Being a data scientist requires a mix of skills that anyone can develop. You only need patience, time and be willing to undergo a process of trial and error. You need to understand businesses and be able to adapt to different situations according to the business’ needs. Another important skill is basic statistics to understand how the algorithms work and remove the idea that the algorithms are magic, it’s just math and computational power. Although some programming skills are needed to develop the models, you do not need to be a programmer.

Business

Understanding the business is the first key to succeed, you not only need to know how to develop the algorithms but also you need the ability to find value in the data and understand the business environment around the data, how it is being generated, by whom, how is it being stored, which are the privacy conditions and what is the most important how to take advantage of the data and which are the ones benefited from the value generated from it.

This skill is the most complicated to have, since each business is different; each business has particular characteristics and needs. So, you have to be prepared for anything and try to adapt to circumstances.

This skill is also difficult to teach, it is something that you learn as you go. A good way to prepare yourself to build this skill is studying how other companies used the data, which are the ways that it can be monetized, and who might be benefited from it.

Statistics

Certain knowledge of statistics is needed to understand how algorithms work and the data is distributed.  All the algorithms are based on algebraic and mathematical concepts that handle probability and statistics issues. Sometimes people just execute machine learning algorithms without knowing what they mean or do. Doing that is very risky,  because you won’t have the tools to interpret the outcomes or detect errors in the code. It is important to understand what is going on behind the box, being able to trust the algorithms and be sure that you can explain the results and base decisions on them.

Analytics

Data analytics is the science of analyzing the available data to make conclusions. Many of the techniques used to process and analyze the information have been automated into algorithms that can process more information in less time. Machines can help us to reveal trends and patterns in the data that might have significant value for the business. These findings can help organizations to predict and improve business performance.

The three main types of analytics are:

• Descriptive analytics: aggregation of data to provide insights from past records.
• Predictive analytics: prediction of future outcomes based on historical data.
• Prescriptive analytics: recommendation of best course of action in a scenario from the available data.

Communications

Communicating your findings is fairly important. If you are not able to communicate to your boss/client/team what the data is saying, you won’t be able to get any value from it. So, it is very important that you practice and develop these skills. It is about how you present the results graphically and as well as how you explain them. Sometimes the algorithm gets so complex that it is difficult to explain to others that are not technical or the results might seem obvious but for others it is not. Thus, put yourself into the other person’s shoes, before presenting your results in a formal meeting, by validating with someone less technical or not from the project team to see if your presentation is understandable.

Planning

Last, but not least, you need to establish stopping points, since the vast number of possibilities and the huge variety of algorithms. From the beginning you should have a clear vision of what you want to achieve. As any development process, planning how is going to be your project, how you are going to divide the activities is fairly important. Define how much time you are going to spend either on researching or trying to improve the performance of your model. You would need to stop at some point!. This point has to be transparent for each of the parts involved, and it has to be followed closely during each phase of the project in order to take actions and make adjustments if needed.

Conclusion

Data scientist’s job is a hot position, there are many areas where data science can be applied, so we would never get bored. In addition, you would need to work in cross-functional teams, with different people’s profiles to complement each other, share knowledge and skills to get the best value from the data. Each day there is more and more data to analyze. Thus, demand for data scientists is increasing rapidly.

So, becoming a data scientist means a safe job position as well as well-paid, it is a fun activity, you work in multidisciplinary teams, and you are always challenging yourself. What else do you need? Oh, yes there is more! Data will surprise you!