Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
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.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Key reasons why creating conda environments is necessary/useful:

  • Dependency and version management: Conda environments allow you to isolate different projects that may have conflicting dependencies or require different versions of packages. This avoids dependency clashes.

  • Reproducibility: Environments make it easy to exactly replicate the software environment used for a project/analysis. This ensures reproducible results.

  • Code sharing: Environments allow sharing code/notebooks with others while bundling all dependencies. Avoid issues caused by missing or wrong package versions.

  • Testing different stacks: You can test different package combinations and Python/IPython versions easily using separate environments without interfering with system setup.

  • Package conflicts: Some packages conflict in how they use or install dependencies. Environments prevent such issues from arising.

  • Clean project separation: Keeping each project activitiesisolated in its own environment prevents namespace pollution and clutter. Easy to switch between projects.

  • System stability: Environments avoid the risk of new package versions breaking existing projects or the system Python setup.

  • Temporary setups: Useful for experimenting with packages without permanently changing the system configuration. Easy clean up by deleting env.

So in summary, conda environments make dependency and version management clean and reproducible for both development and production environments.

Install miniconda from here

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komal Baloch 8 months ago
Syed Abdul Qadir Gilani 9 months ago