Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Python for Data Science builds the practical programming foundation every data role depends on. Starting from core Python, you move quickly into the scientific stack: NumPy for numerical work, pandas for wrangling messy real-world data, and Matplotlib and Seaborn for visual analysis. You practise the full workflow — loading, cleaning, transforming, exploring and visualising datasets — then drawing defensible conclusions. Every concept is taught through hands-on notebooks on realistic data, so you finish able to open an unfamiliar dataset and produce a clear, reproducible analysis. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Python for Data Science builds fluency in the scientific Python stack — NumPy, pandas and Matplotlib/Seaborn — for loading, cleaning, exploring and visualising real-world data.
📋 Course Objectives
1. Write clean, idiomatic Python for data-manipulation tasks.
2. Wrangle and reshape tabular data with pandas and NumPy.
3. Run exploratory data analysis on real datasets.
4. Produce publication-quality charts with Matplotlib and Seaborn.
5. Structure an analysis as a reproducible Jupyter notebook.
👥 Who Should Enroll?
• Beginners starting a data-science or analytics career
• Researchers and students moving analysis from spreadsheets to code
• Developers adding data skills to their profile
• Analysts wanting a reproducible, code-first workflow
🚀 Key Learning Outcomes
• Fluency with the core Python data-science stack.
• A reproducible exploratory-analysis notebook for your portfolio.
• The ability to turn a raw dataset into clear insight.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Python Foundations
Develop a comprehensive understanding of linear algebra and calculus for data science applications • Analyze the fundamentals of probability and statistics for machine learning model development • Configure Python environments and libraries, including NumPy, pandas, and Matplotlib, for data science tasks
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using Apache Beam and Apache Spark for large-scale data processing • Evaluate and preprocess datasets using techniques such as handling missing values, data normalization, and feature scaling • Implement data quality checks and data validation using Python libraries like Great Expectations and Pandas
Model Architecture, Algorithm Design, and Python Methods
Develop and train machine learning models using scikit-learn and TensorFlow for classification, regression, and clustering tasks • Analyze and compare the performance of different algorithmic approaches, including decision trees, random forests, and neural networks • Optimize model hyperparameters using techniques such as grid search, random search, and Bayesian optimization
Training, Hyperparameter Optimization, and Evaluation
Configure and train deep learning models using Keras and TensorFlow for image classification, natural language processing, and time series forecasting • Evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, F1 score, and mean squared error • Implement cross-validation techniques, including k-fold cross-validation and stratified cross-validation, for model evaluation and selection
Deployment, MLOps, and Production Workflows
Design and deploy machine learning models using Docker, Kubernetes, and cloud platforms like AWS and GCP • Develop and implement model serving pipelines using TensorFlow Serving, AWS SageMaker, and Azure Machine Learning • Configure and monitor model performance in production environments using tools like Prometheus, Grafana, and New Relic
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and identify potential biases in machine learning models and datasets using techniques such as data auditing and fairness metrics • Develop and implement strategies for bias mitigation, including data preprocessing, feature engineering, and model regularization • Evaluate the ethical implications of AI systems and develop guidelines for responsible AI development and deployment
Industry Integration, Business Applications, and Case Studies
Develop and present business cases for AI adoption in various industries, including healthcare, finance, and retail • Analyze and discuss real-world applications of machine learning, including recommender systems, natural language processing, and computer vision • Design and propose AI-powered solutions for business problems, including customer segmentation, demand forecasting, and supply chain optimization
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | Matplotlib |
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