4 Things You Should Know For A Career In Data Analytics
1. You have to like working with numbers
"Doing data analytics makes use of two skills," Howe says: "One, statistics, and two, telling a story with those statistics in ordinary words.
"If you're going to be a data analyst, you must know how to use statistical techniques accurately. You have to like and be good at working with numbers. You have to be able to see data like a mystery or puzzle, and think, 'There's something in here that I want to discover.' Then you apply your math skills to find clues and eventually solve that mystery."
But that's only half the story. Jobs in data analytics focus not only on the numbers but also on how we communicate insight, Howe says. "You're turning data and statistics into a story that can influence others. That story probably has to be told in pictures, because that's the way we internalize information quickly."
2. You have to know how to code, but you don't have to know computer science
Along with a love for numbers, data analysts need an affinity for working with them programmatically.
"You should learn to code, for reproducibility so others can build on what you've done," Howe says. If you can't write down a program that does what you are doing, "you're left with two choices: teach others how to do it or keep doing it yourself forever."
What computer languages and other software tools are most likely to be useful for a data analyst? SQL is essential — it's the standard language for data manipulation. Other useful options:
Python - "Pick a graphing library in Python and get to where you're pretty good at it," May says, "and learn Pandas, he Python Data Analysis library."
R - a free software environment for statistical computing and graphics. "R is written specifically for data analytics and science," RStudio's Howe says. However, URSA's May says if you don't already know R, it may not be worth your time to learn it. "If you mostly deliver one-off answers that don't get put into production, R is OK, but If you're shipping code to production, R gets really tricky."
Hadoop - a collection of tools for processing large datasets, and Spark, a fast and general cluster computing system for big data. "I think anyone who gets hired as a data analyst and can't wrangle and clean data will struggle in a real-world environment because at least 90% of an analyst's work is cleaning and transforming data. If your data set is large enough that you can't process it on your laptop, you need big data (and usually cloud-based big data) skills such as Hadoop and Spark," says Howe.
Cloud-native and desktop analytics platforms, such as Looker, Tableau, and Power BI. However, notes Carl Howe, "Many of those tools are simply cloud-based versions of point-and-click visualization tools, which rely on manual and irreproducible processes for analyzing data. If you're an analyst who knows how to use a programming language, you'll have no trouble picking up those tools if you need them. On the other hand, if your skills are primarily in the point-and-click world, you'll find it difficult to make the transition to a code-based analysis environment, which is where hardcore data analysts work."
Excel. Howe says, "Data scientists and analysts love to disparage Excel, but the reality is that many businesses run on Excel data. A good data analyst can build a dialog with end users and find ways to work with those users, and in many businesses, that may mean working with Excel data. The problem with Excel, though, is that it guarantees that you are doing the analysis manually, using your mouse and keyboard. That's not a recipe for creating reproducible results. Most analytics isn't doing one-offs, but doing things over and over again, and you always want to do them in the same way. The trick usually is to get that data out of Excel as quickly as possible and put it into a form more amenable to reproducible analysis."
3. Communication skills are (almost) as important as math
You may have the technical chops to handle data analytics, but that might not be enough to get hired. What else do you need to ace an interview?
"One, make sure you can talk your way through a number of machine learning algorithms," says URSA's May. "Two, be able to speak to the prediction models — and what you can do to/about it using SQL. And three, be able to talk through an end-to-end data science or data analytics problem that you've solved — what the problem was, your solution, and how you dealt with the roadblocks you encountered along the way."
Silipo says, "I look for many different things when I run interviews for these people and positions. First of all I look for technical skills. I give them an exercise and see how they approach it, how their way of thinking is, and whether they have the right math background. This applies to both data analytics and data science.
4. Much of what you'll do won't be at the top of the job description
You may have an intuitive idea about what a data analyst does, but what you imagine might not line up with how you actually spend your time. URSA's May says, "Mostly, you'll be thinking about a problem or question, and how you can use data to potentially solve or answer that. And you'll be doing EDA — exploratory data analysis — which means seeing if you can find a signal that can help answer that question or problem."
Some of this may get done on a blackboard (traditional or digital), and some with coding, May says. "You do EDA by writing code. There's also writing the code for the model that you build, and the code to create the graph to show your answer, and the code for the statistic you're going to derive."
Once you think you know how to answer a question, you still have a ways to go before you create a report or a visualization. "One irony of both data science and analytics is that while you need to know a great deal about models and machine learning, you will usually spend as much as 90% of your time cleaning real-world data before you analyze it. It's the old story of 'garbage in, garbage out,'" says RStudio's Howe. "You need clean data to work with before you can model it."
