A career in Data Analytics is very appealing. There are a plethora of opportunities available across a range of industries. There is loads of room for growth, and the pay is not too shabby either! But is it right for you? Check yourself with this list to see. If a career in data analytics is really the path you should be following.
Do you like working with numbers?
Numbers are integral to data analytics so, you must not only like working with numbers. You must love it, and be good at it. Essentially data analytics uses two skills:
Using statistical techniques to solve a problem or gain insight,
Interpreting and communicating the solutions and insight in an easy to understand way.
If you look at numbers in a data set and see a puzzle that needs solving. If you get excited at the prospect of applying your math skills to discover clues that will lead you to solve the puzzle. The data analytics is for you!
If you are inquisitive and not put off when faced with a problem you don’t know how to solve. If you like nothing better than to wade through data to find a usable solution or actionable answer, then data analytics is for you.
You must also be able to take the data and statistics and present them such a way that they tell a story that is easily understable by anyone. This entails using ordinary words and visuals, like graphs and charts, to present your findings.
Sometimes, people who have a business degree, or major in statistics or another math-related field, aren’t necessarily cut out for a career as a data analyst. Data analytics requires more than technical skills and aptitude.
You need to be really comfortable with using statistical models and techniques to glean insights and interpret data. You need to have a sound knowledge of coding, data modeling, analysis, data visualization, and the ability to create insights that people who aren’t quantitative can interpret.
Do you know how to code?
Whilst a computer science background is not necessary for a career in data analytics, knowing how to code is. It’s all well and good being passionate and good with numbers but you also have to have an affinity for working with them programmatically if you are going to be a data analyst.
SQL is the standard language for manipulating data, so you should be proficient in it. Python is another must, along with the Python Data Analysis library, Pandas. Part of being a data analyst is wrangling and cleaning data. Sometimes this data is too big to process on an ordinary computer and it becomes necessary for you to have big data skills. Knowledge of Hadoop and Spark becomes vital to your toolbox.
These big data tools shouldn’t be difficult to pick up for a data analyst who knows how to programming. Often, code-based analysis is difficult to learn if your experience is limited to point-and-click.
Excel, despite being regarded with a pinch of disdain by data analysts and scientists, is a programme that you should be proficient in. There are still an enormous amount of businesses that use Exel for their data and, as a data analyst, you need to be prepared to work with it.
The reason that the experts are so disparaging about it is because you are essentially doing the analysis manually which is not efficient and doesn’t create reproducible results. This is not conducive to efficiency because most analytics entails data out of Excel and put it into a form which is suit to reproducible analysis.
A data analyst needs to know how to code as well. As be proficient in a few programming languages but not at a level that requires a computer science degree. The most important thing is to be able to understand large datasets.
Do you know how to communicate effectively?
In data analytics your communication skills are just as important as your technical skills. You need to be able to talk through machine learning algorithms, you need to be able to speak to the bias-variance tradeoff and what you can do about it, and you need to be able to talk through an analytics problem you’ve solved, end-to-end. You also need to be able to communicate your findings and recommendations to directors , colleagues, or clients in an understandable way without the jargon.
Communication skills are not exclusive to verbal communication. It is also essential to be able to visually communicate a problem, process, and solution. This means that data analysts need to be visually literate and have a good design sense.
A good attitude is also essential to communicating effectively. With data science constantly evolving an analyst needs to be able to say when they don’t know something and not let their egos get in the way. It is impossible to know everything so having a humble attitude mixed with a self-starting, can-do approach to life is needed in the data analytics space.
Are you tenacious?
Data analysts do a lot more than what their job description implies. You may think that data analysts spend their days exploring data, looking for clues and information to answer business questions, solve problems, or producing easy-to-understand presentations of their discoveries and solutions.
Most of the time, data analysts spend collecting, cleaning, and processing data. This entails finding bugs, bad coding, or transcription problems. You will also need to ensure that data is consistent. For example, if a time field recorded the local time of the person inputting the information. It will need to be adjust to one uniform reference like Greenwich Mean Time, before the data can be analyzed.
Before a data analyst can even start to model and analyze data. The first thing to do is to do a lot of housekeeping. A data analyst spends ninety percent of their time cleaning data. If you’re tenacious and dedicated enough to constantly be cleaning data. A career in data analytics could well be for you. Go for it!