15 Python Data Science Real World Hands-on Projects

In this video, you will learn how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python.

It is a project of Data Analysis with Python or you can say, Data Science with Python.

The commands that we used in this project :

* head() - It shows the first N rows in the data (by default, N=5).

* shape - It shows the total no. of rows and no. of columns of the dataframe

* index - This attribute provides the index of the dataframe

* columns - It shows the name of each column

* dtypes - It shows the data-type of each column

* unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.

* nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.

* count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe.

* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.

* info() - Provides basic information about the dataframe.

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Q. 1) Find all the unique 'Wind Speed' values in the data.

Q. 2) Find the number of times when the 'Weather is exactly Clear'.

Q. 3) Find the number of times when the 'Wind Speed was exactly 4 km/h'.

Q. 4) Find out all the Null Values in the data.

Q. 5) Rename the column name 'Weather' of the dataframe to 'Weather Condition'.

Q. 6) What is the mean 'Visibility' ?

Q. 7) What is the Standard Deviation of 'Pressure' in this data?

Q. 8) What is the Variance of 'Relative Humidity' in this data ?

Q. 9) Find all instances when 'Snow' was recorded.

Q. 10) Find all instances when 'Wind Speed is above 24' and 'Visibility is 25'.

Q. 11) What is the Mean value of each column against each 'Weather Condition ?

Q. 12) What is the Minimum & Maximum value of each column against each 'Weather Condition ?

Q. 13) Show all the Records where Weather Condition is Fog.

Q. 14) Find all instances when 'Weather is Clear' or 'Visibility is above 40'.

Q. 15) Find all instances when :

A. 'Weather is Clear' and 'Relative Humidity is greater than 50' or B. 'Visibility is above 40'

In this video, you will learn how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python.

It is a project of Data Analysis with Python or you can say, Data Science with Python.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library

* pd.read_csv - To import the CSV file in Jupyter notebook

* head() - It shows the first N rows in the data (by default, N=5)

* shape - It shows the total no. of rows and no. of columns of the dataframe

* df.isnull( ).sum( ) - It detects the missing values from each column of the dataframe

* fillna() - To fill the null values of a column with some particular value

* value_counts - In a column, it shows all the unique values with their count. It can be applied to a single column only

* isin() - To show all records including particular elements

* apply() - To apply a function along any axis of DF

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Q. 1) Instruction ( For Data Cleaning ) - Find all Null Values in the dataset. If there is any null value in any column, then fill it with the mean of that column.

Q. 2) Question ( Based on Value Counts )- Check what are the different types of Make are there in our dataset. And, what is the count (occurrence) of each Make in the data ?

Q. 3) Instruction ( Filtering ) - Show all the records where Origin is Asia or Europe.

Q. 4) Instruction ( Removing unwanted records ) - Remove all the records (rows) where Weight is above 4000.

Q. 5) Instruction ( Applying function on a column ) - Increase all the values of 'MPG_City' column by 3.

In this video, you will learn how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python.

It is a project of Data Analysis with Python or you can say, Data Science with Python.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library

* pd.read_csv - To import the CSV file in Jupyter notebook

* head() - It shows the first N rows in the data (by default, N=5)

* df.isnull( ).sum( ) - It detects the missing values from each column of the dataframe.

* df.drop(‘Col_name’ ) - To drop a column from dataframe.

* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only

* df.groupby(‘Col_1’)[‘Col_2’] .sum( ) - To create groups - Two Keys – Apply on Col_2 grouped by Col_1

* df['Column_name'].map( { old1:new1 , old2:new2} ) – Change the all values of a column from old to new. We have to write for all values of column otherwise Nan will appear.

* df['Column_name'].mean() - To show Mean value of a column.

* df.groupby('Column_1').Column_2.describe() - To create groups based on Column1 and show statistics summary based on Column2.

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Q. 1) Instruction ( For Data Cleaning ) - Remove the column that only contains missing values.

Q. 2) Question ( Based on Filtering + Value Counts ) - For Speeding , were Men or Women stopped more often ?

Q. 3) Question ( Groupby ) - Does gender affect who gets searched during a stop ? Question ( mapping + data-type casting )

Q. 4) Question ( mapping + data-type casting ) - What is the mean stop_duration ?

Q. 5) Question ( Groupby , Describe ) - Compare the age distributions for each violation.

In this video, a mini dataset related to the Covid-19 pandemic is taken and analysed in a very Easy To Understand (ETU) language.

