Over the last few years, interest in data science has grown tremendously and the domain has emerged as a lucrative career. Whether you want to Learn data science or are eyeing the position of a data scientist as a probable career move, you need to have certain skills – both technical and non-technical, to succeed. Be it running a complex database query, or communicating with data producers and users in your company, you need to equally adept in various data scientist skills that would help you become successful in your chosen field.
Here are the top ten data scientist skills that we believe are essential to posses:
“Measuring programming progress by lines of code is like measuring aircraft building progress by weight.” — Bill Gates
Data scientists need to be proficient in various programming languages and software packages since they have to use these efficiently and flexibly to pull out, clean, examine and visualize data. Python, R and SQL feature among the top three data scientist skills that are most frequently mentioned in relevant job postings. Since these three are closely interconnected, they are often called the “bread and butter” skills that every aspiring data scientist should learn.
2. Statistical sensibility
Though you will get software to execute all the necessary statistical tests, it’s your statistical sensibility that will help you decide which specific tests to run, when to run them and how to deduce the results. Apart from a robust understanding of linear algebra and multivariable calculus, you also need to Learn analytics (especially quantitative analysis, which is the key among all data scientist skills), all of which will facilitate creation of in-house executions of analysis routines, as and when required.
3. Machine Learning (ML)
Even when you don’t implement machine learning models, having ML knowledge would let you help in the creation of prototypes with the objective of choosing and generating features, examining assumptions, spot areas of opportunity and strength in existing ML systems.
4. Data mining
This involves analyzing datasets to interesting patterns. As a growing number of businesses and others these days are relying heavily on Big Data (which involves storage and processing of large data sets on a huge scale), data mining – especially of Big Data, is getting a lot of attention at present, which has made data mining one of the most sought after data scientist skills in today’s job market.
5. Big Data processing frameworks
With the growth of Big Data, understanding big data processing frameworks platforms like Spark, Hadoop, Apache Samza, Apache Flink and Apache Storm has become vital since these have become a significant element of the data science realm.
6. Managing unstructured data
Data scientists need to manage both structured and unstructured data. SQL rules over structured data (or relational data) but storage and interaction of unstructured data isn’t as straightforward as structured However, the sound knowledge of 2-3 popular NoSQL database system implementations (such as CouchDB, MongoDB, Druid, Cassandra etc) would be the key to storing, recovering, evaluating, and otherwise processing this unstructured data.
7. Effective Communication
This is one of those data scientist skills that can make all the difference between a good data scientist and one who is great. This skill can manifest in different ways. For one, presenting data in a visually compelling way (rather than using words, texts or a graph) is often more effective to drive home a point. Similarly, communicating insights in a concise way with clarity too is important to enable others in the company act fast and effectively on them. Since data scientists often work as a part of the team that consists of designers, engineers, product managers and others, being adept in communication would facilitate good understanding and build trust. This in turn will help in faster and streamlined work, which is crucial for someone who is seen as the custodian of a huge pool of data.
As mentioned before, being a good team player is crucial for becoming a successful data scientist. However, when you aspire to learn data science, developing good team player skills doesn’t just mean having good communication skills. It goes beyond that. For one, you have to focus on the bigger picture and put the company’s goals ahead of your own personal career ambitions. Being ready to offer other team members help and mentoring the novice members are other teamwork skills that feature prominently among non-technical data scientist skills. Since this profession requires quick feedback and back-and-forth iterations to arrive at effective solutions, being a good team member whom others are ready to help is crucial. Even for your professional growth, sharing your methods, knowledge, and results with others and learning from them in return would go a long way, since you can never have the complete set of skills and are always developing your arsenal by learning about newer frameworks, techniques, tools and languages.
9. Intellectual curiosity
If you don’t have it, you are simply not cut out for the job of a data scientist. Many consider data science to be an extremely diverse field, where it’s often difficult to arrive at a real consensus of what it actually entails. Today, you will find data scientists playing a wide variety of roles in organizations, which could be anything related to varying levels of technical and business skills to domain, communication and interpersonal skills or sometimes, even more. In such a scenario, unless you are curious to learn about new techniques, tools, implementations, Development etc in the data science landscape, you won’t be able to keep pace with the changing trends and demands.
10. Business insight
Becoming a data scientist doesn’t just need you to learn analytics or crunch numbers. It also demands you to have a solid grasp of the industry you are working in, and have a clear idea of the problems or issues plaguing your company that you need to find an answer to. One of the crucial data scientist skills is to determine which problems or issues are critical for the business and which need to be solved on a priority basis together with recognizing new ways the business can leverage its data to create a big impact.
- Advanced Degree – More Data Science programs are popping up to serve the current demand, but there are also many Mathematics, Statistics, and Computer Science programs.
- MOOCs – Unanth, Udacity and codeacademy are good places to start.
- Certifications – Unanth has compiled an extensive list.
- Bootcamps – For more information about how this approach compares to degree programs or MOOCs, check out this guest blog from the data scientists at Datascope Analytics.
- LinkedIn Groups – Join relevant groups to interact with other members of the data science community.
- Data Science Central and Unanth – Data Science Central and Unanth are good resources for staying at the forefront of industry trends in data science.
The Job Search and the Interview
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” —
James L. Barksdale
So you’ve learned your skills, networked, and are now ready to begin working as a data scientist!
The Job Search
The first step is to begin your search for a new job, a lot of this will vary depending on your personal circumstances and goals.
One of the best ways to begin your search and practice your skills at the same time is to participate in Kaggle challenges and blog about your experience with them. Some Kaggle challenges can even directly lead to Interviews as part of the prize! Even if nothing comes of the prize, its still valuable experience on a real data set!
Freelancing through sites like UpWork, contributing to open-source projects, and answering questions on StackOverflow is another great way to make your presence known to recruiters.
You will also want to make sure that your CV, LinkedIn, and Github are all updated to reflect your new skills and projects.
Make use of sites like Indeed or DataJobs for a general job search, of try out sites like Triplebyte that directly give you a series of technical interviews to quickly go through the initial interview phase for many companies at once. You can also check out startup jobs with the AngelList Job board and HackerNews Job Board.
For better or for worse, many companies still rely on classic interview questions that involve Data Structures and Algorithms. To prepare for these sort of questions you should review topics such as Arrays,Graphs, Recursion, Linked Lists, Stacks, etc… you should reference a book or course, and go through lots of practice problems!