It seems like every other month, someone’s out there touting the new best thing in the data science industry or must-have data science skills. The first step in honing your data science skillset is figuring out what skills you’re good at and what needs to be improved. Being a successful data scientist means you have to have a diverse set of skills and be adaptable to changing data science tools and technologies. Every data scientist will bring their unique blend of skills and experience to the role, obviously, but we’ve highlighted what we think are the much-needed skills for a data scientist.
If you are exploring a data scientist role and are wondering if you have the required data science skills and experience, here is the answer. What follows is a list of the most critical data science skills for new data scientists to bring to the table. These skills will give aspiring data scientists the most opportunity to succeed in their careers.
Now, right onto the must-have data science skills.
Knowledge of Programming Languages: Python, R, or SAS
Programming is a key skill to get started with a career in data science. Having hands-on experience with languages and tools is important to dive into the data science industry. You can then make use of statistics skills and programming to analyze, dissent and interpret huge volumes of data and make use of the technical skills to build better analysis tools, design frameworks for automation, develop better visualization techniques, and a lot more. One should ideally begin with learning one of Python/R/SAS that will help a long way in climbing up the success ladder as a data scientist. Having learnt programming, you can acquire experience in tackling various data problems using a diverse set of tools and technologies for data cleaning, processing, exploration, and machine learning.
Knowledge of Machine Learning and Deep Learning
Having knowledge of various supervised and unsupervised machine learning techniques like decision trees, logistic regression, random forests, etc helps solve various data science problems based on predictions of important organizational outcomes. However, there are several data scientists who are not proficient in machine learning techniques like neural networks, adversarial learning, survival analysis, and reinforcement learning. Machine learning is an important skill for data scientists but is one of many. Data science requires the application of skills in different areas of machine learning. The availability of various machine learning tools makes this easy for professionals with limited machine learning expertise. Having good hands-on knowledge of supervised and unsupervised machine learning techniques will make you stand out from other data scientists.
There is a popular saying –“A picture is worth a thousand words.” It is easier to understand insights in the form of appealing charts and graphs than raw data. One crucial skill that many people tend to miss out is data visualization. Having knowledge of data visualization tools like QlikView, D3.js, Tableau helps you convert complex analytic results into a format that is easily comprehensible by people in technical and non-technical roles. Most of the stakeholders and business leaders do not understand the output of machine learning models. In such cases, data scientists can make use of visualizations to explain what those insights represent and their business implications.
Math and Statistics
Math and Statistics are among the most powerful tools in a data scientist toolkit for performing the art of data science. As a data scientist, one will not just make use of complex techniques like neural networks to glean insights. Simple linear regression analysis is also a kind machine learning algorithm that every data science enthusiast starts with. Just by plotting the data on a chart and analyzing what it means is one of the essential first steps in the data science process.
A basic visualization like a histogram or a bar chart just gives some high-level information but with statistics, data scientists get to work with data in an information-driven and targeted way. The math involved in performing technical analysis of data helps draw concrete conclusions rather than just guesstimating. Having a good foundation in math concepts like rational and irrational numbers helps data scientists write accurate and efficient code.
Following are the basic math and statistic concepts every data scientist must know –
- Statistics and probability theory,
- Probability distributions,
- Multivariable Calculus,
- Linear Algebra,
- Hypothesis testing,
- Statistical modeling and fitting,
- Data summaries and descriptive statistics,
- Regression analysis,
- Bayesian thinking and modeling, and
- Markov Chains
However, if you ask how much you need to know about these statistical concepts, then this heavily depends on the kind of data scientist job description you’re looking at because that is a real reflection of the data scientist job role that you yearn for.
Problem-solving is the most critical data science skill because data science is all about solving challenging business problems. Without business problems, there wouldn’t be a need for a data scientist. As a data scientist, it does not matter what technology or programming language you use, if you cannot solve business problems, you won’t be very good at developing algorithms for the same. We constantly hear complaints about job interviews that are too difficult to crack because they ask the candidate to solve some difficult business cases at hand to test a candidate’s ability to solve problems.
A data scientist’s job role relates to that of a doctor. The more problems they solve and the more experience they have, the better they become at their job. This is one of the reasons why organizations value hands-on experience a lot more than just qualifications. However, it is still important to have the basic educational qualification.
A data scientist needs to know how to approach a problem productively. This implies identifying the salient features of a situation, finding out how to frame a question that will produce the desired answer, making a decision on what assumptions and approximations make sense, and coordinating with the right co-workers at the appropriate stages of the data science process. All this, along with knowledge of which data science technique or method needs to be applied to the problem at hand is a key skill for a successful data science career.
While programming in Python, querying in SQL, and visualizing data are the core technical skills a data scientist must-have, the need to have a strong business acumen cannot be overlooked. It is important to have industry-specific knowledge to gain an in-depth understanding of the business problem and design a solution for it. For instance, if you are working in the healthcare domain, how human testing of medicines is conducted, the permissions that are needed for testing, etc can be considered as industry-specific knowledge. If you are working in the finance domain, then basic business knowledge on rules like minimum age criteria for credit cards, loan quantum for a mortgage as defined by the regulatory authorities, compliance, and regulations, knowledge of accounting standards and risk management, etc are industry-specific knowledge. Industry-specific business knowledge can be easily picked up through business periodicals or books that report the latest trends and analysis.
And the Final One – Teaching Yourself
Mentors at Springboard believe that one of the most important skills for aspiring data scientists is learning to learn because data science tools and technologies are constantly evolving. One cannot actually invest time to master one particular technology or framework as they are evolving very fast. Rather, having the ability to quickly learn and acquiring the knowledge required for the task at hand is a must-have skill to propel through your data science career.
They say if you give a wise man 10 hours to cut a tree, he will spend 9 hours sharpening his saw. That is what an aspiring data scientist should do; build strong foundational skills to take the big leap.
Data scientists are expected to know a lot – math, statistics, machine learning, programming, data visualization, and communication. Within all these areas, there are tons of languages, tools, frameworks, and technologies one can learn. While you can gain a lot of theoretical knowledge the self-starter way, it would not be complete unless it is applied to practical real-world problems. A comprehensive project-based data science course could be the best bet for spending your learning budget to acquire all the required data science skills. Mentors can play an important role in this aspect. Mentors at Springboard help aspiring professionals understand the practical roadblocks and difficulties in applying their knowledge to real-world problems. Aspiring Data scientists can hone their skills through Springboard’s comprehensive data science program and eventually earn a certification credential to showcase their expertise as a Data Scientist.