The fact that big data science is one of the highest-paid professional areas to get into, means you need a long list of data scientist’s qualifications and skills.
It doesn’t matter where you live (in the USA, Canada, UK, Australia, India or somewhere else), the minimum set of required skills (such as technical knowledge, software, math, and statistics) is not enough to succeed and to get above the average median base salary – US$ 110,000 per year.
You need a broad range of behavioral characteristics, traits, qualifications, knowledge, certificates, and understandings to be a professional who is able to bring accurate data insights for decision-making in an organization.
On this page:
- The top 35 qualifications and skills you need to become a successful Data Scientist
- Infographic in PDF
When it comes to the fundamentals of data science, programming skills are in the top place.
In their work, data scientists use several programming languages to accomplish tasks such as extracting, analyzing, cleaning, and visualizing data.
Here is a list of the 8 most popular programming languages and skills:
R is a programming language – an excellent tool for unlocking the patterns in large datasets. It has a great range of high-quality and open-source packages that come with in-built statistical analysis methods and functions.
R allows you to manage matrix algebra and provides you with excellent data visualization capabilities.
Python is a general-purpose language that is broadly used by data scientists and therefore has great community support.
Its wide range of purpose-built modules makes Python a very popular and mainstream programming language.
Python is an easy-to-learn language. That’s why it is a perfect first language for the newbies in programming.
With the boom in technologies like machine learning and artificial intelligence, data scientists who have a solid knowledge of Python are in rising demand.
This extremely popular general-purpose language allows integrating data analysis methods directly into the codebase.
A large number of companies build their modern applications and system solutions upon a Java back-end.
Java is also a relatively simple and easy-to-learn programming language. Java is suitable for creating intensive machine learning algorithms and ETL production code.
Reliability, practicality, and compatibility are some of the key Java benefits.
More than 50% of the data scientist job listings on LinkedIn require expertise in SQL. It is because SQL is efficient at querying and manipulating relational databases.
It lies at the heart of storing and retrieving data in an organization. SQL deals greatly with large databases, providing a fast processing time.
SAS is also one of the most popular programming languages in the data science world. It provides a great range of statistical functions with a user-friendly GUI that helps you learn quickly.
SAS is an easy-to-learn language. It is preferred by beginners in the analytics area.
MATLAB is a numerical computing language that is fast and stable. It works with solid algorithms for complex math and has a place in a lot of applications.
MATLAB is known as a hard-core language among mathematicians, data scientists, and scientists who deal with sophisticated systems.
Scala is a Java-based programming language. It is becoming a preferred tool for those using machine learning at high-volume data sets and creating high-level algorithms.
The code written on Scala runs practically anywhere that Java runs. This makes Scala a very powerful general-purpose language.
Julia is a newcomer to watch when it comes to modern data scientist’s qualifications and skills. It is a high-level dynamic language that aims to meet the needs of high-performance numerical analysis.
Julia is an impressive language that gains great popularity amongst data scientists. It is adopted by some major companies including many operating in the finance industry.
An aspiring person needs to be familiar with at least 3 of the above data scientist skills. Most of the data professionals use Python, R, and SQL daily.
However, your preferred development style might be different. You might have experience with Java, Scala or Julia. Whatever it is, developing and mastering your programming skills is a must for becoming a high-paid professional.
Statistics and Mathematical Skills
Although today’s software can fulfill all the necessary statistical activities, you still need math and statistical understanding to choose which tests to perform and how to interpret the results.
Here are some basic data scientist’s qualifications and skills required in the field of statistics.
9. Discrete vs. Continuous Data
Both discrete and continuous variables are the two types of quantitative data also called numerical data. In practice, many data mining decisions depend on whether the basic data are discrete or continuous.
To know what is the difference, see our post discrete vs continuous data.
10. Binomial Distribution
The path of mastering statistics and data science starts with probability. The solutions to many data science problems are often probabilistic in nature.
The binomial concept has a core role when it comes to defining the probability of success or failure in an experiment or other data science events.
Our post (binomial distribution examples) contains a lot of basic information.
Non-linear and linear regression models are the oldest and widely used supervised machine learning algorithms for predictive analysis.
Linear regression modeling and formula have a range of applications in the business data science. For example, they are used to evaluate business trends and make forecasts and estimates.
They can also be used to analyze the result of price changes on consumer behavior.
12. Hypothesis Testing
Hypothesis testing is widely used to test the validity of a claim.
