This is my first personal project for learning data with MySQL and Tableau. The datasets it self talk abut Insane Level Store, it contain dataset : order, product, customer, employee. In this project im doing data preparation, data cleaning, basic EDA, Ad-Hoc Request and make visualization into dashboard.
Insane Level Store is a dummy store that sells apparel and accessories, including items like t-shirts, cargo pants, parka jackets, and more. In this scenario, I need to build a database in MySQL Server (local), create the schema and tables, and input the data into them. After preparing the data for use, I will complete a basic exploratory data analysis (EDA) to familiarize myself with the data. Next, I will fulfill ad hoc requests for stakeholders and finally create a dashboard in Tableau for future use. The datasets is not real, im generate those using Mockaroo Website for easy to pick variables into the datasets. But im have prepared the datasets in All Datasets Folder.
- đź“– MySQL : Database, Data Preparation, Data Cleaning & EDA
- 📉 Tableau : Data Visualization into Dashboard
/* DATA PREPARATION
The case is you need to do data preparation, migration from other data source to MySQL
Cause there is no database yet in MySQl Server, lets make the database and schema
*/
-- Making database --
CREATE DATABASE insanelevelstore;
-- Creating employee(karyawan) data table --
CREATE TABLE karyawandata (
admin_id VARCHAR(255),
nama VARCHAR(255),
tanggal_karyawan DATE,
domisili VARCHAR(255),
jenis_kelamin VARCHAR(10),
gaji DECIMAL,
PRIMARY KEY (admin_id)
);
-- Insert karyawandata from csv --
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/karyawandata.csv'
INTO TABLE karyawandata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- Checking karyawandata table --
SELECT
*
FROM
karyawandata;
-- Creating product data table --
CREATE TABLE productdata (
product_id VARCHAR(255),
produk VARCHAR(255),
kategori VARCHAR(255),
Harga DECIMAL,
Modal DECIMAL,
PRIMARY KEY (product_id)
);
-- Inserting product data from csv --
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/productdata.csv'
INTO TABLE productdata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- Cheking productdata table --
SELECT
*
FROM
productdata;
-- Creating customer data table --
CREATE TABLE customerdata (
User_id VARCHAR(255),
Tanggal_Registrasi DATE,
Waktu_Registrasi TIME,
Nama_Customer VARCHAR(255),
No_Handphone VARCHAR(255),
Email VARCHAR(255),
Tanggal_Lahir DATE,
Kota_Domisili VARCHAR(255),
Pekerjaan VARCHAR(255),
Status_Hubungan VARCHAR(255),
Jenis_Kelamin VARCHAR(255),
PRIMARY KEY (User_id)
);
-- Inserting customr data from csv
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/customerdata.csv'
INTO TABLE customerdata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- Checking customerdata table
SELECT
*
FROM
customerdata;
-- Creating order data table --
CREATE TABLE orderdata (
order_id VARCHAR(255),
Tanggal_Order VARCHAR(255),
Waktu_Pesan TIME NULL,
Nama_Pemesan VARCHAR(255),
No_Resi VARCHAR(255),
Produk VARCHAR(255),
Kategori VARCHAR(255),
Jumlah_Pesanan INT,
Harga VARCHAR(255),
Diskon VARCHAR(255),
Total_Harga VARCHAR(255),
Profit VARCHAR(255),
Kota_PengirimanMedia_Pemesanan VARCHAR(255),
Admin_Proses VARCHAR(255),
Ekspedisi VARCHAR(255),
Rating VARCHAR(255),
PRIMARY KEY (order_id)
);
-- Inserting order data from csv --
-- there is 3 file csv : backup_2018_raw, backup_2019_raw and backup_2020_raw --
-- inserting 2018 order data --
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/Order_Datasets_ Raw/backup_2018_raw.csv'