Here, you will learn how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library

* pd.read_csv - To import the CSV file in Jupyter notebook

* df.count() - It counts the no. of non-null values of each column

* df.isnull().sum() - It detects the missing values from the dataframe.

* import seaborn as sns - To import the Seaborn library.

* import matplotlib.pyplot as plt - To import the Matplotlib library.

* sns.heatmap(df.isnull()) - It will show the all columns & missing values in them in heat map form.

* plt.show() - To show the plot.

* df.groupby(‘Col_name’) - To form groups of all unique values of the column.

* df.sort_values(by= ['Col_name'] ) - Sort the entire dataframe by the values of the given column.

* df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1.

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Q. 1) Show the number of Confirmed, Deaths and Recovered cases in each Region.

Q. 2) Remove all the records where the Confirmed Cases is Less Than 10.

Q. 3) In which Region, maximum number of Confirmed cases were recorded ?

Q. 4) In which Region, minimum number of Deaths cases were recorded ?

Q. 5) How many Confirmed, Deaths & Recovered cases were reported from India till 29 April 2020 ?

Q. 6-A ) Sort the entire data wrt No. of Confirmed cases in ascending order.

Q. 6-B ) Sort the entire data wrt No. of Recovered cases in descending order.

In this video, the London Housing Data is analyzed in a very Easy To Understand (ETU) language.

Questions are given in the project and then solved with the help of Python.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library

* pd.read_csv - To import the CSV file in Jupyter notebook

* df.count() - It counts the no. of non-null values of each column.

* df.isnull().sum() - It detects the missing values from the dataframe.

* import seaborn as sns - To import the Seaborn library.

* import matplotlib.pyplot as plt - To import the Matplotlib library.

* sns.heatmap(df.isnull()) - It will show the all columns & missing values in them in heat map form.

* plt.show() - To show the plot.

* df.dtypes - To show the datatype of each column.

* pd.to_datetime(df.Date_Time_Col) - Converts the data-type of Date-Time Column into datetime[ns] datatype.

* df.Time_Col.dt.year - Creating a new column with only year values

* df.Time_Col.dt.month - Creating a new column with only month values.

* df.insert( index , ‘new_column_name’, new_column_values) - To insert a New column at a particular position with values in it.

* df.drop - To drop a column from the dataframe permanently.

* df.groupby(‘Col_name’) - To form groups of all unique values of the column.

* df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1.

* df.groupby(‘Col_1’)[‘Col_2’] .mean( ) - To create groups - Two Keys – Apply on Col_2 grouped by Col_1.

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Q. 1) Convert the Datatype of 'Date' column to Date-Time format.

Q. 2) Add a new column ''year'' in the dataframe, which contains years only.

(2.B) Add a new column ''month'' as 2nd column in the dataframe, which contains month only.

Q. 3) Remove the columns 'year' and 'month' from the dataframe.

Q. 4) Show all the records where 'No. of Crimes' is 0. And, how many such records are there ?

Q. 5) What is the maximum & minimum 'average_price' per year in england ?

Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ?

Q. 7) Show the total count of records of each area, where average price is less than 100000.

In this video, India Census 2011 data is analyzed in a very Easy To Understand (ETU) language.

Here, you will learn about the python commands & how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python commands.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library

* pd.read_csv - To import the CSV file in Jupyter notebook

* style.hide_index( ) - To hide the index of the dataframe.

* style.set_caption('Description of the dataframe') - To give a caption to the dataframe.

* isin( ) - To show all records including particular elements.

* groupby(‘Col_1’)[‘Col_2’] .sum( )[‘value’] - GroupBy – Two Keys – Apply on Col_2 grouped by Col_1.

* df[df.Col_1 == 'Element1']['Col_2'] - Filtering - Filter the records of the dataframe wrt to Element1 of Col1 and then showing results of Col2 only.

* set_index( ‘Col_Name’ ) - To set any column of a DF as an index.

* add_prefix(‘value_’) - To add prefix to the column name.

* add_suffix(‘_value’) - To add suffix to the column name.

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Q. 1) How will you hide the indexes of the dataframe.

Q. 2) How can we set the caption / heading on the dataframe.

Q. 3) Show the records related with the districts - New Delhi , Lucknow , Jaipur.

Q. 4) Calculate state-wise :

A. Total number of population.

B. Total no. of the population with different religions.

Q. 5) How many Male Workers were there in Maharashtra state ?

Q. 6) How to set a column as index of the dataframe ?

Q. 7a) Add a Suffix to the column names. Q. 7b) Add a Prefix to the column names.

In this video exercise, Udemy Courses Dataset is analyzed in a very Easy To Understand (ETU) language.

