In guest lectures I give at universities, I often refer to the Harvard Business Review report which states that being a Data Scientist is the sexiest job of the 21st century. Naturally, this always seems to capture the students’ attention, and drives their enthusiasm to sit up and listen carefully. As a result, one question I am consistently asked by inquisitive students is, “What skills do I need to become a Data Scientist?”
In my experience with solving analytical problems and conversations with customer looking to hire their next Data Scientist, I have drawn out the 5 most sought after skills you need to consider when applying for a Data Scientist role. Here are my top 5:
5. Know how to develop a predictive model using regressions and decision trees
This doesn’t sound too sophisticated to a pure statistician, I know, but businesses want to know the best outcome for a particular event. The most common business questions asked are, “Which customers are most likely to leave? Which customers will take up a product? Which customers should I approve for a loan?” In most cases a regression or decision tree results in an easy to explain predictive model to address these questions. More importantly, they are easy to productionise to meet the organisation’s demands.
This is a data science skill that contributes to companies creating a more targeted customer experience to increase profits while reducing marketing spend – a fantastic result for organisations!
4. Know how to develop a segmentation model
The first thing organisations want to do with their database is understand the characteristics displayed by their customers. And of course, from a marketing perspective, they want to know what groups of customers look like and what makes the groups different. Applying the skill of clustering (and there are many different kinds in this discipline), to obtain cohorts or segments that are distinct, is extremely valuable in driving successful business outcomes. It may be one of the first tasks you are asked to perform in a Data Scientist role.
3. Know how to use SAS with R
It is no secret that R is the common tool of choice for many students graduating from Information Management courses. But the reality is, when you need to apply analytics to commercial corporate data that is often exponential in growth, you must be able to incorporate R skills with SAS skills. Organisations have invested in analytical enterprise platforms that are often already embedded successfully into the organisation's model lifecycle environment. So, those who bring a variety of both SAS and R skills will make valued Data Scientists.
2. Know how to access relevant data quickly
About 80% of a Data Scientist's work is focussed on knowing where the appropriate data is housed and how to access relevant data quickly. In my experience at one particular company I worked at, I developed a data dictionary for every data source I needed to access. The data dictionary was like the Holy Grail to fast and accurate data extraction, and let me get on with the science of deriving insights from the data. Everyone wanted a copy of the data dictionary… even IT!
1. Know how to articulate your analytical results to drive business outcomes
Communication is critical to your success. I often practice communicating the business outcomes of my analytical results with colleagues that aren’t analytically inclined. Once I know that they understand the value of the results from a business context, only then am I satisfied that my analytical results are actually useful to my organisation. You need to move away from statistical jargon and be creative with how you illustrate data and models in a business friendly manner. Do it in a fashion that is easy on the eyes and ears – you don’t want to scare people away, but rather welcome them into the journey of business analytics.
The tips described above aren’t rocket science. However, they’re not something you develop overnight either. If you can practice these skills continuously, and keep them at the front of your mind when applying for a Data Scientist role, then you will be a step ahead of your competition. Success awaits!
All opinions are my own, and based on my experience, conversations and feedback from the professional field and customers looking to hire quality Data Scientists.