7 Powerful Steps to Build Successful Data Analytics Project

Starting a data analytics project can be very daunting for newbies. In this blog we will explore a multiple multi-technological project involving SQL, Excel, Power BI, Talend, Azure Data Factory, and Python. At the end of it, you will learn how to extract, process, analyze, and visualize your data to generate actionable insights.

1. Project Overview

We shall examine the sales data of a retail company to see what is going on, spot trends, and provide actionable recommendations for improvement. So, we will cover extraction, cleaning, integration, visualization, and advanced analysis of the data.

Step i: Data Extraction Using SQL

Our first step in our project is data extraction where we will extract the required data from the database using SQL. If you are new to SQL, check out W3Schools SQL Tutorial for an all-comprehensive guide.

Setup Database

Ensure that you can access the retail sales database, which is a crucial part of your end-to-end data analytics project. That might mean getting the appropriate login credentials and understanding the schema of the database.

Writing SQL Queries

We should retrieve the sales data together with the corresponding product and customer data.

SELECT sales_id, sales_date, product_id, customer_id, quantity_sold, sales_amount
FROM sales;

SELECT product_id, product_name, category, price
FROM products;

SELECT customer_id, customer_name, location
FROM customers;

Storing Data: Save the extracted data as CSV files for further processing.

Step ii: Data Integration using Talend

Then we’ll include the extracted data into Talend, which is one of the big ETL tools, Extract, Transform, Load.

Installation of Talen: Download the Talend Open Studio from its official site.

Then, make a configuration of the system; it includes setting up the connections to your data sources, such as the database from which you extracted the data.

Creating a Job: In Talend, design an ETL job for creating, transforming and loading data

• Extract Data: Import data to the SQL queries in Talend.
• Transform Data: Perform all data transformations such as date formatting, missing value handling, etc.
• Load Data: Load the cleaned data into a central point storage solution like an Azure Data Lake

Step iii: Data Orchestration using Azure Data Factory

Automate the data workflow in Azure Data Factory to make smooth data movement between systems.

Setting Up Azure Data Factory

• Start by creating an Azure Account: This step requires creating one if you do not have an account.
• Build a Data Factory: Go to the Azure portal and provision a new instance of a Data Factory.

Creating Pipelines

• Data Ingestion: Configure pipelines to intelligently ingest data from Talend outputs into Azure Data Lake
• Data Movement: Build data pipelines that can move data out from Azure Data Lake and in to a data warehouse such as Azure SQL Data Warehouse.

Step iv: Preprocessing and initial analysis in Excel

Execute preliminary cleaning and analysis of the data in Excel.

Data Importing: Import the data retrieved from the data warehouse into Excel.

Data Cleaning

1. Eliminate Duplicate Records: Utilize the duplicate functionality offered in Excel to eliminate duplicates.
2. Handling Missing Values: Identify missing values and address them. Fill in any missing values or delete the affected records.
3. Standardize Date Formats: All date fields must be formatted correctly.

Initial Analysis

1. Create Pivot Tables: The pivot tables summarize the sales by product, category, and location.
2. Calculate Key Metrics: To find the total amount of sales, average sales per transaction, and growth in sales.

Step v: Data Visualization using Power BI

Use Power BI to create interactive visualizations.

Loading Data into Power BI: Import cleaned data from Excel into Power BI.

Creating Visualizations:

• Sales Overview: Line charts showing sales trends over time.
• Product Performance: Create bar graphs for various products as well as categories, thus comparing sales.
• Geographic Analysis: Utilize map visualizations that show sales distribution by location

Interactive Dashboard:

Include various visualizations in the dashboard. Make it interactive with date range filters, product category, and location filters.

Step vi: Advanced Data Analysis using Python

Using Python, carry out advanced data analysis and predictive modelling.

Setup the python environment

1. Installation of Jupyter Notebook: Install the Jupyter notebook, as an interactive notebook environment for running python code.
2. Installation of Required Libraries: Utilize pip, install the required libraries like pandas, scikit-learn, matplotlib.

Loading Data:

import pandas as pd

sales_data = pd.read_csv('sales_data.csv')
products_data = pd.read_csv('products_data.csv')
customers_data = pd.read_csv('customers_data.csv')

Merging Dataset:

merged_data = pd.merge(sales_data, products_data, on='product_id')
merged_data = pd.merge(merged_data, customers_data, on='customer_id')

Data Analysis:

• Descriptive statistics: Utilize pandas library to compute mean, median, mode etc.
• Correlation Analysis: Establish relationship between different variables.

sales_data.corr()

Predictive Modeling

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

X = sales_data[['quantity_sold', 'price']]
y = sales_data['sales_amount']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

Step vii: Report and Presentation

Compile and present findings to stakeholders.

Compilation of Findings

Summarize the key observations and findings from the analysis. Observe some critical trends, correlations, and predictions.

Report Development

• Results Presentation in PowerPoint or any reporting application
• Key results based on the analysis in Power BI
• Recommendations obtained from insights drawn

Final Presentation

Submission of the dashboard and report to the stakeholders. Discussion on the recommendations and actionable insights obtained from the analysis.

2. Conclusion

This end-to-end data analytics project guide will walk you through putting together all manner of tools and technologies. Freshers will learn about extracting, cleaning, and integrating data, to the visualization and advanced analysis leading to actionable business insights. Learn How to Build a Data Analyst Portfolio: Tips for Success.

Hope you liked this blog too. Feel free to ask your precious questions in the comments below.