
- Random numbers and seeds
- Reshaping and stacking
- Matrix operations
- Simulation examples
Pandas Series & DataFrames Data analysis
Load and inspect tabular data.
- Series and DataFrame structures
- Reading CSV, Excel and JSON
- Head, info and describe
- Data types and memory
- Saving results
Selecting, Filtering & Sorting Data Data analysis
Find exactly the rows and columns you need.
- loc and iloc
- Boolean filtering
- Sorting and ranking
- Adding and dropping columns
- Apply and map functions
Data Cleaning & Preparation Data analysis
Turn messy data into analysis-ready data.
- Missing values: detect, drop and impute
- Duplicates and inconsistent values
- Fixing data types
- Outlier detection
- Cleaning checklist and documentation
GroupBy, Aggregation, Merge, Join & Pivot Data analysis
Summarise and combine datasets.
- groupby and aggregation
- Pivot tables and crosstab
- Merge, join and concat
- Reshaping: melt and stack
- Multi-table analysis
Time Series & Text Data in Pandas Data analysis
Handle dates and text columns.
- Datetime index and resampling
- Rolling windows
- Lag and shift features
- String methods on columns
- Categorical data
Exploratory Data Analysis (EDA) Workflow Data analysis
Ask questions of a dataset and answer them with data.
- EDA framework
- Univariate and bivariate analysis
- Correlation and patterns
- Data quality report
- EDA mini project
Phase 3: Data Visualisation & Business Intelligence Modules 17–21, Weeks 7 to 9
Matplotlib for Data Visualisation Visualisation & BI
Draw clear charts in Python.
- Line, bar, scatter and histogram
- Figures, axes and subplots
- Labels, legends and styling
- Saving charts
- Choosing the right chart
Seaborn & Interactive Charts with Plotly Visualisation & BI
Make statistical and interactive visuals.
- Distribution and relationship plots
- Heatmaps and pair plots
- Categorical plots
- Plotly interactive charts
- Themes and consistency
Data Storytelling & Dashboard Design Visualisation & BI
Present data so decisions get made.
- Insight, context and recommendation
- Chart choice and colour use
- Avoiding misleading visuals
- Dashboard layout principles
- Presenting to non-technical audiences
Power BI: Data Modelling & DAX Basics Visualisation & BI
Build a data model in Power BI.
- Importing and transforming data with Power Query
- Relationships and star schema
- Calculated columns and measures
- DAX basics
- Time intelligence basics
Power BI Dashboards & Publishing Visualisation & BI
Create and share business dashboards.
- Visuals, slicers and filters
- Drill-through and bookmarks
- Dashboard design for business users
- Publishing to Power BI Service
- Sales or HR dashboard project
Phase 4: SQL & Databases Modules 22–27, Weeks 9 to 11
Database Fundamentals & MySQL Setup SQL & databases
Understand how relational databases work.
- Tables, keys and relationships
- MySQL and Workbench setup
- Data types and constraints
- Creating databases and tables
- Inserting and updating data
SQL Queries: SELECT, WHERE & Sorting SQL & databases
Retrieve data with SQL.
- SELECT and aliases
- WHERE, AND, OR, IN, LIKE
- ORDER BY and LIMIT
- Handling NULL values
- Practice query set
Aggregations, GROUP BY & HAVING SQL & databases
Summarise data in the database.
- COUNT, SUM, AVG, MIN, MAX
- GROUP BY
- HAVING vs WHERE
- CASE expressions
- Business reporting queries
Joins & Subqueries SQL & databases
Combine tables and nest queries.
- INNER, LEFT, RIGHT and FULL joins
- Self joins
- Subqueries
- EXISTS and IN
- Multi-table case study
Window Functions & CTEs SQL & databases
Write advanced analytical SQL.
- ROW_NUMBER, RANK and DENSE_RANK
- Running totals and moving averages
- LAG and LEAD
- Common table expressions
- Interview-style SQL problems
SQL with Python & Data Modelling Basics SQL & databases
Connect databases to your analysis workflow.
- Connecting Python to MySQL
- Reading query results into Pandas
- Writing data back to a database
- Normalisation basics
- Views and indexes overview
Phase 5: Statistics, Probability & Maths for Data Science Modules 28–35, Weeks 11 to 14
Descriptive Statistics Statistics & maths
Summarise data numerically.