Data scientists use hypothesis testing to come up with conclusions about a population using sample data.
A hypothesis test estimates two mutually exclusive statements about a population. The goal is to find out which statement is best supported by the sample data.
Here is a great article “Data Science Simplified Part 3: Hypothesis Testing” by Towards Data Science, which can help you understand the concept in detail.
13. Bayesian Thinking & Modeling
Bayesian modeling is an extremely powerful suite of tools for modeling any random variable, such as business KPIs and demographic statistics.
The popular Bayes theorem (named after British mathematician Thomas Bayes) is a mathematical formula for defining conditional probability.
It revises existing predictions (probabilities of hypotheses) when given new or additional evidence.
For example, Bayes’ theorem is used in banking to rate the risk of lending money to new borrowers.
14. Machine Learning
Machine learning is a constantly growing area that is used for credit scoring, placing ads, stock trading, and many other purposes.
Machine learning is about developing, testing, and applying algorithms for predicting future outcomes.
For more information, see our post supervised vs unsupervised algorithms. Supervised and unsupervised learning represent the two key methods in which the machines can automatically learn and improve from the experience.
15. Markov Chains
Markov chains are simple methods to model random processes in a statistical way. They are a popular way of learning data science techniques and probabilistic modeling.
You can learn more on the topic in the Towards Data Science’s article “Introduction to Markov Chains”
Software and Analytical Tools
The data science is an enormous area, and the types of software tools the different companies are using can vary significantly.
Some data scientists provide data cleaning services. Some data scientists do specific researches.
Nevertheless, there are essential software tools you should be familiar with.
KNIME is a popular software company that offers an open-source analytics platform (KNIME Analytics Platform) for data reporting, data mining, and predictive analysis.
KNIME Analytics Platform helps data scientists all over the world discover the potential hidden in the data, gain fresh insights, or predict new features.
With more than 2000 modules, a great range of integrated tools, and a variety of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist.
Weka is a machine learning software provided by The University of Waikato.
Weka combines machine learning algorithms for data mining tasks. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
Weka is open source software issued under the GNU General Public License.
RapidMiner is a software platform that helps you build predictive models faster. The tool unites data prep, machine learning, and predictive model deployment.
The base of the platform (RapidMiner Studio) is free and open-source. There also are enterprise-level solutions. Their price depends on the number of logical processors, the amount of data used, and productivity features.
19. Apache Hadoop
Hadoop is a highly scalable platform. It can store and easily distribute very large data sets over hundreds of servers. Hadoop services have it all: data storage, data processing, governance, operations, and security.
20. Apache Spark
Apache Spark is a unified analytics engine for large-scale data processing, a cluster-computing framework for data analysis.
It is fast, flexible, and data scientist-friendly.
Apache Spark is a leading platform for large-scale SQL, batch, and streaming data processing, and machine learning.
21. Data Melt
Additionally, it provides advanced mathematical calculations, statistical analysis, and data mining capabilities.
22. Apache Storm
Apache Storm is a free and open-source platform for real-time analytics. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing.
Storm is simple. It can be used with any programming language. Also, you can gain a lot of fun when using it!
TensorFlow is an open-source machine learning framework for everyone – from students and researchers to data science professionals and innovators. It is a software library for high-performance numerical computation.
It allows you to access the big power of deep learning without even understanding the complicated principles behind it.
Data Visualization Skills
It is impossible to develop data scientist’s qualifications and skills without including data visualization. In the end, data scientists are those who help others make data-driven decisions.
Pictures can communicate much more effectively than words. So, it is a must for a data scientist to present data in a visually compelling way.
This means you not only have to use data visualization tools but also understand the principles of visualizing data effectively.
Tableau is one of the most popular data visualization and dashboarding tools out there. Tableau is a business intelligence software that helps people see and understand their data.
It allows you to connect and visualize your data in minutes, combine multiple views of data to get richer insight, and share dashboards on the web.
25. Google chart
Google chart is a powerful, simple-to-use, and free solution for visualization of big data. It is totally free and has great support from Google.
Google chart provides a wide variety of charts. From a simple scatter plot to hierarchical treemap and multi-dimensional interactive matrixes, you can find many professional and cool ways to show data.
JupyteR is an open-source project that allows you to analyze, visualize, and real-time collaborate on software development across a variety of programming languages.
JupyteR is a non-profit project born to support interactive data science and scientific computing.
