Complete Data Analytics Portfolio Project with Python and SQL in 2 Hours | 2024 Guide 🚀

Today’s dynamic employment environment requires candidates in the sphere of data analytics to have an extensive and well organized portfolio in order to be considered for an employment. Showcasing a diverse portfolio of ordered projects will help capture the attention of the employers. This blog is intended to help you complete an interesting data analytics project that will incorporate both SQL and Python in its structure while also touch upon practical skills and applications.

Creating a Standout Portfolio :Data Analytics Portfolio Project 💼

It’s typical for a strong portfolio to showcase projects where different tools and technologies were applied. The project that we are going to work on today comprises the use of both SQL and Python which, in addition to your technical proficiency, will also test your data analysis and visualization skills.

Prior to commencing work on the project, ensure that all relevant software such as Python, SQL and libraries such as Pandas and Matplotlib are installed. We will use data from the e-commerce sales dataset it’s beneficial in actual practical analysis.

Downloading the E-Commerce Dataset 📥

The first step in our project is to download the e-commerce sales dataset. That is about five different e-commerce data files for customers, orders, payments and products etc – all of which we will need for our analysis.

1. First, visit a resource such as Kaggle, and search for any e-commerce sales dataset.
2. Save the dataset by downloading and unzip the files in the correct folder on your computer.
3. Verify that you have CSV files for returning or new customers, orders, products, and payments which are all in readiness for analysis.

After performing downloading and extracting the files, the next step is to load the information into MySQL with the Python programming language.

Loading Data into MySQL Using Python 📊

In order to perform data loading to MySQL, the user has to connect Python with MySQL. It includes incorporating mysql connector library and writing a program in Python to read CSV files and put the data into MySQL databases.

Here’s how you can do this:

1. 1. Install the MySQL Connector library if you haven’t already:

pip install mysql-connector-python

1. Create a python program that performs the required tasks of importing the required libraries as well as connecting to your Mysql database
2. Make use of the Pandas library to load the CSV files and put the information into your Mysql tables.

Once, the data load into MySql is successful, we can proceed into performing different analytical tasks related to the dataset.

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Analytical Questions and Solutions 🧠

This time around there are 15 questions which will be subdivided into 3 sectors which includes basic, Intermediate and advanced. This will display how analytical you are, as well as how well you can work with SQL and Python.

Basic Questions 🔍

Let’s start with the basic questions. The first question we will answer is:

List all unique cities where customers are located.

To answer this question, we will write a SQL query that selects distinct cities from the customers table.

SELECT DISTINCT city FROM customers;

Once we execute this query, we will retrieve a list of all unique cities from our dataset.

Intermediate Questions 📈

Next, we will move on to intermediate questions. One such question might be:

Calculate the total sales per category.

In order to address this issue, an in-depth analysis of the order, order items, and products tables will have to be done b be able to summarize the sales data in groups by monitoring the sales amount a particular category generates.

SELECT p.category, SUM(o.amount) AS total_sales
FROM orders o
JOIN order_items oi ON o.id = oi.order_id
JOIN products p ON oi.product_id = p.id
GROUP BY p.category;

Advanced Questions 🚀

For advanced questions, we can analyze trends over time. For example:

Calculate the year-over-year growth rate of total sales.

To calculate the growth rate, we will first gather sales data by year and then apply the growth rate formula.

SELECT year, SUM(amount) AS total_sales
FROM orders
GROUP BY year;

Next, we will use Python to calculate the growth rate based on the retrieved data.

Uploading Your Project to GitHub :Data Analytics Portfolio Project 📂

After completing your study and answering all of the inquiry, you now have to present the result of your hardwork. You may upload your project on GitHub so that employers can view a publicly available portfolio.

1. To do this, kindly take the following steps to upload the project:
2. Go to your GitHub page and create a new repository.
3. Upload the project files into the repository.

Make sure to include a summary of the project description, how it was done, what methods were used, and what were the findings.

Creating a PDF of the Project 📄

When a project is submitted, there is also the possibility to generate a project summary and the answers to the question supplied and the conclusions reached in a separate PDF document. It can be useful to your project portfolio.

In order to prepare the PDF:

• Prepare a final report with bullets and key points.
• Put the material in the structure resembles to that of a Latex or even word processor.\
• Convert the above document to PDF format.

Conclusion :Data Analytics Portfolio Project 🎉

Since the inception of the work, you have not merely built a well-crafted data analytics portfolio but also explored the use of SQL and Python for data analysis. Such a combination of skills is in great demand in the job market, and therefore, you can easily secure a spot in the data analyst job roles.

But in relation to the creation of the portfolio, the impounding issue is not solely the projects, but the manner in which you illustrate the results and thinking ability. Well done in your data analytics quest!