Key Skills Expected from a Successful Data Scientist
In order to be a step ahead of themselves and to stay fit for the market companies need to depend on keen critical analysis of humongous amounts of data. No wonder the role of a Data Scientist is among the most coveted jobs of the 21st century across industries, around the globe. Lisa Qian a Data Scientist in Airbnb reveals in an interview, “Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data.” A crucial mix of technical skills, like analytics languages and algorithms along with non-technical abilities is required to be a successful Data Scientist.
The Basic Tools and Technical Skills:
An analysis of 3,490 data science jobs posted on LinkedIn provides a clear view of the key skills that can get you hired as a data scientist. Different companies with various and ever shifting goals can ask for any or multiple of a long array of services from the job holder. It is quite obvious that the skill set required is multifaceted.
1. Computer Science Knowledge: It often occurs that someone is required to take charge of a crucial development in your company’s data management scripts or structures; with a bit of knowledge in computer science you can chip in.
2. Data Munging: It is as important as difficult to deal with the little imperfections in data. Missing values, inconsistent string formatting and date formatting such imperfections are common and have to be dealt with. Efficiency in cleaning up messy data is a key skill no doubt for every data scientist. Reasonably Python and R make it to the list of the most essential skills required to get hired as a data scientist.
3. Statistics and Modelling: If you are responsible for data science, a lot of decisions regarding a product’s design and up gradations will be dependent on the insights provided by you and a well directed statistical knowledge will come immensely handy and in most occasions, essential. If we think of the theoretical Unicorn, in depth and formal knowledge of statistics seems to be indispensable; while I dare not wish to undermine the need of a minimal statistical prowess for a successful data scientist, Facts confirm that there is little need to be obsessed with it.
4. Machine Learning: Large scale machine learning and widely used algorithms like support vector machines, k-nearest neighbour, Random forests, ensemble methods can be crucial if you are working with large amounts of data or if your company’s product is data driven.
5. Updating and Retrieving Data: Using database systems is usually the first step toward any form of data analysis, making predictions or generating insights. The most popular tools for this kind of tasks at this point are SQL and Hadoop. If you are an aspiring data scientist you do not need me to tell you how important these are; let us just say that these are basic.
Effective Communication Skills: Like almost any job that you can think of communication skills are indispensable for the success of a data scientist. You, as a data scientist help a set of professionals to decide the path ahead and to take the best decisions by offering insight into the patterns that you decode. It is your responsibility to quantify your findings and make sure that your technical and non-technical counterparts understand you and can do what is necessary.
Curiosity: The world of data science is one that shifts fast; it is a necessity to be aware of what is happening all around you, and you have to be curious for that. It might occur that you were so busy with your once trendy skill that you did not even realize when it lost currency. Your intellectual curiosity will prevent this kind of professional calamities.
Domain Knowledge: You must know why you are doing what you are doing. However technical your task may be you have to understand the business end of it otherwise the job often becomes fruitless for the company, for your teammates and of course for your growth. As Lisa Qian says a data scientist is “involved in every step of a product’s life cycle”
The scope of a data scientist is hard to define and harder to measure there are exceptions at every point with requirements never constant. There is yet a lot to be discovered.