Sales data Analysis in Python

Name: Sales data Analysis in Python
Rating: 4.3 (57 reviews)

Indian sales data analysis, data analysis using seaborn and pyplot in python, Data visualization in python

Created byHarman Waheed

Last updated 6/2024

English

English [Auto],

What you'll learn

How to analyze large dataset in python
How to visually see important components of data
In-depth knowledge of patterns of seaborn in python
Graphs handling through matplotlib

Course content

3 sections • 19 lectures • 39m total length

What you need to start0:25
What in this course0:48
Dataset we will be working with0:12
Downloading dataset2:26
Download the Indian sales data from Kaggle, extract the zip, and explore a dataset of about 11,000 rows and 13 columns, including customer ID, gender, age group, and amount spent.

Importing data in python0:33
Importing dataset in python environment5:18
Load your dataset in a Python environment (preferably Jupyter), import pandas and matplotlib, read a CSV or XLS, set the customer ID as index, and install required packages if needed.
Dealing with missing data0:34
Dealing with missing data video4:22
Identify and handle missing data in a sales data set using python: inspect nulls with isnull and sum, then drop rows with missing values.
Plotting graphs, Extracting data1:08
Plotting graphs in python, video5:16
Use seaborn in python to create count plots that analyze sales by marital status, gender, and zone; reveal that unmarried customers shop more and central zone leads.
Plotting graphs in python again, video5:03
Plot readable charts in Python by increasing figure size and rotating x-axis labels. The graphs show the market is driven by food and clothing items, with IT sector purchases leading.
Comparison Graphs: CG0:15
CG: Advanced Plotting, classifying sales on bases of Occupation and Gender both1:15
Video for upper details3:29
Analyze the interrelation of occupation and gender with a hue-based bar chart to compare purchasing power across genders in sales data analysis in Python.
CG: Classifying sales on the bases of Category of product and Gender0:37
CG: Analyzing sales with Age Group and Occupation1:25
Video for upper details6:02
Classify occupation by age group to build Seaborn bar graphs with seven age groups and a legend, revealing that the 26 to 35 segment dominates purchases across occupations.

Requirements

No programming experience is required. If you understand python a little, its a plus point

Description

Hi there, welcome to this course of Sales data analysis in python. Its for you if you want to get started or in field of data analysis. We will go advanced in data analysis through graphs in python.

We hope you are already familiar with the language 'English' just. Yeah! Even if you are beginner you can start this course and will hopefully enjoy it!

In this course we will be extracting meaningful information from a large dataset with 11k data points. Its a large data with many columns like customer id, name, number of orders, amount spent etc.

We will be using an Indian sales dataset. The dataset contain almost 10k rows of data with many columns like customer name, costumer id, product customer bought, amount he spent, number of orders he made and a few more.

What we will do in this course:

Clean our data
Analyze it
Extract some patterns in data
Extract product giving good result economically
Extract the type of audience interested in shopping

And much more! gear up!

It is advanced that you should practice the codes as well with us. This will create a strong base of yours in the field of data science and analytics.

Good luck for your journey!

Who this course is for:

a person who want to boost his/her skills in data analysis and visualization in python
a person interested in recognizing hidden patterns in data

Sales data Analysis in Python

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 4min

Working with our data13 lectures • 35min

What next2 lectures • 1min

Requirements

Description

Who this course is for: