Session 1 - Data Collection

Why Learn Data Analytics in 2025?

The demand for data analytics professionals is growing at an unprecedented pace. The global data market is expected to exceed 285 billion dollars by 2031, making data literacy one of the most valuable skills in the job market. Learning data analytics today is not only a wise career move — it is a way to future-proof your professional journey.

This course is designed for beginners who want to build a solid understanding of data analytics, even without a technical background. By the end of this tutorial series, you will be able to confidently apply your skills to real business problems using tools like Google Sheets and trusted open-source datasets.

Understanding the Data Analytics Pipeline

In this course, you will work through the complete data analytics pipeline, which includes the following steps:

First, you will learn how to collect data from reliable sources. After that, you will clean the data to remove inconsistencies and errors. You will then apply descriptive and exploratory analysis to uncover patterns and trends. Next, you will visualize your findings using charts and graphs. Finally, you will present your insights through clear storytelling, with a focus on business impact of data analysis.

All of these steps will be applied to a real-world dataset, so you gain practical, hands-on experience that you can include in your portfolio.

Step 1: Collecting Real-World Data for Analysis

Before you can analyze data, you need to find it. Fortunately, there are trusted platforms where you can access high-quality, free datasets. One of the most popular resources for beginners and experienced analysts alike is Kaggle, a global platform for data science competitions, learning resources, and a vast library of open datasets.

When you search for a dataset on Kaggle, you should always check a few important details. Look at who uploaded the dataset and whether they are a verified, active contributor. Pay attention to how many people have downloaded or interacted with the dataset. You should also review when the dataset was last updated and how well it is described. A well-documented dataset with clear descriptions and structure is always preferable for beginners.

For this course, we will use the Amazon Dataset for Sales Analysis. This dataset, originally available at Kaggle (you can view it here, if you want), contains information such as product names, categories, prices, discounts, customer ratings, and reviews. With over 1,465 data points, this dataset provides enough complexity to perform meaningful analysis while remaining approachable for those new to data analytics.

Importing the Amazon Sales Dataset into Google Sheets

Once you have downloaded the dataset from Lumen github repository above, you will need to import it into a tool that allows for easy manipulation and analysis. For this course, we recommend Google Sheets, which is free –  perfect for beginners.

To import your dataset into Google Sheets, follow these steps:

First, log in to your Google account and open Google Sheets. Next, go to the File menu, select Import, and upload the CSV file you downloaded from Kaggle. Make sure that the file type is recognized as a comma-separated value file. If you are unsure, you can use the automatic detection feature.

Once the dataset is imported, take a moment to rename your spreadsheet to something meaningful, such as Amazon Sales Data. Review the dataset to ensure all expected columns are present. You should see product IDs, product names, categories, actual and discounted prices, discount percentages, ratings, number of reviews, and additional information like product links.

By completing this step, you have successfully collected and prepared your dataset for analysis — the first building block of your real-world data analytics project.

Thinking Beyond the Data: Business Impact and Reflection

Technical skills are essential for any data analyst, but what truly sets you apart is the ability to connect your analysis to real business outcomes. Every dataset you work with should be tied to clear business questions.

As you look at the Amazon Sales Dataset, ask yourself:

• Which products are performing the best in terms of sales?

• How do discounts affect customer purchasing behavior?

• What patterns exist in customer satisfaction ratings?

• How can this data help improve product offerings or pricing strategies?

By consistently linking your analysis to the business impact of data analysis, you demonstrate not just technical ability, but strategic thinking — a key differentiator for anyone entering the field of data analytics.

Next Steps

In the next module of this Learn Data Analytics with Real-World Project course, you will dive into data cleanup, where you will learn how to prepare your dataset for accurate, reliable analysis by removing errors and inconsistencies.

Before moving forward, take a few minutes to explore your dataset in Google Sheets. Familiarize yourself with the structure, review the columns, and start thinking about the types of insights you could extract to drive business decisions.