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Python – Data Analytics – Real World Hands-on Projects

First step towards Data Science in this competitive job market
Data Science Lovers
15,053 students enrolled
English [Auto]
Big Data Analytics with Python
How we complete the tasks related to data analytics with python
Solving real time questions with Python Pandas Library
Learn Python Libraries - Pandas, Matplotlib and enhance your analytical skills
Core Python Programming Language
Basic Data Science
Download the Source Codes and Datasets of all projects
You will enjoy it !

In this course, we have uploaded 8 Data Analytics projects, solved with Python.

These projects are useful if you are looking for a starting level job as a Data Analyst.

These projects are useful for career transition into data analytics field.

If you are a student, you can use these projects to submit in college/institute.

The source code and datasets files are available to download.

All the projects are created with a very easy explanation.

We have mainly used the popular Python Pandas Library to solve these projects.

Kindly go through the description of each video lecture for more details.


The projects are :

Project 1 – Weather Data Analysis

Project 2 – Cars Data Analysis

Project 3 – Police Data Analysis

Project 4 – Covid Data Analysis

Project 5 – London Housing Data Analysis

Project 6 – Census Data Analysis

Project 7 – Udemy Data Analysis

Project 8 – Netflix Data Analysis


Some examples of commands used in these projects are :

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

* 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.

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