Orange is a great easy-to-use teaching tool and you will love it. Orange allows you to perform a simple data analysis with clever data visualization.
You can seamlessly explore statistical distributions, box and whisker plot, decision trees, hierarchical clustering, heatmaps, linear projections and many more.
Guess what! Some of the best data scientists are not engineers, not statisticians, not programmers, and not even computer specialists. Many of them have a degree in other areas such as Psychology, Marketing, and Economic.
Although data science is a technical field and you need strong technical data scientist’s qualifications and skills, you also need non-technical abilities that can set you apart from regular data workers.
Here are the key non-technical skills:
29. Communication Skills
Working as a data scientist means working with other members of a team such as product managers, engineers, designers, stakeholders, etc.
Communication skills can help you build trust and understanding, which is incredibly important for those being stewards of the data.
Besides, you must be able to report your technical findings to non-technical colleagues such as those from the marketing department.
A data scientist needs to be very persuasive, especially when reporting findings.
All of the statistical and mathematical computations can be useless if the data scientist can’t communicate insights properly.
The range of effective communication skills involves active listening, storytelling, writing and presentation skills, body language, enthusiasm, and patience.
30. Data-driven Decision Making
There are so many questions and problems a data scientist needs to answer and decide.
For example, what qualitative data analysis methods to use and how to visualize data in the best way.
All of these questions relate to specific business situations and issues that need to be resolved.
Decision scientists have to make their insights useable for business.
Today, the whole management world talks about how to create a successful data-driven decision-making process in business to improve results.
31. Domain Knowledge And Business Acumen
Data scientist aims to make a positive difference in business.
However, the expertise in developing algorithms and building statistical models is not enough to achieve the goals.
You must know the business relevance of the algorithms and statistical models at hand.
Understanding the fundamentals of the industry and the objectives of your company allow you to achieve the expected results, making a difference in today’s competitive market.
Teamwork is critical to data scientists for bringing a project to fruition. Every data scientist should be a team player.
Each project requires people with different specialization (data engineers, project managers, data analysts, software developers, designers, etc.).
As a data scientist, you need to successfully collaborate with the team members to achieve the best use of data insights that you draw.
In today’s world of connected people, team working skills are essential for reaching success.
33. Intellectual Curiosity And Passion For Work
If you want to be a data scientist, you need to know why and how to eliminate the present mess in the big data – from both structured and unstructured data.
You need to come up with insights that make sense and help others to resolve situations.
It means you not only need knowledge, but also a passion for your work.
You need a passion for finding patterns, trends, and answers to business problems.
Sometimes, you might not even have a clear problem or situation to work on it, just some signals that there is something wrong.
This is where your intellectual curiosity should help you observe new or complicated areas to find what is going on.
34. Good Data Intuition
Data intuition is perhaps one of the most valuable non-technical data scientist skills.
It involves seeing patterns where none are observable on the appearance.
Data intuition can make you much more efficient in your career. However, this is a skill that comes with experience and years working in the world of data science.
You need to look at a vast number of data sets, play with them, catch what it is going on, and understand the kinds of problems you need to resolve.
Once you’ve exposed yourself to enough data work, you’ll develop your data intuition.
35. Project Management Skills
Most data analytics work is project-based and has to be handled effectively.
In each project, there is a high degree of uncertainty.
For example, the uncertainty might relate to what the purpose of the project is, whether enough data is available to resolve the problem, methods that need to be applied to achieve the purpose, etc.
This means it is hard to create the right plan at the beginning of the project.
That is why project management is a key skill in data analytics.
Unfortunately, project management skills are very underrated and most data scientists lack them.
Need more information regarding the critical skills needed to be a good data scientist?
Dive deeper with the following guide:
The guide will help you brush up on your skills and see how they stack up against the current best practices and trends in the field.
The above-listed data scientist’s skills are essential for your development as a professional.
The first two groups of skills (programming and statistical skills) are perhaps what most people first think about when they consider the data scientist role and position.
While those build the technical background of your knowledge, it is crucial to note that not-technical skills are also so important and required.
Every decade has its top job opportunities and hottest science fields.
Today, big data, artificial intelligence, and machine learning are key success factors that can define whether businesses are successful or not.
Hopefully, this round-up will help you create a clearer picture for you and help you understand the core skill set that employers require today.
It’s is your turn. What are the most vital skills according to you? Share your thoughts with us.
And best wishes on your own path to develop your data scientist’s qualifications and skills.