Here, you will learn about the python commands & how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python functions/commands.

The commands that we used in this project :

* import pandas as pd -- To import Pandas library.

* pd.read_csv - To import the CSV file in Jupyter notebook.

* head() - It shows the first N rows in the data (by default, N=5).

* unique( ) - It shows the all unique values of the column.

* value_counts - In a column, it shows all the unique values with their count. It can be applied to a single column only.

* df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1.

* df.sort_values(‘Col_name' , ascending=False ) - To sort the dataframe wrt any column values in descending order.

* df[ (df.Col1 = = ‘Element1’) & (df.Col2 == ‘Element2’) ] - Multilevel filtering - And Filter – Filtering the data with two & more items.

* str.contains('Value_to_match’) - To find the records that contains a particular string.

* dtypes - To show datatypes of each column.

* pd.to_datetime(df.Date_Time_Col) - To convert the data-type of Date-Time Column into datetime[ns] datatype.

* dt.year - Creating a new column with only year values.

* df.groupby(‘Col_1’)['Col_2'].max() - Using groupby with two different columns.

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Q. 1) What are all different subjects for which Udemy is offering courses ?

Q. 2) Which subject has the maximum number of courses.

Q. 3) Show all the courses which are Free of Cost.

Q. 4) Show all the courses which are Paid.

Q. 5) Which are Top Selling Courses ?

Q. 6) Which are Least Selling Courses ?

Q. 7) Show all courses of Graphic Design where the price is below 100 ?

Q. 8) List out all the courses that are related to 'Python'.

Q. 9) What are courses that were published in the year 2015 ?

Q. 10) What is the Max. Number of Subscribers for Each Level of courses ?

In this video, you will learn how to work on a real project of Data Analysis with Python.

Questions are given in the project and then solved with the help of Python.

It is a project of Data Analysis with Python or you can say, Data Science with Python.

The commands that we used in this project :

* head() - It shows the first N rows in the data (by default, N=5).

* tail () - It shows the last N rows in the data (by default, N=5).

* shape - It shows the total no. of rows and no. of columns of the dataframe.

* size - To show No. of total values(elements) in the dataset.

* columns - To show each Column Name.

* dtypes - To show the data-type of each column.

* info() - To show indexes, columns, data-types of each column, memory at once.

* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.

* unique() - It shows the all unique values of the series.

* nunique() - It shows the total no. of unique values in the series.

* duplicated( ) - To check row wise and detect the Duplicate rows.

* isnull( ) - To show where Null value is present.

* dropna( ) - It drops the rows that contains all missing values.

* isin( ) - To show all records including particular elements.

* str.contains( ) - To get all records that contains a given string.

* str.split( ) - It splits a column's string into different columns.

* to_datetime( ) - Converts the data-type of Date-Time Column into datetime[ns] datatype.

* dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column.

* groupby( ) - Groupby is used to split the data into groups based on some criteria.

* sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph.

* max( ), min( ) - It shows the maximum/minimum value of the series.

* mean( ) - It shows the mean value of the series.

You will learn these things also: Creating New Columns & Dataframe Filtering (Single Column & Multiple Columns) Filtering with And and OR Seaborn Library - Bar Graphs

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Task. 1) Is there any Duplicate Record in this dataset ? If yes, then remove the duplicate records.

Task. 2) Is there any Null Value present in any column ? Show with Heat-map.

Q. 1) For 'House of Cards', what is the Show Id and Who is the Director of this show ?

Q. 2) In which year the highest number of the TV Shows & Movies were released ? Show with Bar Graph.

Q. 3) How many Movies & TV Shows are in the dataset ? Show with Bar Graph.

Q. 4) Show all the Movies that were released in year 2000.

Q. 5) Show only the Titles of all TV Shows that were released in India only.

Q. 6) Show Top 10 Directors, who gave the highest number of TV Shows & Movies to Netflix ?

Q. 7) Show all the Records, where "Category is Movie and Type is Comedies" or "Country is United Kingdom".

Q. 8) In how many movies/shows, Tom Cruise was cast ?

Q. 9) What are the different Ratings defined by Netflix ?

Q. 9.1) How many Movies got the 'TV-14' rating, in Canada ?

Q. 9.2) How many TV Shows got the 'R' rating, after year 2018 ?

Q. 10) What is the maximum duration of a Movie/Show on Netflix ?

Q. 11) Which individual country has the Highest No. of TV Shows ?

Q. 12) How can we sort the dataset by Year ?

Q. 13) Find all the instances where: Category is 'Movie' and Type is 'Dramas' or Category is 'TV Show' & Type is 'Kids' TV'.