INTO TABLE orderdata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- inserting 2019 order data --
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/Order_Datasets_ Raw/backup_2019_raw.csv'
INTO TABLE orderdata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- inserting 2020 order data --
LOAD DATA INFILE 'E:/Project Porto Data/Insane Level Store/Order_Datasets_ Raw/backup_2020_raw.csv'
INTO TABLE orderdata
FIELDS TERMINATED BY ';'
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
-- checking order data table --
SELECT
*
FROM
orderdata;
/* DATA CLEANING AND NORMALIZATION
This section you need to remove duplicates, removing null in some core column like tanggal_order, nama_pemesan, produk etc (confirmed that null is error),
after that you need to remove unnecery colum or redundance data by making good table relationship, filling the important missing value like harga(price) etc
and change the data type for proper use for each column
*/
-- Make an orderdata backup table in database --
CREATE TABLE orderdata_backup AS
SELECT *
FROM orderdata;
-- Checking if there are missing value in the core column/variable --
-- delete it cause its confirmed that the missing is error from the data system --
SELECT
*
FROM
orderdata
WHERE
order_id = '' OR Tanggal_Order = ''
OR Waktu_Pesan = ''
OR Nama_Pemesan = ''
OR No_Resi = ''
OR Produk = ''
OR Kota_PengirimanMedia_Pemesanan = ''
;
-- Deleting rows who have mising value and checking it --
START TRANSACTION;
DELETE FROM orderdata
WHERE
order_id = '' OR Tanggal_Order = ''
OR Waktu_Pesan = ''
OR Nama_Pemesan = ''
OR No_Resi = ''
OR Produk = ''
OR Kota_PengirimanMedia_Pemesanan = ''
;
-- checking --
SELECT
*
FROM
orderdata;
-- commit changes --
COMMIT;
-- checking duplicates 1 --
SELECT
order_id,
Tanggal_Order,
Waktu_Pesan,
Nama_Pemesan,
No_Resi,
Produk,
Kategori,
Jumlah_Pesanan,
Harga,
Diskon,
Total_Harga,
Profit,
Kota_PengirimanMedia_Pemesanan,
Admin_Proses,
Ekspedisi,
Rating,
COUNT(*) AS total
FROM
orderdata
GROUP BY order_id , Tanggal_Order , Waktu_Pesan , Nama_Pemesan , No_Resi , Produk , Kategori , Jumlah_Pesanan , Harga , Diskon , Total_Harga , Profit , Kota_PengirimanMedia_Pemesanan , Admin_Proses , Ekspedisi , Rating
HAVING COUNT(*) > 1;
-- Checking duplicates 2 --
SELECT
*
FROM
orderdata
WHERE
(order_id , Tanggal_Order,
Waktu_Pesan,
Nama_Pemesan,
No_Resi,
Produk,
Kategori,
Jumlah_Pesanan,
Harga,
Diskon,
Total_Harga,
Profit,
Kota_PengirimanMedia_Pemesanan,
Admin_Proses,
Ekspedisi,
Rating) IN (SELECT
order_id,
Tanggal_Order,
Waktu_Pesan,
Nama_Pemesan,
No_Resi,
Produk,
Kategori,
Jumlah_Pesanan,
Harga,
Diskon,
Total_Harga,
Profit,
Kota_PengirimanMedia_Pemesanan,
Admin_Proses,
Ekspedisi,
Rating
FROM
orderdata
GROUP BY order_id , Tanggal_Order , Waktu_Pesan , Nama_Pemesan , No_Resi , Produk , Kategori , Jumlah_Pesanan , Harga , Diskon , Total_Harga , Profit , Kota_PengirimanMedia_Pemesanan , Admin_Proses , Ekspedisi , Rating
HAVING COUNT(*) > 1);
-- Remove duplicates --
-- There is no duplicates --
/*
DELETE t1
FROM orderdata t1
JOIN orderdata t2
ON
t1.order_id > t2.order_id
AND t1.Tanggal_Order = t2.Tanggal_Order
AND t1.Waktu_Pesan = t2.Waktu_Pesan
AND t1.Nama_Pemesan = t2.Nama_Pemesan
AND t1.No_Resi = t2.No_Resi
AND t1.Produk = t2.Produk
AND t1.Kategori = t2.Kategori
AND t1.Jumlah_Pesanan = t2.Jumlah_Pesanan
AND t1.Harga = t2.Harga
AND t1.Diskon = t2.Diskon
AND t1.Total_Harga = t2.Total_Harga
AND t1.Profit = t2.Profit
AND t1.Kota_Pengiriman = t2.Kota_Pengiriman
AND t1.Media_Pemesanan = t2.Media_Pemesanan
AND t1.Admin_Proses = t2.Admin_Proses
