Introduction to Analyzing Data

analyzing data
Business People Meeting Data Analysis Planning Concept

You’re finally past the data cleaning side of your research, now what? This is where the real fun begins – data exploration and analyzing data. After weeks or months of collecting and scrubbing data, you can finally see tangible results. Analyzing data is more than just looking at numbers on a spreadsheet and hoping to make sense for it – data is meant to be decisive and objective, not just glanced at using a gut feeling. Let’s explore what analyzing data really means.

What Does Analyzing Data mean?

We already covered some basics on cleaning data, so now you can start analyzing data. This entails quite a bit, but it’s a bit more fun now. Analyzing data means going into the cleaned data and making quantitative and qualitative deductions that are objective, reliable, and based on numbers, rather than just making decisions without research. A company will now be able to discover trends, patterns, and relationships between variables that they may not have known before, allowing them to make a business decision that’s new to them.

If you want to develop a new product, what would you do? Would you choose your product based off of what you want or think is the best idea, or would you base your decisions off of factsreliable numbers, and research? Anyone aiming for a successful business would go with the second set of options. Sure, sometimes a person’s gut feeling is right, but it’s not as reliable as data. When testing the success of a new product, it may be hard to predict your exact target audience. If you do an initial product release, and continue collecting data while it’s available for sale, you can easily identify the age range of people who purchase the product, which will help for future marketing outreach.

What goes into analyzing data?

Analyzing data entails using statistical software and programming languages to identify trends within your data to make objective, reliable conclusions. AKA math, statistics, coding, and a lot rows on a spreadsheet. Most folks will consider this the fun part, as you can finally get an answer to all of your research.

There are generally two ways people look at analyzing data. One is by coming up with a series of questions beforehand and using the data to collect it. Let’s say you put together a large survey of people who watch Game of Thrones, and you collected a large amount of data including their demographics (age, gender, etc), TV viewing habits, if they read the books or not, and more. You may have been previously interested in comparing the age of people who read the books and if they like the show or not. Once you have the data, you can answer your own question.

There’s also the exploratory approach, which allows you to play around with comparing variables and coming up with deductions that you may not have considered before. Exploratory data analysis is also used in academia as an entryway into more focused research, so the researchers can test a proof of concept using the initial data set, deciding future questions. You have tons of data, use it!

Analyzing data isn’t meant to lead to okay results – it’s meant to lead to awesome results that prove to be worth the time of the researchers/backing company. New York Times pointed out an anecdote from Netflix’s purchase of the rights to House of Cards and how this now cultural phenomenon came to be. Netflix saw that many people were streaming David Fincher’s work, movies with Kevin Spacey, and the British version of House of Cards. Netflix put two and two together and voila, we now have the incredibly successful Netflix original American version of House of Cards.

How do I get started analyzing data?

A popular programming language is R, as it’s great for working with large data sets (Twitter big data for example), has reproducible code (if you develop a useful set of codes, you can keep using them throughout your project and in future data sets), has native data visualization, and is always expanding thanks to its open-source nature. You can use any number of options, but R is quite useful.

If you’re not looking to go too in-depth and just want to explore data a bit, it’s surprising what Microsoft Excel can even do. Pivot tables go a long way.

If learning hands-on in a classroom is more helpful for you, then consider taking our Data Analytics Set bootcamp, which is an entry-level foray into analyzing data. Data analytics is an increasingly in-demand job that can shape a company or non-profit for the better, and worth considering as a career path.



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