In this video, you will learn how to do Sales Data Analysis with Python. First the data is cleaned and modified, then the questions are given in the project that are solved with the help of Python. It is a project of Data Analysis with Python or you can say, Data Science with Python.

The commands that we used in this video :

* reset_index() - To convert the index of a Series into a column to form a DataFrame.

* loc[ ] - To show any row's values.

* info() - To provide the basic information about the dataframe.

* drop() - To drop any column or row from the dataframe.

* str.strip().str.replace(r'\s+', ' ', regex=True) - To remove extra spaces in any text column.

* duplicated() - To show all the duplicate records from a dataframe.

* drop_duplicates(inplace=True) - To remove the duplicate records from the dataframe.

* round() - To round-off the values of a numerical column.

* pd.to_datetime() - To convert the datatype of date column into datetime format.

* groupby() - To make the group of all unique values of a column.

* std()  - To check the standard deviation of any numerical column.

* var()  - To check the variance of any numerical column.

* mean()  - To check the mean of any numerical column.

* agg() - Using agg() with groupby().

* head() - It shows the first N rows in the data (by default, N=5).

* columns - To show all the column names of the dataframe.

* unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.

* nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.

* describe() - To show some summary about the columns.

* astype() - To change the datatype of any column.

* dtype - To check the datatype of any column.

* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.

* plot(kind='bar') - To draw the bar graph.

* type() - To the type of any variable.

* plt.figure(figsize = ()) - To set the size of any figure.

* plt.title(), plt.xlabel(), plt.ylabel() - To set the Title, x-axis label, y-axis label.

* sort_values(ascending = False) - To sort the values in descending order.

* dt.month - To create a new column showing Month only.

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Q.1) What was the Most Preferred Payment Method ?

Q.2) Which one was the Most Selling Product

- By Quantity

- By Revenue

Q.3) Which City had maximum revenue, or, Which Manager earned maximum revenue ?

Q.4) Show the Date wise revenue.

Q.5) What was the Average Revenue ?

Q.6) What was the Average Revenue of November & December month ?

Q.7) What was the  Standard Deviation of Revenue and Quantity ?

Q.8) What was the Variance of Revenue and Quantity ?

Q.9) Was the revenue increasing or decreasing over the time?

Q.10) What was the Average 'Quantity Sold' & 'Average Revenue' for each product ?

In this video, we have analysed the big dataset of Spotify & YouTube.

There are multiple questions given that we answered with Python.

Q.1) Top 10 Artists - with the Highest Views on YouTube?

Q.2) Top 10 Tracks - with the Highest Streams on Spotify?

Q.3) What are the most common Album Types on Spotify? How many tracks belong to each album type?

Q.4) How do the Average Views, Likes, and Comments are compared between different Album Types?

Q.5) Top 5 YouTube Channels - based on the Views?

Q.6) The Top Most Track - based on Views?

Q.7) Which Top 7 Tracks have the highest Like-to-View ratio on YouTube?

Q.8) Top Albums having the Tracks with Maximum Danceability ?

Q.9) What is the Correlation between Views, Likes, Comments, and Stream?

In this video, we have analysed the big dataset related to various Airlines' Flights.

Here, The Flights Booking Dataset of various Airlines is a scraped date-wise from a famous website in a structured format. The dataset contains the records of flight travel details between the cities in India.

Here, multiple features are present like Source & Destination City, Arrival & Departure Time, Duration & Price of the flight etc.

This data is available as a CSV file.

We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Airlines, Travel domain.

There are multiple questions given that we answered with Python :

Q.1 What are the airlines in the dataset, accompanied by their frequencies?

Q.2. Show Bar Graphs representing the Departure Time & Arrival Time.

Q.3. Show Bar Graphs representing the Source City & Destination City.

Q.4. Does price varies with airlines ?

Q.5. Does ticket price change based on the departure time and arrival time?

Q.6. How the price changes with change in Source and Destination?

Q.7. How is the price affected when tickets are bought in just 1 or 2 days before departure?

