Data Analysis in Python and AI (Urdu medium) Live Via Zoom
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- Curriculum
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This course is designed to provide a comprehensive and practical introduction to Data Analysis using Python and AI techniques. The program covers fundamental to advanced analytical methods, enabling learners to analyze, visualize, and interpret real-world datasets with confidence.
Through hands-on practice, participants will learn Python programming, exploratory data analysis, statistical modeling, regression analysis, time series forecasting, multivariate methods, and advanced visualization techniques. The course is delivered in an Urdu-medium teaching style, making complex concepts easier to understand while maintaining international academic standards.
This course is ideal for students, researchers, and professionals who want to build strong, publication-ready data analysis skills using Python.
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1
Module 1.1: Introduction to Python and AnacondaThis lesson introduces Python as a programming language and Anaconda as a complete data-science platform. Students will learn the difference between Python and Anaconda, understand why Anaconda is widely used in data analysis and AI, and learn how to launch Jupyter Notebook and create a proper working directory for projects.
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2Module 1.2: Introduction to Jupyter Notebook, Working Directory, and Python Packages
To introduce you to the Jupyter Notebook environment, show how to manage your working directory, and work with essential Python packages used in data science.
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3Module 1.3:How to Import an Excel File in Jupyter Notebook (Python)
In Jupyter Notebook, you can import Excel files using pandas. You’ll install required libraries (if needed), place your Excel file in the correct folder, and then load it into a pandas DataFrame using
read_excel(). -
4Module 1.4:Import Excel in Python and Make a Seaborn Scatter Plot
Load an Excel (.xlsx) file into Python using pandas, then visualize two columns with a scatter plot using seaborn.
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5Module 1.5: Import Multiple Sheets from One Excel File and Create Bar, Box, Line & Violin Plots
This workflow explains how to load different sheets from the same Excel file into your analysis environment and then create four common plot types — bar plot, box plot, line plot, and violin plot — for data exploration and presentation.
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6Module 1.6: Using ChatGPT to Generate Python Scripts (Best Practice Workflow)
ChatGPT can help you write Python scripts faster by generating code templates, fixing errors, improving performance, and adding comments—if you provide clear inputs like your goal, data format, and expected output.
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7Module 1.7: PCA, Heatmap, Dendrogram & Chord Diagram in Python: Data Import → Explore → Visualize
Load your dataset in Python, check structure and missing values, prepare numeric features + metadata, then create PCA, heatmap, dendrogram, and a chord diagram, saving each plot as high-quality files.
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8Assignment Task 1
Dear Students,
To strengthen your understanding of today’s concepts, please complete the following assignment.
📩 Send your plots in PDF/PNG format in group.
This assignment will help you build confidence in applying PCA and clustering techniques independently on real datasets.
If you face any issues, note them down and we will discuss them in short zoom meeting.
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9Assignment task 2
Dear Students,
To strengthen your understanding of today’s concepts, please complete the following assignment.
📩 Send output plots in PDF/PNG format in group.
This assignment will help you build confidence in applying clustering techniques independently on real datasets.
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10Assignment Task 3
Dear Students,
To strengthen your understanding of today’s concepts, please complete the following assignment.
📩 Submit your output plots in PDF/PNG format.
This assignment will help you build confidence in applying Heatmap plots and clustering techniques independently on real datasets.
If you face any issues, note them down and we will discuss them in the live session.
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11Module 1.8: create Study area map in python
In this module, you will learn how to create a clean, publication-ready study area map in Python using shapefiles (boundaries), point locations (samples/sites), and basic cartographic elements like title, legend, scale bar, and north arrow.
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12Module 1.9: Study area map with pie plots or % values
In this module, you will learn how to create a clean, publication-ready study area map in Python using shapefiles (boundaries), point locations (samples/sites), and basic cartographic elements like title, legend, scale bar, and north arrow.
Certificate fee is PKR 1,500 (optional).
Course Title: Data Analysis in Python & AI
Batch: 24
Medium of Instruction: Urdu
Duration: 15 Days
Total Seats: 20
Available Seats: 15
Course Fee: 3500PKR
Basic computer literacy
No prior programming experience required
A laptop or desktop computer (Windows/macOS/Linux)
Stable internet connection
Willingness to practice and complete hands-on exercises
Interest in data analysis, statistics, or research
This course is intended for:
Undergraduate, MS, MPhil, PhD, and Postdoctoral students
Researchers and academic scholars from science, social science, and health disciplines
Professionals seeking to develop data analysis skills using Python
Beginners who want to start a career in data analysis or data science
Teachers and instructors aiming to enhance their analytical and research skills
Industry professionals interested in data-driven decision making
Anyone interested in learning Python for real-world data analysis and AI applications
No prior programming experience is required. The course is structured to support learners from beginner to advanced level through practical, step-by-step instruction.