AND t1.Ekspedisi = t2.Ekspedisi
AND t1.Rating = t2.Rating;
*/
/* DATA NORMALIZATION AND REDUNDANCY FIX
There are some column not in correct data type like tanggal_order, diskon, total_harga, profit and rating
*/
START TRANSACTION;
-- Make a new column for order_date / tannga_order_new --
ALTER TABLE orderdata ADD COLUMN tanggal_order_new DATE AFTER tanggal_order;
-- Set value for the new colum with right format or data type --
UPDATE orderdata
SET tanggal_order_new = STR_TO_DATE(tanggal_order, '%d/%m/%Y');
-- cheking the orderdata table structure --
DESCRIBE orderdata;
-- deleting older colum tanggal_order --
ALTER TABLE orderdata DROP COLUMN tanggal_order;
-- change the new column name to the tanggal_order --
ALTER TABLE orderdata CHANGE tanggal_order_new tanggal_order DATE;
-- checking the table --
SELECT
*
FROM
orderdata;
-- commit change for column date done --
COMMIT;
-- moving to customer_name (Nama_Pemesan) column --
/* Cause we have a dedicated customer table for data, we have to change Nama_Pemesan column become user_id and fill the value with the ID,
and then we remove Nama_Pemesan column since we have id for reference to customerdata table
*/
-- make new column for user_id --
ALTER TABLE orderdata ADD COLUMN user_id varchar(255) AFTER Nama_Pemesan;
-- filling the user_id column --
UPDATE orderdata o
JOIN customerdata c ON o.Nama_Pemesan = c.Nama_Customer
SET o.user_id = c.user_id
WHERE o.user_id IS NULL;
-- checking if there any mistakes --
SELECT *
FROM orderdata o
LEFT JOIN customerdata c ON o.Nama_Pemesan = c.Nama_Customer
WHERE o.user_id IS NULL AND c.user_id IS NULL;
-- drop column nama_pemesan --
ALTER TABLE orderdata
DROP COLUMN Nama_Pemesan;
-- checking the table --
SELECT
*
FROM
orderdata;
-- moving to Product (Produk) column --
/* Cause we have a dedicated produk table for data, we have to change produk column become produk_id and fill the value with the ID,
and then we remove produk. category (kategori) and price (harga) column since we have id for reference to productdata table
*/
-- make new column for product_id --
ALTER TABLE orderdata ADD COLUMN product_id varchar(255) AFTER Produk;
-- filling the product_id column --
UPDATE orderdata o
JOIN productdata p ON o.Produk = p.produk
SET o.product_id = p.product_id
WHERE o.product_id IS NULL;
-- Removing produk, kategori, and harga column
ALTER TABLE orderdata
DROP COLUMN produk,
DROP COLUMN kategori,
DROP COLUMN harga;
-- checking the table --
SELECT * FROM orderdata;
/* Moving to diskon column
there is some null, lets set it to zero
*/
UPDATE orderdata
SET Diskon = 0
WHERE Diskon = '';
-- checking Diskon column --
SELECT Diskon
FROM orderdata
WHERE Diskon NOT REGEXP '^[0-9]+$';
-- Change data type column to int --
ALTER TABLE orderdata
MODIFY COLUMN Diskon INT;
-- Moving to Total_Harga Column --
-- fill the value with = (jumlah_pesanan x harga) x (1 - diskon / 100) --
UPDATE orderdata o
JOIN productdata p ON o.product_id = p.product_id
SET o.total_harga = (p.harga * o.jumlah_pesanan) * (1 - o.diskon / 100);
-- change total_harga to decimal data type --
ALTER TABLE orderdata
MODIFY COLUMN Total_Harga DECIMAL(10, 2);
-- Moving to profit column --
-- fill the value by = total_harga - (jumlah_pesanan x modal)
UPDATE orderdata o
JOIN productdata p ON o.product_id = p.product_id
SET o.Profit = o.Total_Harga - (o.Jumlah_Pesanan * p.Modal);
-- change total_harga to decimal data type --
ALTER TABLE orderdata
MODIFY COLUMN Profit DECIMAL(10, 2);
-- Moving to Kota_PengirimanMedia_Pemesanan column --