Q.8. How does the ticket price vary between Economy and Business class?

Q.9. What will be the Average Price of Vistara airline for a flight from Delhi to Hyderabad in Business Class ?

In this video, we have analysed the big dataset related to financial market of AI companies. This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI).

There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc.

Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating.

The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file.

We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

In this video, we have analysed the big dataset with Python.

This dataset contains HR information for employees of a multinational corporation (MNC).

It includes 2 Million (20 Lakhs) employee records with details about personal identifiers, job-related attributes, performance, employment status, and salary information.

The dataset can be used for HR analytics, including workforce distribution, attrition analysis, salary trends, and performance evaluation.

This data is available as a CSV file.

We are going to analyze this data set using the Pandas.

This analyse will be helpful for those working in HR domain.

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There are multiple questions given that we answered with Python :

Q.1) What is the distribution of Employee Status (Active, Resigned, Retired, Terminated) ?

Q.2) What is the distribution of work modes (On-site, Remote) ?

Q.3) How many employees are there in each department ?

Q.4) What is the average salary by Department ?

Q.5) Which job title has the highest average salary ?

Q.6) What is the average salary in different Departments based on Job Title ?

Q.7) How many employees Resigned & Terminated in each department ?

Q.8) How does salary vary with years of experience ?

Q.9) What is the average performance rating by department ?

Q.10) Which Country have the highest concentration of employees ?

Q.11) Is there a correlation between performance rating and salary ?

Q.12) How has the number of hires changed over time (per year) ?

Q.13) Compare salaries of Remote vs. On-site employees — is there a significant difference ?

Q.14) Find the top 10 employees with the highest salary in each department.

Q.15) Identify departments with the highest attrition rate (Resigned %).

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These are the main Features/Columns available in the dataset :

1) Unnamed: 0 – Index column (auto-generated, not useful for analysis, will be deleted).

2) Employee_ID – Unique identifier assigned to each employee (e.g., EMP0000001).

3) Full_Name – Full name of the employee.

4) Department – Department in which the employee works (e.g., IT, HR, Marketing, Operations).

5) Job_Title – Designation or role of the employee (e.g., Software Engineer, HR Manager).

6) Hire_Date – The date when the employee was hired by the company.

7) Location – Geographical location of the employee (city, country).

8) Performance_Rating – Performance evaluation score (numeric scale, higher is better).

9) Experience_Years – Number of years of professional experience the employee has.

10) Status – Current employment status (e.g., Active, Resigned).

11) Work_Mode – Mode of working (e.g., On-site, Hybrid, Remote).

12) Salary_INR – Annual salary of the employee in Indian Rupees.

In this video, we work on a real-world data analytics project using Python, where we analyze a Salary Dataset with 22,000+ records. This project is perfect for beginners to intermediate learners who want to build strong hands-on skills in Data Analysis, Python, and Data Science.

You will learn the complete data analysis workflow, just like in real industry projects:

✅ Data Understanding & Exploration (EDA)

✅ Data Cleaning & Handling Duplicates

✅ Outlier Detection using IQR Method

✅ Data Visualization using Matplotlib & Seaborn

✅ Business Questions & Insights

✅ Correlation Analysis

✅ Advanced Charts (Scatter, Line, Histogram, Dashboard)

✅ Final Mini Dashboard

✅ Portfolio-Ready Project

You will learn how to use popular Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn to analyze patterns in salary data across job roles, locations, companies, and employment types. The project also includes nine real-world business questions that help you understand how data-driven decision-making works in practice.

In this project, you will work with a real-world Quick Commerce dataset to understand how modern instant delivery businesses operate using data. The lecture walks you through a complete data analysis workflow using Python, covering data exploration, cleaning, visualization, and business-driven analysis.

You will learn how to analyze key aspects of a quick commerce platform such as order patterns, delivery performance, product categories, customer behavior, and revenue trends. The project makes extensive use of popular Python libraries including Pandas, NumPy, Matplotlib, and Seaborn to extract meaningful insights from raw data.

By the end of this lecture, you will be able to:

• Load and explore real business datasets using Python

• Clean and preprocess data for analysis

• Identify trends and patterns in customer orders

• Analyze delivery time, order volume, and revenue

• Create informative visualizations to support business decisions

• Answer practical business questions using data

This project is ideal for beginners in data analytics, aspiring data scientists, and anyone interested in understanding how data drives decision-making in the fast-growing quick commerce industry.