Introduction to Data Science Programming
Date: March-June 2023
Lecture Week 1 - Introduction to Data Science and Python
Overview
1.1 Introduction to Data Science and Python
This session covers an introduction to Data, Data Science and Python. We also cover the 'Anaconda distribution' and different interfaces it has for python. |
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1.2 Getting Started with Python: Lab Session
This is a worksheet that covers the instructions on installing Anaconda, Python Interfaces and a few start-up activities to work with. |
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1.3 Collection of data
This session covers the data types in python that are used for representing collection(s) to Data. They are List, Tuple, Sets, and Dictionary. |
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1.4 Collection of Data: Lab Session
This worksheet (Jupyter-Notebook) covers the Collection(s) of Data, specifically, list, tuple, sets, and dictionary. Download this zip folder and, extract it in a folder, and open jupyter-notebook in your jupyter-notebook interface. Note that 'images' folder should be in same folder as this notebook to display all the required figures for explanation. |
Lecture Week 2 - Arrays in Numpy and visualisation in Matplotlib
Overview
2.1 Vectors, Matrices, and Numpy Arrays
This session starts with a brief introduction of for-loop and its applications in linear algebra, followed by a details of NumPy Arrays. |
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2.2 More on Numpy Arrays
This session continues on Numpy Arrays. |
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2.3 Loop and NumPy: Lab Session
This worksheet (Jupyter-Notebook) covers the Loop (specifically for-loop) and NumPy. Download this zip folder and, extract it in a folder, and open jupyter-notebook in your jupyter-notebook interface. Note that 'data' folder should be in same folder which includes some of the data files you would need. |
Lecture Week 3 - Program Development and visualisation tools
Overview
3.1 Control Flow: Program Development
This session covers the control flow tools such as if-else, nested loops and interruptions |
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3. 2 Control Flow: Lab Session
This worksheet (Jupyter-Notebook) covers the Control Flow Tools such as if-else, Boolean operators and for-loop/while-loop. |
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3.3 Function: Program Development
This session covers a more on control flow tools and details of Function. |
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3.4 Visualisation with Matplotlib
This session covers visualisation of data using Matplotlib library |
Lecture Week 4 - Data and File Handling
Overview
4.1 Data Handling with Pandas
This session covers the Data Handling using Pandas library. |
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4.2 Function, Visualisation, and Pandas : Lab Session
This worksheet (Jupyter-Notebook) covers the tasks related to functions, visualisations using matplotlib and data handling using pandas. |
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4.3 Error Handling
This session includes the Error Handling in Python. |
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4.4 Error Handling: Lab Session
This worksheet covers the error handling and docstring. |
Lecture Week 5 - File Handling and Conclusion
Overview
5.1 More on File Handling
This session covers more on file handling, specifically, text files, numpy files and pickle files. We will also have time for doubts and questions about any topics that we have covered so far. |
Lab Weeks - Practical sessions
Overview
1.4 Worksheet
This session covers the worksheet on python. |
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2.3 Worksheet
This session covers the worksheet on python. |