-- we need to use substring the column into 2 column each for each data --
-- add 2 column for each data --
ALTER TABLE orderdata
ADD COLUMN Media_Pemesanan VARCHAR(255) after Kota_PengirimanMedia_Pemesanan,
ADD COLUMN Kota VARCHAR(255) after Kota_PengirimanMedia_Pemesanan;
-- fill the value from Kota_PengirimanMedia_Pemesanan --
UPDATE orderdata
SET
Kota = TRIM(SUBSTRING_INDEX(Kota_PengirimanMedia_Pemesanan, ' - ', 1)),
Media_Pemesanan = TRIM(SUBSTRING_INDEX(Kota_PengirimanMedia_Pemesanan, ' - ', -1))
;
-- remove unused column --
ALTER TABLE orderdata
DROP COLUMN Kota_PengirimanMedia_Pemesanan;
-- checking the table --
SELECT
*
FROM
orderdata;
/* MOVING TO Admin_Proses column
since we have dedicated table data in karyawandata table, we are gonna change the column and using admin_id instead
*/
-- make new column for admin_id --
ALTER TABLE orderdata ADD COLUMN admin_id varchar(255) AFTER Admin_Proses;
-- filling the admin_id column --
UPDATE orderdata o
JOIN karyawandata k ON o.Admin_Proses = k.nama
SET o.admin_id = k.admin_id
WHERE o.admin_id IS NULL;
-- Removing Admin_Proses column --
ALTER TABLE orderdata
DROP COLUMN Admin_Proses;
-- checking the table --
SELECT
*
FROM
orderdata;
-- Moving to Ekspedisi column --
-- checking the value --
SELECT
*
FROM
orderdata
WHERE
Ekspedisi = '';
-- There are some blank value, fill these with unknown for default blank or null --
UPDATE orderdata
SET Ekspedisi = 'unknown'
WHERE Ekspedisi = '';/* EXPLORATORY DATA ANALYSIS (EDA)
Now lets try find some several basic insights from the data like basic statictics, the most high sales product, the most less sales category etc
*/
-- Basic statistics descriptive --
SELECT
COUNT(*) AS Jumlah_Pesanan,
AVG(Jumlah_Pesanan) AS RataRata_Pesanan,
MIN(Jumlah_Pesanan) AS Min_Pesanan,
MAX(Jumlah_Pesanan) AS Max_Pesanan,
STD(Jumlah_Pesanan) AS StdDev_Pesanan,
AVG(Diskon) AS RataRata_Diskon,
AVG(Total_Harga) AS RataRata_TotalHarga,
AVG(Profit) AS RataRata_Profit
FROM
orderdata;
-- How many each unique user making order --
SELECT
user_id, COUNT(order_id) AS Jumlah_Pesanan
FROM
orderdata
GROUP BY user_id
ORDER BY Jumlah_Pesanan DESC;
-- Let see how each product performed --
SELECT
product_id,
COUNT(order_id) AS Jumlah_Pesanan,
SUM(Jumlah_Pesanan) AS Total_Pesanan,
SUM(Total_Harga) AS Total_Penjualan,
SUM(Profit) AS Total_Profit
FROM
orderdata
GROUP BY product_id
ORDER BY Total_Penjualan DESC;
-- How admin works --
SELECT
admin_id,
COUNT(order_id) AS Jumlah_Pesanan,
SUM(Total_Harga) AS Total_Penjualan,
SUM(Profit) AS Total_Profit
FROM
orderdata
GROUP BY admin_id
ORDER BY Total_Penjualan DESC;
-- The most shipping brand used --
SELECT
Ekspedisi,
COUNT(order_id) AS Jumlah_Pesanan,
SUM(Jumlah_Pesanan) AS Total_Pesanan
FROM
orderdata
GROUP BY Ekspedisi
ORDER BY Jumlah_Pesanan DESC;
-- How each city performed --
SELECT
Kota,
COUNT(order_id) AS Jumlah_Pesanan,
SUM(Jumlah_Pesanan) AS Total_Pesanan
FROM
orderdata
GROUP BY Kota
ORDER BY Jumlah_Pesanan DESC;
-- How each platform referral performed --
SELECT
Media_Pemesanan,
COUNT(order_id) AS Jumlah_Pesanan,
SUM(Jumlah_Pesanan) AS Total_Pesanan,
SUM(Total_Harga) AS Total_Penjualan,
SUM(Profit) AS Total_Profit
FROM
orderdata
GROUP BY Media_Pemesanan
ORDER BY Total_Penjualan DESC;
-- Profit performed by each discount --
SELECT
Diskon, AVG(Profit) AS RataRata_Profit
FROM
orderdata
GROUP BY Diskon
ORDER BY Diskon;
Before making the dashboard, stakeholders like product manager, sales manager, human resource team and marketing team asking several data for spesific purpose. I need to give the data for the exact what they ask.
