https://github.com/shiftkey-labs/pythonda-course
https://github.com/shiftkey-labs/pythonda-course
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/shiftkey-labs/pythonda-course
- Owner: shiftkey-labs
- Created: 2024-10-15T00:35:46.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-16T16:08:13.000Z (over 1 year ago)
- Last Synced: 2025-04-15T07:19:10.161Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 1.2 MB
- Stars: 2
- Watchers: 0
- Forks: 15
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Foundations of Data Analytics with Python
Welcome to the **Foundations of Data Analytics with Python** course! This repository contains all the materials you'll need to follow along with the course. Each week is structured with its own set of instructions, hands-on labs, and additional resources to guide your learning. The course is designed and taught by **Sahil Chawla**.
## Course Structure
The course runs over four weeks, with one session per week, beginning on Wednesday, 16 October 2024. Each session includes theoretical explanations, hands-on practical examples, and regular pop quizzes to assess understanding of the topics covered.
## Course Timeline
- **16 Oct - Week 1**: Python Programming Basics (Variables, Input/Output, Conditional Statements)
- **23 Oct - Week 2**: Functions, Loops, Modules, Data Structures in Python
- **Oct 30 - Week 3**: Data Analytics & Visualization - Introduction to Pandas, NumPy for data manipulation
- **Nov 06 - Week 4**: Data Analytics & Visualization - Using Matplotlib for data visualization and insights
## Assessment
- An assessment will be released after Week 3.
- Due date: **16th November**.
- The assessment should take around 2 hours to complete.
- Grades will be provided by **30th November**.
- Submit the assessment by the due date to be eligible for the certificate!
## Certificate Eligibility
To receive a certificate of completion, you must meet the following criteria:
1. Attend at least 3 out of the 4 sessions (75% attendance).
2. Complete the assessment and achieve a grade of 80% or higher.
## Using the Colab/Jupyter Notebooks
All the hands-on labs are provided as Jupyter notebooks that can be run in Google Colab. Here’s how to use them:
1. Navigate to the appropriate week folder (e.g., `week_1`) to view the presentation and the summary of the week.
2. Locate the .ipynb file in the home page (e.g., `Week_1_&_2_Basics_of_Python.ipynb`) for the specific content.
- Find the `open in colab` button at the top of the notebook
- Click that to open the notebook in colab
3. Open a notebook in Google Colab by following these steps:
- Go to Google Colab at colab.research.google.com.
- Click on `File > Open Notebook`.
- Select the `GitHub` tab and enter your repository's URL.
- Browse to the notebook file you want to open and click on it.
4. Run each cell by clicking on the play button next to it.