Course Content
Day-2: How to use VScode (an IDE) for Python?
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Day-3: Basics of Python Programming
This section will train you for Python programming language
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Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
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Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
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Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
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Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
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Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
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Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
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Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
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Day-39: git and github
We will learn about git and github
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Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
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Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Here are some key reasons why 📝 Markdown language is important for Jupyter notebooks:

  1. Documentation: Markdown allows you to write documentation and explanations for your code and analysis within the same Jupyter notebook. This helps provide context to your work. 📖

  2. Readability: With formatting options like headings, bold, italics etc., Markdown makes your notes more readable and understandable compared to plain text. 📚

  3. Sharing: When you share Jupyter notebooks, the Markdown formatting is retained. This allows others to easily understand your work without requiring any additional documentation. 📩

  4. Updatability: As you update or refine your code and analysis over time, you can also update the Markdown cells. This keeps your documentation in sync with the latest version. ✅

  5. Platform Support: Major open-source platforms like GitHub, HuggingFace and many others support and recognize Markdown formatting in Jupyter notebooks. This improves portability of your work. 💻

  6. Multi-purpose Usage: Markdown cells are useful for documentation, explanations, visualizations, findings, conclusions and any other text-based contents in your notebooks. ✏️

  7. Lightweight: Markdown syntax is very easy to read and write. It doesn’t require much space or processing power compared to other markup languages. 📝

In summary, Markdown allows seamless integration of documentation with code in Jupyter notebooks, improving readability, reusability and sharing of data science projects. This makes it an essential part of the Jupyter notebook workflow. 📕

Join the conversation
Marium Iqbal 1 month ago
mark down is really interesting. Thank you
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Marium Iqbal 1 month ago
I am new in data sciences, the material you are providing and the efforts you put in are commendable
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Marium Iqbal 1 month ago
we can comment out in markdown by pressing ctrl +/ keys simultaneously
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Mehwish Khan 2 months ago
I learn markdown in three days ooops.
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Hanzala Rehman 4 months ago
space nhi dia
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Farrukh Ilyas 4 months ago
Baba Amaar has made markdown language really easy to understand. Jazak Allah! :)
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Bushra S 4 months ago
Amazing lecture, Thank you very much!
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Abdul Samad 5 months ago
Sir g mei apka fan hu gya hn bht acha smjhaty hn ap Allah apko hmesha khush rkhy
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Farhan Ahmad 5 months ago
bht acha method ha apk perhnay ka bhai. thanks a lot Dr shb.
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muhammad farhan safdar 5 months ago
mark down is very easy to understand with baba ji Dr Aammar
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