In 2020, nobody can ignore the importance of data. It’s everywhere and fundamental for company operations. Without it, we go back twenty years - perhaps more. 

Therefore, many smart people are learning data skills in an attempt to progress their careers. Some are exploring tools, like Python, Excel, and Tableau, while others are going into basic statistics and regression analysis. Learning these skills can be a challenge, but it is also precious. People who can get to grips with math often find themselves at a distinct advantage in the job market. 

Statistical Knowledge

Anyone who wants to contribute to an enterprise’s data capability needs a grounding in statistics before progressing onto more specialist areas. The idea of learning statistics might seem a little daunting. Still, once you understand the basic principles, the whole edifice fits together quite nicely. 

Statistics is all about coming up with hypotheses and determining whether you can falsify them using statistical methods. You take a bunch of data, feed it through a mathematical operation, and then see whether it falls within a confidence interval. If it does, you can say with confidence that your hypothesis can either be rejected or not.

For businesses, tools like these are invaluable. It allows them to avoid making decisions based on hunches instead of cold, hard facts. Some hypotheses will still be wrong, but that shouldn’t matter in the long-term. By the law of large numbers, everything should even out. 

Data Visualisations

We also see a massive shift in the way that companies present data. You can click here to learn more. Essentially, developers are making software that allows practically anyone in an organization, including non-technical staff, to create data illustrations.

These charts are primarily to help other people understand what is going down in the depths of the data. The software allows anyone to take data from a central database and then manipulate it in any way that they want. 

Data Cleaning

Data cleaning might sound like a bit of a dull task, but it is in high demand. Companies need people who can prepare data for numerical or statistical analysis. 

Evidence from industry suggests that data cleaning is the number one way that people in the industry spend their time. Sorting through data and preparing it for analysis eats up around 80 percent of the typical data professional’s week. 

The task isn’t perhaps the most exciting that you’ll encounter as part of your work, but it can be a great problem-solving exercise. You’re looking for tidbits of information that don’t quite conform to your analytical requirements and sorting them out. When you finish cleaning a data set, it can be a rewarding experience. 

Writing And Communication

Data science isn’t just about learning complicated methods to analyze data. Companies also need people who can explain and present it too. Communication skills are, therefore, vital. 

Talking about statistical results is a massive challenge, especially when speaking to a lay audience. So too, however, is understanding the needs of stakeholders. Sometimes a non-technical colleague will ask you to conduct data analysis, but won’t know precisely what they’re asking for. In situations like that, it’s your job to fill in the gaps and come up with something that approaches their basic questions. 

While it might sound like a soft skill, interpreting instructions and then delivering work that offers insight is no means easy. Doing the analysis is one thing, but communicating it in a way that people understand is quite another. 

Domain Knowledge

Industries tend to have varying data requirements. Those in the retail sector, for instance, need people who understand how to interpret eCommerce store traffic data. Others in the mining industry need to have skills in assessing the safety of workers. 

Domain knowledge, therefore, is becoming increasingly important. Companies want people who have data skills and experience to know which questions to ask and answer. It is by no means easy. 

Domain knowledge is perhaps the best way for people to increase their lifetime value in their careers. Companies need people who can take their data and put it to good use immediately. Smart people, therefore, focus on learning on a specific sector and then use that to their advantage when seeking work. They’re learning as much as they can about their chosen area and leveraging that during the interview process. 

Companies love data because it provides them with new problem-solving abilities. Employees can delve into any number of data streams, analyze them, and sniff out issues holding firms back. Smart people, therefore, are learning the skills and boosting their value.