Data science is a hot field of the 21st century, with tremendous opportunities and high demand. As per the job site Glassdoor, the average salary of a data scientist in the US is $122,321.

Data scientists, like any other team, need to communicate effectively and collaborate. There are a lot of skills you can learn which will make your life easier and increase your productivity, but here we have listed a few essential skills sets for data scientists.

What is data science?

Data science is a process that uses mathematics and computer science to analyze large amounts of data to answer questions that were previously difficult or impossible to answer. A successful data scientist is someone who uses their skills in math and programming to find patterns in data, determine whether those patterns are statistically significant, and extract meaningful information from them.

What does a data scientist do?

The most common use for data scientists is in research and development. They can be used by companies or organizations who want to gather information about their customers, research new products and services, or test the effectiveness of existing ones. Data scientists can also help with training programs for employees by helping them learn how to make decisions based on data analysis rather than intuition alone.

A data scientist does three main things:

  • Collects and organizes data.
  • Analyzes the data with mathematical modeling techniques.
  • Distributes the results so that people can understand them.

List of skill sets for data scientists

Data science is a growing field that has the potential to change the world. But it’s also a lot of work: Data scientists are expected to do more than just crunch numbers. In this article, I’ll be listing out 10 essential skill sets that every Data Scientist should have:

Data Visualization

Data visualization is one of the most important skills for data scientists. Skilled data scientists are the ones who are able to visualize data in an effective way and make it easy for people to understand. A good visual can help you share your findings with stakeholders, and even educate non-technical people about how your data analysis was done, and the story the data tells.

A good data visualization tool like Tableau, Matplotlib, ggplot etc can also help you understand the insights that your models have uncovered, which is crucial for making sense of the big picture.

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Programming Skills

Being able to code is essential for many people who work with data because it allows them to create new algorithms and run simulations on large datasets (the kind that is often found in advanced analytics). Data scientists need proficiency in at least one computer language: R or Python are popular choices, but other options include Scala, Java, C++, and others. Having a strong foundation in programming languages is a must. Is it important to learn programming languages before jumping into machine learning projects.

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Machine Learning

Programming is required for nearly every aspect of the field. You need to be able to program in R or Python if you want to work with large-scale machine learning models. You also need to know SQL if you want to build databases or manage your analytics infrastructure.

Once the required data has been cleansed and prepared for analysis, Data Scientists build and test predictive models. Machine learning algorithms vary greatly in complexity, so you need to be comfortable with a variety of machine learning algorithms if you want to be a data scientist. This isn’t something that can be learned overnight, but it’s an important skill to have.

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When it comes to algorithms, a data scientist should be comfortable with a variety of them because the complexity of the models they build can vary greatly. Some common algorithm types that a data scientist might need to know are linear regression, logistic regression, neural networks, clustering, decision trees, random forests, Naïve Bayes classifiers, k-nearest neighbor, k-means clustering, and graph analysis.


Statistics is the study of descriptive data and predicting outcomes from it. It helps us understand the properties of our observations as well as predict events based on those observations. Statistics is used in many fields like biology, medicine, economics, and so on.

A person knowing statistics can use this knowledge to solve real-world problems but also complex ones. The data scientist aims to create solutions for business problems, but they need to be able to get the right information and make the right decisions.

Artificial intelligence

AI refers to the ability of machines to perform tasks that require human intelligence or creativity. For example, an AI system can be used to analyze stock market information to predict future trends or price changes. Today, AI is used to create customer service experiences, analyze large amounts of data, and recommend products.

AI is a rapidly-growing field, and data scientists who are skilled in this area can find many opportunities in the future. There’s no doubt that AI will continue to play an important role in our lives and businesses, so it’s important for data scientists to be familiar with the basics of this technology.

Deep learning

Deep learning refers to algorithms that take advantage of deep neural networks that have very high levels of complexity at their core. These networks can identify patterns in large amounts of data quickly and efficiently without having any prior knowledge about what those patterns might be.

Deep learning is still in its early stages, so there are a lot of opportunities for data scientists who are skilled in this area. As deep learning algorithms become more sophisticated, it’s likely that more jobs will be created that use this technology.


The most important thing to remember about data science is that it’s not just a collection of skills — it’s a mindset. You’ll need to develop your intuition and learn how to think like an engineer, but you’ll also need to understand how scientists think, how they ask questions, and how they arrive at conclusions based on evidence. As a data scientists one must be comfortable with rigorous methods such as experimentation, hypothesis testing, and other common tools of scientific inquiry. They need to be able to communicate their findings, and they need to understand how those findings can be applied in real-world scenarios.

There are a lot of resources available to help data scientists develop their intuition, including books, online courses, and discussion forums. It’s important to find the resources that work best for you and to continue learning so that you can stay ahead of the curve in this rapidly changing field.

Data Analytics Experience

Data analytics experience is essential for a data scientist to be able to analyze and interpret data — whether that be through visualizations or more complex analytical methods like machine learning. They need to understand how data is collected and analyzed, what the purpose of each piece of information is, and how those pieces fit together into an overall picture.

The ability to think critically

Data scientists should be able to identify patterns and trends in large amounts of data while also understanding when those patterns and trends might not apply across all contexts or datasets (which means they have an analytical mindset). They need to be able to think creatively and come up with solutions that address unique areas of problems that require adaptation.

Soft Skills Matter

Besides the extensive knowledge in Python, R, Machine Learning, data visualization, it is important for a data scientist to have good communication skills and be a great team player. The soft skills like problem-solving, time management, decision-making, stress management, are all essential traits to be a good data scientist.

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The data science field is constantly advancing and evolving, with new methods and tools being developed all the time. Big tech companies collect far more data than they can effectively and efficiently analyze. There is a huge demand for individuals who can help interpret the data to create a narrative in order to drive business decisions. This means that there are many opportunities for career growth in this field, no matter what your experience or qualifications may be.

Data science is a hot job market, and companies are willing to pay top dollars for it. That’s why you should learn these essential data science skills as mentioned in this guide.

Further Reading:

How to start your career in Data Science and Machine Learning

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