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)
    Join the conversation
    Shagufta Perveen 3 weeks ago
    sir can you share link for "andrew abela guide for plotting "
    Reply
    Abdullah Abrar 1 month ago
    Best video Ever
    Reply
    Abdullah Abrar 1 month ago
    Love Your Videos
    Mehwish Khan 2 months ago
    awesome
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    qamar alam 2 months ago
    love it
    Reply
    Ghayas uddin 6 months ago
    # ADD column of family size df['family_size']=pd.cut(df['parch']+ df['sibsp']+ 1 , [0,1,2,6], labels=['single','couple','parents'])
    Reply
    Ghayas uddin 6 months ago
    FOR LMPLOT: sns.lmplot( x='age', y='fare', data=df[df['fare']<300]) plt.show()
    Reply
    Muhammad Akhtar 6 months ago
    df['family_size'] = df['sibsp'] + df['parch'] + 1
    Reply
    Zohaib Hassan 7 months ago
    sns.lmplot(x='fare', y='age', data=df[df['fare'] < 300], hue='pclass') plt.show()
    Reply
    qamar alam 2 months ago
    good
    shahid khan 8 months ago
    very good
    Reply
    Azeem Log 8 months ago
    1:20:15 Assignment done! sns.Implot(data=df.loc[df["fare"] < 250], x="age", y="fare")
    Reply