Python ka Chilla for Data Science (40 Days of Python for Data Science)

About Course

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Course Content

Day-1: Welcome and Introduction
This topic will cover the basic introduction of this course and software installation.

Who is a Data Scientist?
03:20
Introduction to python ka chilla for Data Science
01:00:13
Installation of Software needed for the course
05:37
Write your first line of code
06:42

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-6 to Day-9: Pandas Library
You will learn basics to advanced use of Pandas library 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-14: ABC of Statistics for Data Science
In this section you will learn ABC of Statistics (From a beginner's perspective) with Desi Tarkay wali examples.

Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python

Day-16 to Day-25: Machine Learning
In This Section for next 10 Days we will Learn Machine Learning.

Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section

Day-27 to Day 35: Deep Learning with Tensorflow
We will learn using tensorflow for Deep Learning

Day-36: DashBoard and Web apps development for Data Science
In this Section we will learn how to make dashboard and webapps for Data Science in Python.

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.

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Student Ratings & Reviews

5.0

Total 3 Ratings

66 Ratings

8 Ratings

1 Rating

0 Rating

Sarwat jabeen

7 months ago

I have not seen such sincere effort of teaching before you from a Pakistani.

Faizan Ahmad

7 months ago

I would say this is by far the best course out there, it is fun, it is fast paced, it is a dense packed with knowledge course. Anyone and everyone who is looking forward to moving into this field should opt for this course ...
Thanks a lot to Sir Aammar Tufail ....

1 year ago

Very detailed

About Course

What Will You Learn?

Course Content

Day-1: Welcome and Introduction This topic will cover the basic introduction of this course and software installation.

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.

Data Visualization and Jupyter Notebooks

Day-5: MarkDown language You will learn whole MarkDown Language in this section.

Markdown Language Crash Course

Day-6 to Day-9: Pandas Library You will learn basics to advanced use of Pandas library in this section.

Pandas-Import Dataset and basics function

Pandas-More Pandas Functions

Pandas-Tips and Tricks (Part-1)

Pandas-Tips and Tricks (Part-2)

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.

Data Wrangling and Data Visualization

Day-11: Data Visualization in Python We will learn about Data Visualization in Python in details.

Data Visualization (Part-1)

Data Visualization (Part-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.

EDA (Part-1)

EDA (Part-2)

Day-14: ABC of Statistics for Data Science In this section you will learn ABC of Statistics (From a beginner's perspective) with Desi Tarkay wali examples.

ABC of Statistics (Part-1)

ABC of Statistics (Part-2)

ABC of Statistics (Part-3)

ABC of Statistics (Part-4)

ABC of Statistics (Part-5)

ABC of Statistics (Part-6)

ABC of Statistics (Part-7)

ABC of Statistics (Part-8)

ABC of Statistics (Part-9)

Day-15: Data Wrangling Techniques (Beginner to Pro) Data Wrangling in python

Python Data Wrangling Techniques: From Beginner to Pro

Day-16 to Day-25: Machine Learning In This Section for next 10 Days we will Learn Machine Learning.

What is Machine Learning?

Basics of Machine Learning

Regression

Classification

Machine Learning with Scikit-learn in Python

Hyperparameter Tuning with GridSearchCV

Machine Learning Terminologies

Machine Learning in another perspective

Cross Validation

Confusion Matrix

Sensitivity vs. Specificity

Bias Variance

Entropy

Linear Regression

Multiple Linear Regression

Logistic Regression

ROC and AUC

Maximum Likelihood

R-square for Logistic Regression

Ridge Regression

Lasso Regression

Elastic-Net Regression

Principal Component Analysis (PCA)

K-means Clustering

How to save and use a trained ML Model?

Practice on Machine Learning Real Projects

Day-26: How to use Conda Environments? We are going to learn conda environments and their use in this section

Conda Environments

Day-27 to Day 35: Deep Learning with Tensorflow We will learn using tensorflow for Deep Learning

Intro to Tensorflow

Neural Network

Intro to Computer Vision

Image Classification with TensorFlow

Activation Functions (Part-1)

Activation Functions (Part-2)

Project on Tensoflow

How many Epochs should we use?

Types of Neural Network

Day-36: DashBoard and Web apps development for Data Science In this Section we will learn how to make dashboard and webapps for Data Science in Python.

Introduction to Streamlit

My First Webapp

Use plotly in DashBoards

Animated Plots in Dashboard

EDA Webapp Development