Want To Be A Data Analyst? These Five Skills Are A Must-Have.
With the consistent increase in the rise of data-driven positions, more people are seriously considering taking up a career as Data Analysis. Taking up a career in this field requires core technical skills. Of course, attaining excellence will demand an evolving skill set.
Every Data Analyst is driven towards providing reliable business solutions. These essential skills will help you establish yourself in today’s world of data;
1. Computer Skills
First of all, tech experts should have a vast knowledge of anything that deals with a computer. A Data Analyst actually needs to know more than the basics.
Depending on the company’s existing platform, virtually every business program has to interact with data in some way. As a data analyst, once you have an in-depth knowledge of coding, it becomes easier to adapt to several frameworks and apply them to business progress.
Building Queries To Extract The Information You Want Is Important For Data Analysis.
Data management and manipulation skills are critical. This is achievable through languages such as SQL (Structured Query Language), HIVE, R, or Scala.
SQL is the ubiquitous industry standard database language and a vital skill for analysts. It is often taught as an advanced version of Excel but SQL can handle large datasets that Excel simply can’t.
Basically, this skill is required in a lot of organisations – could be to relate, change or build multiple databases, or to manage and store data.
Similarly, R or Python can do what Excel can’t, 10 times faster as well. They are powerful statistical programming languages, used to perform advanced analyses and predictive analytics on Big Data sets.
In order to scale up your experience as a data analyst, you’ll need to go beyond SQL and master at least one of these languages.
Don’t forget, a chunk of your job will be to create efficient and accurate reports using tools that can spot patterns and forecast trends. These common solutions can help you; Microsoft Power BI, SAS, Oracle Visual Analyzer, and Cognos.
2. Analytical and Creative Skills
Solving problems with data isn’t quite easy, data collection in this age can produce enormous data sets. Your job, as an analyst, is to unveil and synthesize vague connections – this means you have to figure out the right questions to ask, which can be very tricky.
It’s why you must be critical in every approach, and “Think” like an analyst.
You have to understand techniques for cleansing, organising, and structuring data to provide reliable results. This means defining data rules to work seamlessly with data technologies.
More so, the harder you work towards your investigation and verification skills, the better you become at providing the right information for the right business problem.
While this ability is innate to a certain extent, there are a number of tips you can try to help improve your critical thinking skills.
3. Numerical and Statistical Knowledge
This is an obvious reality. Thorough mathematical skills with formulas and statistics are required to supply data, provide real-world value and satisfy common business needs. Also, you have to be great at expressing numeric results and combining figures to consistently produce new measures.
4. Business and Communications Skills
Some data analysts are good in business and statistics but not in programming. On the other hand, there are expert programmers that don’t understand how to put the programs in the context of the business goal.
Understanding the analytical and business part is one huge advantage.
As an analyst, it’s your responsibility to provide exact information to your employer or the decision makers. Understand that data isn’t enough, you must understand the major requirements of the end users too.
Data visualization and presentation skills go hand-in-hand. You’ll have to discuss and collaborate with IT specialists, team members, executives, and clients. Even though your presentation and communication skills aren’t well seasoned, you can always get better with good practice.
5. Attention to Details and Data Purity
As much as you need to think critically and be quick about it, you must be able to pay attention to detail. Recognizing incorrect or superfluous information is critical to achieving useful results. A simple mistake in the information that business units receive could lead to incorrect conclusions, which in turn leads to loss of money and valuable time.
As a data analyst, you can choose to widen your horizon and dive into the pool of Machine Learning and predictive modeling. Although not every analyst has to work with Machine Learning, the tools and concepts are important to learn in order to get ahead in the Data Science field. However, your statistical programming skills must be top-notch before you proceed to Machine Learning.
If you have a thing for divergent thinking, a tool like Orange can also help you start building Machine Learning models.