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?
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Learn VScode for python
01:01:41
Day-3: Basics of Python Programming
This section will train you for Python programming language
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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.
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Data Visualization and Jupyter Notebooks
01:16:56
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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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.
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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.
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Data Wrangling and Data Visualization
01:25:46
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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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.
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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.
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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
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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
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Conda Environments
54:41
Day-27 to Day 35: Deep Learning with Tensorflow
We will learn using tensorflow for Deep Learning
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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.
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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.
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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

Some Advanced Pandas functions are taught here.

Join the conversation
Amina Rasheed 6 months ago
in contrast, kindly any one here can guide type casting in Pandas
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Amina Rasheed 6 months ago
in contrast, kindly any one here can guide type casting in Pandas
Reply
Omar Rashed 9 months ago
Assalamualikum sir. Assignment1: df = df.assign(height = pd.to_numeric(df['height'])) df.weight=df.weight.astype('int64') df.Favourite_Dish=df.Favourite_Dish.astype("category")
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Sheikh Irfan Ullah Khan 9 months ago
Asalam-0-Alaikum Dr Sab I have found these results after running the code of df.describe()height weights count 87.000000 87.000000 mean 5.547931 70.459770 std 0.333321 16.753899 min 5.000000 25.000000 25% 5.250000 60.000000 50% 5.600000 70.000000 75% 5.800000 80.000000 max 6.200000 125.000000These are different from your results. Is it right?
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Akhtar Alvi 9 months ago
Same here with df.describe(). Any solution
TAHA EHSAN ULLAH 10 months ago
After type casting: _____df.sort_values(by="weight")______________________________________height weight favourite_dish _____________________________________ 5.0 25 Biryani_______________________________________________________ 5.0 31 Biryani_______________________________________________________ 5.2 48 Biryani_______________________________________________________ 5.6 49 Chicken Pulao_______________________________________________ 5.0 50 Chicken Pulao
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TAHA EHSAN ULLAH 10 months ago
df.column() with paranthesis aik function ki trah deal krta hy jo k Atribute error deta hy jbky df.column dataframe mein individual columns ko access or onky upar kam krny k lye use hota hy
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TAHA EHSAN ULLAH 10 months ago
*Correction* its df.columns and it is used to show you the labels of all the columns in your table. It returns a list of the column names as text.
Sana Shah 10 months ago
sana your student
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Mohsin Muntazir 10 months ago
Type Casting is already done in previous video sir why you are asking for wrangling again ?
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