Subject: Request for Customer List Below Average Purchases in 2020
Hi Data Team,
I hope this email finds you well. As part of our CRM follow-up program, we are looking to target customers who have made purchases below the average sales amount in 2020. Could you please provide us with a list of these customers, including their name, phone number, email, gender, domicile, and total purchase amount?
Thank you for your assistance on this.
Best regards, Marketing Follow-Up Team
SELECT
c.Nama_Customer,
c.No_Handphone,
c.Email,
c.Jenis_Kelamin,
c.Kota_Domisili,
SUM(o.Total_Harga) AS Jumlah_Pembelian
FROM
orderdata o
JOIN
customerdata c ON o.user_id = c.User_id
WHERE
YEAR(o.tanggal_order) = 2020
GROUP BY c.Nama_Customer , c.No_Handphone , c.Email , c.Jenis_Kelamin , c.Kota_Domisili
HAVING SUM(o.Total_Harga) < (SELECT
AVG(Total_Harga)
FROM
orderdata
WHERE
YEAR(tanggal_order) = 2020);Subject: Request for Sales Data for the Last Three Months of Each Year (2018-2020)
Dear Data Team,
I hope all is well. For our end-of-year analysis, we need sales data for the last three months (October to December) of each year from 2018 to 2020. Additionally, we would like to see sales performance by city during this period. Could you provide us with this data at your earliest convenience?
Thank you.
Best regards, Sales Manager Team
SELECT
YEAR(o.tanggal_order) AS Tahun,
MONTH(o.tanggal_order) AS Bulan,
o.Kota,
SUM(o.Total_Harga) AS Total_Penjualan,
COUNT(o.order_id) AS Jumlah_Order
FROM
orderdata o
WHERE
(MONTH(o.tanggal_order) IN (10 , 11, 12))
AND YEAR(o.tanggal_order) IN (2018 , 2019, 2020)
GROUP BY YEAR(o.tanggal_order) , MONTH(o.tanggal_order) , o.Kota
ORDER BY Tahun , Bulan , o.Kota;Subject: Request for Employee Performance Data
Hello Data Team,
As part of our quarterly review, HR needs to evaluate the performance of each employee/admin and identify those who have surpassed the average performance to reward them with a 25% bonus on their base salary. Could you provide us with a report listing the employee name, domicile, gender, total sales, salary, and bonus amount (if applicable)?
Thank you!
Kind regards, HR Team
SELECT
k.nama AS nama_admin,
k.domisili,
k.jenis_kelamin,
SUM(Jumlah_Pesanan) AS total_penjualan,
k.gaji,
CASE
WHEN
SUM(Jumlah_Pesanan) > (SELECT
AVG(total_orders)
FROM
(SELECT
SUM(Jumlah_Pesanan) AS total_orders
FROM
orderdata
GROUP BY admin_id) AS avg_orders)
THEN
k.gaji * 0.25
ELSE 0
END AS besaran_bonus
FROM
karyawandata k
LEFT JOIN
orderdata o ON k.admin_id = o.admin_id
GROUP BY k.nama , k.domisili , k.jenis_kelamin , k.gaji
ORDER BY total_penjualan DESC;Subject: Request for Product Performance Data for Q2 2020
Dear Data Team, We are preparing for a product review meeting next week, and we need data on product performance for Q2 2020. Specifically, we want to identify the products with the highest and lowest ratings. Could you help us with this information?