- Mean, median and mode
- Variance and standard deviation
- Quartiles and IQR
- Skewness and kurtosis
- Summary statistics in Python
Probability Fundamentals Statistics & maths
Reason about uncertainty.
- Sample space and events
- Conditional probability
- Bayes' theorem
- Independence
- Probability puzzles and applications
Probability Distributions Statistics & maths
Model how data is spread.
- Normal distribution
- Binomial and Poisson
- Uniform and exponential
- Z-scores
- Simulating distributions in Python
Sampling & the Central Limit Theorem Statistics & maths
Learn from samples, not just whole populations.
- Sampling methods
- Sampling bias
- Central Limit Theorem
- Standard error
- Bootstrapping basics
Hypothesis Testing Statistics & maths
Test claims with data.
- Null and alternative hypotheses
- z-test and t-test
- Chi-square test
- ANOVA basics
- Choosing the right test
Confidence Intervals, p-values & Statistical Errors Statistics & maths
Interpret results correctly.
- Confidence intervals
- p-values and their limits
- Type I and Type II errors
- Power and effect size
- Common statistical mistakes
A/B Testing & Experiment Design Statistics & maths
Run and read experiments.
- Designing an A/B test
- Sample size and duration
- Metrics and guardrails
- Analysing results
- Reporting to stakeholders
Maths for Machine Learning: Linear Algebra & Calculus Basics Statistics & maths
Get the maths behind ML without fear.
- Vectors and matrices
- Dot product and matrix multiplication
- Derivatives and gradients
- Gradient descent intuition
- Correlation and regression maths
Phase 6: Machine Learning Modules 36–48, Weeks 14 to 19
Machine Learning Foundations & Workflow Machine learning
Understand how ML projects are structured.
- Supervised, unsupervised and reinforcement learning
- Train, validation and test sets
- Overfitting and underfitting
- Bias-variance trade-off
- scikit-learn workflow
Feature Engineering & Preprocessing Machine learning
Prepare data for models.
- Scaling and normalisation
- Encoding categorical variables
- Imputation
- Feature creation and selection
- scikit-learn pipelines
Linear & Polynomial Regression Machine learning
Predict numeric values.
- Simple and multiple linear regression
- Assumptions and diagnostics
- Polynomial features
- Regularisation: Ridge and Lasso
- House price prediction
Logistic Regression & Classification Machine learning
Predict categories.
- Logistic regression
- Decision thresholds
- Multi-class classification
- Class imbalance basics
- Customer churn prediction
Model Evaluation & Cross-Validation Machine learning
Judge models honestly.
- Accuracy, precision, recall and F1
- Confusion matrix
- ROC-AUC
- Regression metrics: MAE, RMSE, R²
- Cross-validation
Decision Trees & Random Forests Machine learning
Use tree-based models.
- Decision tree logic
- Entropy and Gini
- Random forest ensembles
- Feature importance
- Pruning and tuning
Gradient Boosting: XGBoost & LightGBM Machine learning
Use models that win on tabular data.
- Boosting concepts
- XGBoost basics
- LightGBM basics
- Handling categorical features
- Comparing with random forests
KNN, Naive Bayes & Support Vector Machines Machine learning
Learn other classic algorithms.
- K-nearest neighbours
- Naive Bayes for text and simple data
- Support vector machines
- Kernels
- When to use which
Clustering: K-Means, Hierarchical & DBSCAN Machine learning
Find groups in unlabelled data.
- K-means and choosing k
- Hierarchical clustering
- DBSCAN
- Silhouette score
- Customer segmentation project
Dimensionality Reduction: PCA & Visualisation Machine learning
Simplify high-dimensional data.
- Curse of dimensionality
- PCA
- t-SNE and UMAP for visualisation
- Feature selection methods
- Using PCA in pipelines
Hyperparameter Tuning & Model Selection Machine learning
Improve models systematically.
- Grid search and random search
- Bayesian optimisation overview
- Avoiding data leakage
- Model comparison
- Saving and loading models
Time Series Forecasting Machine learning
Predict values over time.
- Trend, seasonality and stationarity
- Moving averages and exponential smoothing
- ARIMA basics
- ML-based forecasting with lag features
- Sales forecasting project
Recommendation Systems & Anomaly Detection Machine learning
Build practical ML applications.
