Course Content
Day-2: How to use VScode (an IDE) for Python?
0/1
Day-3: Basics of Python Programming
This section will train you for Python programming language
0/4
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
0/1
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
0/1
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.
0/1
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
0/2
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.
0/2
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
0/1
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
0/1
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
0/2
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
0/2
Day-39: git and github
We will learn about git and github
0/1
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.
0/1
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

The Code is available here

 
Join the conversation
Muhammad Tufail 4 months ago
Thanks for so comprehensive Leactures
Reply
shafiq ahmed 6 months ago
X = pd.get_dummies(X, columns=['sex'])
Reply
shafiq ahmed 6 months ago
df['age'].fillna(df['age'].mean(),inplace=True)
Reply
shafiq ahmed 6 months ago
age columns main nan values hain
Reply
shafiq ahmed 6 months ago
how we share my notebook in github
Reply
shafiq ahmed 6 months ago
github ka link please share kar dain
Reply
Mamoon Abdullah 8 months ago
X.age.fillna(value = X['age'].mean(), inplace=True)
Reply