3 Things You Should Know Before Pursuing a Career in Data Analytics

02-Feb-2021

No matter what industry you plan to operate in, data will be a big part of its operations. In fact, a report from SaaS Company Domo highlights the sheer amount of data produced every minute: Business profiles on Instagram see 138,889 clicks, Twitter gains 319 new users, and Venmo users transact a collective $239,196 in payments — all within 60 seconds.

Whether an organization plans to use this information for marketing, operations, or product development, the continuous rise of data has called for more professionals on the subject. True enough, careers in data analytics are some of the most in-demand in any industry right now, with data analysts expecting a 33% growth between 2016 and 2026. It doesn’t matter what type of data they specialize in either — machine data, big data, and reporting data are equally important.

As such, if you’re pursuing a career in data analytics, know that you’re entering a growing field. But what does it take to succeed in the job? Here are three things you need to know:

A background in coding is essential, but not in computer science

It’s no secret that data analysts need a background in programming. After all, they need to be familiar with the data they’re pulling insight from, which is written in code. On the other hand, computer science is more concerned with the theory behind systems. You don’t need to learn about architectures, software testing, and the like to become a data analyst.

Given that, focus your studies on programming languages. Your priority should be SQL — the standard language used to manipulate data. You need it to extract information from databases. You will also need to know how to use data processing tools like Hadoop and Looker. It also helps to familiarize yourself with common languages used in data science, like Python and R, to spot data mistakes faster. This leads us to our next point.

Most of the time, you’re going to be cleaning data

Data cleaning is the process of removing errors and inconsistencies from your databases. After all, missing fields, duplicates, structural errors, unwanted outliers, and even incorrect clusters can ruin the quality of your analysis.

When cleaning your datasets, it helps to keep the following questions in mind:

• Does the information gathered make sense?
• Does the data follow appropriate rules for its field?
• Can you find trends in the gathered data to help you form your insights? And if not, is that because of a data issue?

Do consider learning how to use data cleaning software like Xplenty, RingLead, and Oracle to get the job done more efficiently.

You need good communication and presentation skills

A data analyst’s job isn’t just to interpret data, but to make other people understand it as well. Learn how to visualize your data using charts, indicators, and labels. Even though all your findings can be compiled into one figure, a busy graph will only confuse your listeners, and whatever potential your data has might just end with you. Data scientist Kurtis Pykes’s guide to effective data visualization notes how you should have a separate graph or chart for every question your employers want to be answered.

Furthermore, your written reports need to be clear, simple, and concise. Learn how to explain your insights without technical jargon.

Overall, becoming a data analyst has its challenges, but proper knowledge on the subject can help you overcome most of them. Besides, whether you pursue a degree, enter a program, or learn via online classes, many of the skills you need to succeed can be built as early as today.

Article written solely for careerera.com by Jill Berry

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