- Content-based recommendations
- Collaborative filtering
- Anomaly detection methods
- Fraud and outlier use cases
- Evaluation of recommenders
Phase 7: Deep Learning, NLP & Generative AI Modules 49–54, Weeks 19 to 22
Neural Network Fundamentals Deep learning & GenAI
Understand how neural networks learn.
- Neurons, layers and activation functions
- Forward and backward propagation
- Loss functions and optimisers
- Overfitting and dropout
- Building a network from scratch
Deep Learning with TensorFlow & Keras Deep learning & GenAI
Build and train models with Keras.
- Keras Sequential and Functional APIs
- Training, callbacks and early stopping
- Regularisation
- Saving models
- Tabular deep learning example
Convolutional Neural Networks & Computer Vision Basics Deep learning & GenAI
Work with image data.
- Image data and preprocessing
- Convolution and pooling
- Building a CNN
- Transfer learning
- Image classification project
NLP Basics: Text Processing & Classification Deep learning & GenAI
Turn text into features and predictions.
- Tokenisation and cleaning
- Bag of words and TF-IDF
- Sentiment analysis
- Text classification
- Word embeddings overview
Transformers, LLMs & Generative AI Basics Deep learning & GenAI
Understand modern language models and use them responsibly.
- How transformers and LLMs work
- Prompting and using LLM APIs
- Embeddings and semantic search
- Retrieval-augmented generation basics
- Limits: hallucinations, cost and privacy
Model Deployment with Streamlit & FastAPI Deep learning & GenAI
Share your model as a working app.
- Saving models with joblib
- Streamlit apps
- FastAPI basics
- Deploying to a cloud platform
- Monitoring and maintenance overview
Phase 8: Projects, Tools & Career Modules 55–60, Weeks 22 to 24
Git & GitHub for Data Scientists Projects & career
Version your work and share your portfolio.
- Git basics: commit, branch and merge
- GitHub repositories
- README and project documentation
- Collaboration workflow
- Portfolio on GitHub
Big Data & Cloud Awareness Projects & career
Know what happens when data gets big.
- Big data concepts
- Spark and PySpark overview
- Cloud notebooks and storage overview
- Data warehouses and pipelines overview
- When you need big data tools
Problem Framing, Data Ethics & Communication Projects & career
Solve the right problem responsibly.
- Turning business questions into data problems
- Choosing metrics
- Bias, fairness and privacy including India's DPDP Act 2023
- Explaining models to stakeholders
- Project documentation
Industry Projects: Regression, Classification & Forecasting Projects & career
Build end-to-end projects on real datasets.
- Project brief and dataset selection
- Cleaning and EDA
- Modelling and evaluation
- Dashboard or app for results
- Presenting findings
Capstone Project Projects & career
Deliver one complete data science project.
- Problem definition and data collection
- Analysis, modelling and validation
- SQL, Python and Power BI components
- Deployment or dashboard
- Final presentation and review
Portfolio, Resume & Interview Preparation Projects & career
Get ready for data roles.
- Portfolio and case-study pages
- Resume and LinkedIn for data roles
- SQL, Python and statistics interview questions
- Case study and take-home practice
- Mock interviews
Libraries and tools update often, so we teach current versions. Deep learning and generative AI are covered as an introduction. Phase timing is indicative.
Tools you will use
Taught hands-on inside project work.
Programming
- Python
- Jupyter Notebook
- Google Colab
- VS Code
Data analysis
- NumPy
- Pandas
- Pivot tables and GroupBy
Visualisation & BI
- Matplotlib
- Seaborn
- Plotly
- Power BI
Databases
- MySQL
- MySQL Workbench
- SQL with Python
Machine learning & AI
- scikit-learn
- XGBoost
- LightGBM
- TensorFlow
- Keras
Deployment & workflow
- Streamlit
- FastAPI
- Git
- GitHub
Hands-on projects
The capstone takes a problem from raw data to a working result.
- Python data-processing scripts
- EDA on a real-world dataset
- Data cleaning pipeline in Pandas
- Power BI business dashboard
- SQL analysis on a relational database
- A/B test analysis report
- House price or sales regression model
- Customer churn classification model
- Customer segmentation with clustering
- Sales forecasting model
- NLP sentiment analysis
- Streamlit app with a deployed model
- Capstone data science project
Who is this course for?
