Data science has witnessed a huge surge in the recent years. The increasing demand for data science professionals proves the plethora of opportunities available in the market. And to have an added advantage, data science professionals should focus not only focus on technical skills but also enhance practical and soft skills.
Data science and machine learning are “hot” topics now-a-days. The number of job opportunities are on a high and good pay packages are being offered. And hence, there is a surge in the number of institutes providing learning and coaching on data science and machine learning. Still there can be a confusion on the skills required by a data science professional. We are covering the most useful skills for having a good career in data science. They are not the exhaustive list but a very good resource indeed.
Here are three most important skills data science professionals should consider.
1. Technical skills set
Programming and visualization tools: You should have good programming and debugging skills. While the expectation is not to be the ultimate programmer, but above average programming skills are handy. Python, R, SPSS, MATLAB, Julia, SQL, Tableau, Qlik, COGNOS etc. – might prove to be of much use. Data engineering skills and big data knowledge proves of additional advantage. End-to-end model development including deployment of the model are the skills praised by everyone.
Data analysis: Ability to slice and dice data, look out for patterns and anomalies, insights, relations between variables – all will allow you to understand the data better. It will help in generating the actionable insights useful for business decisions.
ML concepts: A good knowledge of machine learning concepts is required. While it is very difficult to know each and every algorithm in the world, you should know at least some machine learning algorithms like linear regression, logistic regression, decision tree, random forest, gradient boosting, means clustering, hierarchical clustering, PCA, SVD, apriori, collaborative filtering etc. Deep learning is gaining a lot of popularity – so understanding the concepts of neural networks is useful.
Database/SQL: Data science cannot function without data. Good knowledge of using SQL for data mining and analysis is required.
Statistics and mathematics: While the expectation is not to be the ultimate expert on statistics and Mathematics, and above average knowledge of statistics and mathematics is always handy. With the due course of time and experience, the knowledge of statistics and mathematics can be enhanced.
2. Practical skillset
Self-starter: A person who is a self-starter, and can take initiative is always admired in the workplace. There are many instances where you might be required to take initiative.
Inquisitive: It is imperative that you should be curious about what you are doing. Are there any relationships, anomalies, patterns, seasonality, correlations present in the data set. You have to be questioning the reasons for those anomalies and those patterns.
Accountable: Having the ownership and accountability of the project is admired by everyone. You should feel the responsibility and be accountable for the success of the project. Having decent project management skills can prove to be really useful.
3. Soft skills
Communication: The art of storytelling is very important in this role. You should be able to communicate your thoughts well. At the same time, it is imperative that you’re able to understand the business problem very well.
Domain knowledge: Having sound business knowledge is required. Any domain – retail, telecom, banking and financial sector, aviation, Pharmaceuticals, manufacturing, utilities etc – having a good business understanding will allow you to address the business problem. It will ensure that you understand the pragmatic challenge very well and will be able to design a solution to solve that business problem.
Collaborative: The profile requires collaborating across different departments of the business. Departments like marketing, product, CRM, operations, quality, automation, pricing, IT and infrastructure teams – hence it requires a lot of collaborative and team-spirit skills.