Thank you very much!
Best regards, Product Manager Team
SELECT
p.product_id,
p.produk,
p.kategori,
AVG(o.Rating) AS avg_rating,
COUNT(o.order_id) AS total_time_orders
FROM
orderdata o
JOIN
productdata p ON o.product_id = p.product_id
WHERE
YEAR(o.tanggal_order) = 2020
AND MONTH(o.tanggal_order) BETWEEN 4 AND 6
GROUP BY p.product_id , p.produk , p.kategori
ORDER BY avg_rating DESC;The data visualization component of this project was focused on creating a comprehensive and interactive dashboard using Tableau. The main objective was to provide clear, actionable insights into various aspects of the business, segmented into four key areas: Sales Overview, Product Performance, Customer Insights, and Employee Performance. You can see on tableau public for this viz dashboard Insane Level Store Dashboard
Sales Overview: This dashboard provides an overarching view of sales metrics, including total sales, profit, and sales quantity. Key performance indicators (KPIs) are presented through scorecards, while trend lines and bar charts highlight sales trends over time. This enables users to quickly assess overall business performance and identify patterns in sales data.
Last Updated Version
Product Performance: This section focuses on the analysis of product-related data, showcasing top-performing products, sales by category, and detailed product performance metrics. Visualizations such as line charts and bar charts illustrate trends and comparisons across different product lines, helping stakeholders understand product success and areas for improvement.
Last Updated Version
Customer Insights: Designed to provide a deep dive into customer behavior and demographics, this dashboard features metrics such as customer segmentation, new vs. returning customers, and customer lifetime value. Scatter plots, pie charts, and heat maps are utilized to depict customer distribution and purchasing patterns, offering valuable insights into customer engagement and retention strategies.
Last Updated Version
Employee Performance: This dashboard evaluates employee contributions by analyzing metrics like order processing efficiency, total sales by employee, and individual performance rankings. Scorecards and bar charts present a clear view of employee performance, which can be used to inform decisions on training, recognition, and resource allocation.
Last Updated Version
Interactive Features :Each dashboard includes filter menus, allowing users to drill down into specific data subsets and customize the view according to their needs. This interactivity enhances the user experience, providing flexibility to analyze data from different perspectives and making the dashboards powerful tools for data-driven decision-making.
Overall, this multi-dashboard setup offers a holistic view of the business, enabling stakeholders to quickly grasp the big picture while also exploring detailed analytics. The dashboards serve as an initial framework for ongoing analysis and are designed to evolve with the business as new data becomes available. But it just intial rough dashboard design that can be improve in the future.
During this project, I gained valuable experience in several areas:
- Database Design and Management: Learned how to design an efficient database schema, create tables, define relationships, and manage data within MySQL.
- Data Cleaning and Preparation: Developed skills in data cleaning techniques, such as handling missing values, correcting data types, and transforming raw data into a usable format.
- SQL Queries and Analysis: Enhanced my ability to write complex SQL queries to extract meaningful insights from data, including the use of joins, subqueries, and aggregate functions.
- Exploratory Data Analysis (EDA): Improved my ability to perform EDA, identifying key trends and patterns in the data to inform decision-making.
- Data Visualization: Gained experience in creating effective dashboards in Tableau, utilizing various chart types and visual techniques to communicate insights clearly to stakeholders.
This project provided a comprehensive learning experience, enhancing my skills in database management, data analysis, and visualization.
This project represents my journey in building a data analysis pipeline from scratch, starting with data preparation and cleaning, then moving on to exploratory data analysis, and finally creating dashboards in Tableau.
By organizing the data in MySQL, performing exploratory data analysis, and developing dashboards, I was able to gain valuable insights that could help in making better business decisions. Throughout this project, I learned a great deal about data management, analysis techniques, and the importance of effective data visualization for stakeholders.
The dashboards created provide a good starting point for monitoring data and making decisions based on the current state of the business. They are also designed to be flexible, allowing for future updates and additional analyses as needed. I recognize there is still much to learn, and I look forward to improving these tools as my skills grow.
Overall, this project has been a great learning experience, highlighting both the challenges and rewards of working with data. It has shown me the significant impact that data analytics can have on understanding business performance and guiding strategic decisions.