Beginners, career switchers and working professionals.
| If you are | You will |
|---|---|
| Students & freshers | A complete path from Python basics to machine learning, with a project portfolio. |
| Working professionals switching careers | Build data skills step by step alongside your job. |
| Data and business analysts | Add Python, statistics and machine learning to SQL and BI skills. |
| Software developers & engineers | Move into data science with statistics, ML and deployment skills. |
| Business owners & managers | Understand data, models and dashboards to make better decisions. |
| Freelancers | Offer analysis, dashboards and prediction projects to clients. |
Career opportunities
Roles this program prepares you for. Outcomes vary by learner.
- Data Analyst
- Junior Data Scientist
- Business Intelligence Analyst
- Business Analyst (data)
- Machine Learning Trainee
- Python Data Developer
Data Science vs related courses
Pick the right course for your goal.
| Course | Duration | Focus |
|---|---|---|
| Data Science (this course) | 6 months | Python, Pandas, SQL, statistics, machine learning, deep learning intro and Power BI: 60 modules |
| ChatGPT & AI Tools Mastery | 2 months | Using AI tools for daily work, with no coding or model building: 24 modules |
| Digital Marketing Pro Track | 6 months | Marketing strategy across SEO, paid media, analytics, content and AI: 80 modules |
What is included
Everything in one program.
- 60 modules in 8 phases
- 8 Python programming modules
- 8 Pandas and NumPy analysis modules
- 6 SQL modules and 5 visualisation and Power BI modules
- 8 statistics and maths modules
- 13 machine learning modules
- 6 deep learning, NLP and generative AI modules
- Industry projects, capstone and interview preparation
Certificate on completion
Awarded by Skill Training Institute after you complete the program.
Frequently Asked Questions
About the Data Science course.
What is the Data Science course?
It is a 6-month program at Skill Training Institute, Alpha 1, Greater Noida with 60 modules in 8 phases. It covers Python, NumPy and Pandas, data visualisation and Power BI, SQL, statistics, machine learning fundamentals and an introduction to deep learning, NLP and generative AI, with live projects and a capstone.
Do I need programming or maths knowledge?
No. Python is taught from the basics in Phase 1, and Phase 5 teaches the statistics and maths used in data science step by step. School-level maths is enough to start.
Which tools and libraries will I use?
Python, Jupyter and Google Colab, NumPy, Pandas, Matplotlib, Seaborn, Plotly, scikit-learn, XGBoost, TensorFlow and Keras, MySQL, Power BI, Streamlit, Git and GitHub.
Will I learn machine learning in depth?
Phase 6 has 13 modules on machine learning: regression, classification, model evaluation, tree-based models, boosting, clustering, dimensionality reduction, time series forecasting and recommendation systems, all with projects.
Do you cover deep learning and generative AI?
Yes, as an introduction. Phase 7 covers neural networks, Keras, CNNs, NLP basics, transformers, LLM APIs and model deployment. It builds on the machine learning fundamentals from Phase 6.
Is SQL included?
Yes. Phase 4 has 6 modules on MySQL, from basic queries to joins, window functions and connecting SQL with Python.
What projects will I build?
Projects include exploratory data analysis, a Power BI dashboard, SQL analysis, an A/B test report, regression, classification, clustering, forecasting, an NLP sentiment model, a deployed Streamlit app and a final capstone.
What kind of jobs does this prepare me for?
Roles such as data analyst, junior data scientist, BI analyst and machine learning trainee. Outcomes vary by learner, and we do not guarantee a job. Phase 8 covers portfolio, resume and interview preparation.
What laptop do I need?
A laptop with a modern processor and at least 8 GB RAM is recommended. Google Colab gives free cloud notebooks for heavier work. Ask us about your setup on WhatsApp.
What is the fee, and can I attend a demo class first?
Fees depend on the mode and batch you choose. WhatsApp or call +91-9773854034 for the current fee, batch timings and to book a free demo class.
Ready to start your data science career?
504, MSX Tower-2, Alpha-1 Commercial Belt, Greater Noida. Email contactus@skilltraininginstitute.in or see all courses.