Course Content
Day-1: Welcome and Introduction
This topic will cover the basic introduction of this course and software installation.
0/4
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?
0/1
Learn VScode for python
01:01:41
Day-3: Basics of Python Programming
This section will train you for Python programming language
0/4
Python-101
01:03:45
Python-101 Extended
01:55:14
Python-101 Extended (Again)
01:55:14
Python-101-Beyond functions
01:04:46
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
0/1
Data Visualization and Jupyter Notebooks
01:16:56
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
0/1
Markdown Language Crash Course
01:11:23
Day-6 to Day-9: Pandas Library
You will learn basics to advanced use of Pandas library in this section.
0/4
Pandas-Import Dataset and basics function
01:15:43
Pandas-More Pandas Functions
11:31
Pandas-Tips and Tricks (Part-1)
02:05:25
Pandas-Tips and Tricks (Part-2)
01:19:44
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
Data Wrangling and Data Visualization
01:25:46
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
0/2
Data Visualization (Part-1)
16:06
Data Visualization (Part-2)
27:55
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
EDA (Part-1)
01:17:20
EDA (Part-2)
01:51:15
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.
0/9
ABC of Statistics (Part-1)
34:23
ABC of Statistics (Part-2)
01:01:44
ABC of Statistics (Part-3)
59:08
ABC of Statistics (Part-4)
58:53
ABC of Statistics (Part-5)
01:04:17
ABC of Statistics (Part-6)
01:00:39
ABC of Statistics (Part-7)
58:21
ABC of Statistics (Part-8)
00:00
ABC of Statistics (Part-9)
01:13:54
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
0/1
Python Data Wrangling Techniques: From Beginner to Pro
01:59:35
Day-16 to Day-25: Machine Learning
In This Section for next 10 Days we will Learn Machine Learning.
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What is Machine Learning?
01:29:04
Basics of Machine Learning
01:39:00
Regression
01:06:06
Classification
01:40:26
Machine Learning with Scikit-learn in Python
23:33
Hyperparameter Tuning with GridSearchCV
01:09:37
Machine Learning Terminologies
50:34
Machine Learning in another perspective
10:56
Cross Validation
11:18
Confusion Matrix
09:21
Sensitivity vs. Specificity
06:55
Bias Variance
08:04
Entropy
19:13
Linear Regression
13:55
Multiple Linear Regression
07:17
Logistic Regression
09:25
ROC and AUC
19:11
Maximum Likelihood
15:52
R-square for Logistic Regression
07:11
Ridge Regression
12:58
Lasso Regression
04:41
Elastic-Net Regression
04:56
Principal Component Analysis (PCA)
17:54
K-means Clustering
12:55
How to save and use a trained ML Model?
34:31
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
0/1
Conda Environments
54:41
Day-27 to Day 35: Deep Learning with Tensorflow
We will learn using tensorflow for Deep Learning
0/9
Intro to Tensorflow
01:23:36
Neural Network
20:34
Intro to Computer Vision
00:00
Image Classification with TensorFlow
40:36
Activation Functions (Part-1)
42:04
Activation Functions (Part-2)
01:15:30
Project on Tensoflow
33:30
How many Epochs should we use?
25:16
Types of Neural Network
17:36
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.
0/14
Introduction to Streamlit
42:29
My First Webapp
36:20
Use plotly in DashBoards
12:21
Animated Plots in Dashboard
02:18
EDA Webapp Development
23:39
Streamlit k Jugar
07:49
ML Web App with Streamlit
19:27
WebApp Deployment on Streamlit
08:47
Add Media files to webapps
11:04
Add Codes to your webapps
03:48
Make Interactive Explainer DashBoard
07:00
Embed code snippets into DashBoards
09:13
Streamlit Project for Data Science (Part-1)
01:11:49
Streamlit Project for Data Science (Part-2)
24:04
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
0/2
Time Series Analysis (Part-1)
01:05:14
Time Series Analysis (Part-2)
24:11
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
0/2
NLP and Text Classification project
34:14
Tensorboard
41:00
Day-39: git and github
We will learn about git and github
0/1
All about git and GitHub
47:41
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
ChatGPT for Twitter and other Social Media Platforms
40:55
Feed Back
0/1
FeedBack and Review on the course
00:00
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Ap ko pata hy kia k Data Visualization bht zaroori hy.

Data visualization ka bahot zaroori kaam hai doston. Agar aapko data ko dekhne se samajhna hai to data visualization ki madad se hum data ko visual form mein present kar sakte hain. Ye hume data me se pattern nikalne me aur analysis karne me madad karta hai.

Python me bohot sare libraries hain jo data visualization karne me madad karte hain. Mujhe pata hai ki matlab sahi nahi hoga agar main unn sabka naam likhunga to main kuch popular wale batata hoon:

  • Matplotlib: Ye sabse popular aur comprehensive library hai. Isse hum 2D v 3D plots banane me madad milta hai.

  • Seaborn: Matplotlib pe based hai par usse data visualization ko moddern feel deta hai through aesthetic default styles.

  • Plotly: interactive plots banane ke liye bahot accha hai.

  • Bokeh: Interactive plots aur dashboards banane me bahot accha hai jisse analysis ko samajhne me madad milta hai.

  • Pandas: Dataframes pe operate karne ke liye bahot helpful hai aur usse basic plots banane me bhi madad karta hai.

  • Pygal: SVG ko render karne wali library hai jisse themes pe based fancy charts banane me madad milta hai.

To bass Pakistani bhaiyonor un ki behnon (just kidding), agar aap data analysis karna chahte ho to zaroor data visualization use karlena taki data samajhne me aasaan ho. Haan, aur Python me bohot saare library hain jinse aap graphics bana sakte ho!

Join the conversation
Salman Ahmed 5 months ago
SIR MEIN AP KA COURSE ABHI KR RAHA HOON MEIN DATA LIABRARY KESAY DOWNLOAD KARO
Reply
Ghayas uddin 7 months ago
QUESTION: What is the full form Pandas? ANSWER: Pandas= Python Data Analysis.
Reply
Nabeel Ahmad 7 months ago
pandas stands for Python data analysis
Reply
Aiman Mukhtar 7 months ago
can you please send the link here too
Reply
Abdul Ghaffar 9 months ago
intalled libraries pandas, numpy, matplotlib, seaborn, plotly, scipy, scikit-learn, streamlit but one by one
Reply
Altaf Hussain 9 months ago
How to install pip
Reply
Muhammad Ikram 2 months ago
cmd m install krein. pip install pip
Umer Riaz 9 months ago
Aslam o Alaikum Sir? You have done Great Job. Sir kindly data ka Source link yahan b send kr dain. jahan se ap plot draw kr re hain
Reply
Jamal Naseem 10 months ago
Salam, I was watching your videos you shared the link with students, May i know the link where you got the code ?
Reply
Aqib Sheraz 10 months ago
Done
Reply
Aqib Sheraz 10 months ago
12/10/2023
Rana Adnan 10 months ago
Hyeeeeeee😍😍 Ye graph tou kamal hain 🥹
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