From Investopedia - the definition of “Data Analytics” is:

“Data analytics is the science of analyzing raw data to make conclusions about that information. Many techniques and processes of data analytics have been automated into mechanical methods and algorithms that work over raw data for human consumption.” (Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques, 2022)

Cool. So what does that really mean?

For example, what exactly is data analytics insight?

If I were to give you a dataset on all the user activity data on Interview Query and say, “come back to me with some insights”, what tangible things would you come back with?

Let’s bring it back to how businesses function and why they even need data analysts or someone to work in data analytics.

Data analytics help support how most businesses function whether you actually use analytics or not.

Most of the time, businesses are pretty damn simple. For example, if I’m running a lawn mowing business, I call a bunch of customers to determine if they need their lawns mowed. When one of them says yes I head over and mow their lawn.

But as we scale into the internet age, data analysis is the one key tool that allows us to scale our business by making smarter decisions. Now instead of calling a bunch of customers, I can run a market analysis to figure out which marketing channels or newspapers are best suited for my lawn-mowing ads to get inbound leads.

Or I can analyze all of my customer calling data to determine better ways to run my outbound cold-calling strategy and decrease the time I’m spending calling or increase the number of qualified leads.

Data analytics as I would define it is the process of mining data to return answers that help improve organizations and businesses.

Sometimes, these organizations and businesses need to hire people that are more accurate with the analysis portion to help them make better decisions.

At the most basic principles, the input is mainly your time and a large dataset, and the output is either an insight, a statement, or evidence for a decision. That’s all data analysis accomplishes.

Data Analytics Learning Path Summary

In this course, we’re going to go over how data analytics works, how it shows up in interviews, and what fundamentals we need to know about data analytics. Specifically, prepare data analysts for their interviews.

The course is going to first start out with the basics of analyzing data. How do we analyze data and what fundamental concepts do we have to tackle? This part will mainly dive into the fundamentals around causal inference and how we can apply those fundamentals for data analysis.

Then we’ll dive into how to tackle case study questions in data analytics, the frameworks we can use, helpful strategies, and tips. We’ll tackle some example problems around diagnosing issues and changes over time.

Lastly, we’ll go into product-specific analytics questions. These are all surrounding analyzing products.

Course Prerequisites

This course will largely be tool agnostic. While most data analytics case study questions are focused around SQL as the query language for the solutions, you can in reality use any tool such as Excel, Python, Pandas, R, etc., to run the analysis.

All you really need to do is to aggregate and manipulate data.

Prerequisites to the course:

• Additionally, in the course, we won’t be going over any data cleaning
• Understanding basic SQL and data manipulation will be largely beneficial here.
• Some basic statistics would be helpful but not necessary.
• Lastly, most of the ideas we’re going over in this course will also be applied in the product sense course.

Reference list entry:

• Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques. (2022, June 27). Investopedia. https://www.investopedia.com/terms/d/data-analytics.asp
• Data Analytics Icon. (2022, Dec 14). Flaticon. https://www.flaticon.com/

Acknowledgements:

• I’d like to thank our members sweatsource59 and cwkoops for helping contribute their infinite knowledge to this course!