Case Study - Dicoding Collection

1. Background

Dicoding Collection or often abbreviated as DiCo is a company specializing in fashion. They produce various fashion items and sell them through online platforms.

As a contemporary company, DiCo realizes how crucial data is for the growth of a business. Hence, they stored all the sales history along with information related to products and customers in a database. This database consists of four tables, including customers, orders, products, and sales.

• Customers table: this table stores various customer-related information, such as customer_id, customer_name, gender, age, home_address, zip_code, city, state, and country.
• Orders table: this table stores various information related to an order consisting of order_id, customer_id, order_date, and delivery_date.
• Products table: this table contains various information related to a product, such as product_id, product_type, product_name, size, color, price, quantity, and description.
• Sales table: this table contains detailed information related to sales, such as sales_id, order_id, product_id, price_per_unit, quantity, and total_price.

2. Project work cycle

1. Data Wrangling:

• Gathering data
• Assessing data
• Cleaning data

2. Exploratory Data Analysis:

• Defined business questions for data exploration
• Create Data exploration

3. Data Visualization:

• Create Data Visualization that answer business questions

4. Dashboard:

• Set up the DataFrame which will be used
• Make filter components on the dashboard
• Complete the dashboard with various data visualizations

Note: Numbers 1 to 3 are in the dicoding-collection-exercise and number 4 is in dashboard.

3. Getting Started

dicoding-collection-exercise

1. Download this project.
2. Open your favorite IDE like Jupyter Notebook or Google Colaboratory (but in here I will use Google Colab).
3. Create a New Notebook.
4. Upload and select the file with .ipynb extension.
5. Connect to hosted runtime.
6. Lastly, run the code cells.

dashboard

1. Download this project.
2. Install the Streamlit in your terminal or command prompt using pip install streamlit. Install another libraries like pandas, numpy, scipy, matplotlib, and seaborn if you haven't.
3. Please note, don't move the csv file because it acts a data source. keep it in one folder as dashboard.py
4. Open your VSCode and run the file by clicking the terminal and write it streamlit run dashboard.py.