7 Essential Data Science Skills for Every Employee

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Click to learn more about author Jessica Chapman.

First and foremost, what, exactly, is Data Science? Data Science is a multidisciplinary field that uses processes, algorithms, and systems to obtain various insights coming from both structured and unstructured data. It is related to data mining, machine learning, and big data. 

data scientist – the person in charge of gathering and using all of this data – must be experienced in numerous disciplines and fields, such as machine learning, data engineering, math, statistics, visualization, and scientific methods.  

Moreover, they need to have advanced computing skills, especially in programming, and there’s a big advantage in having a smart, hacker-like, and efficient mindset.  

Let’s go through the top seven essential and most-looked-for skills that every data scientist should have.

1. Fundamentals and Basics of Data Science 

Naturally, as a data scientist, you cannot go without having a basic understanding of Data Science. Of course, no one has access to this type of information in their day-to-day life, so you’ll have to do some research by yourself.  

Nowadays, data is present almost everywhere you go. For example, even as a writer at a professional essay writing service, you will have to keep track of a multitude of data and resources and also be able to interpret them.  

However, things are a bit different when it comes to Data Science. You will need to have a basic understanding of some specific concepts.

You can start by teaching yourself all about machine learning and trying to understand and apply techniques such as linear regression or finite volume method. You can also look up what deep learning is, all the differences between data science and data engineering, business analysis, and so on. 

Other topics that should be covered are supervised/unsupervised learning, regression and classification problems, as well as what tools and terminologies are most frequently used in this field.  

2. Statistics and Probability

As a data scientist, your work will revolve around statistics and probabilities. When you start understanding machine learning, you will notice that the basic concept of it spins around statistics and probabilities, and then it evolves on larger scales.  

You should make sure you master these two concepts, as well as understand matters such as probability distribution, sample and population, hypothesis testing, and so on.

Knowing how to calculate probabilities is also a must in this domain, as they will be used while running experiments using the data that you collect. Bayesian statistics are often used in Data Science, so understanding this concept will also be very useful.

3. Machine Learning and Its Limitations

We’ve established that a key role of a data scientist is to understand and know how to use the concept of machine learning. Part of this process is to also understand that it has its limitations. Machine learning basically represents a bundle of different algorithms and methods.  

It is an iterative process, meaning that it works as a series of steps, which will improve with each cycle. However, it is important to remember that it will not provide answers that you can’t already find in the databases.

4. Programming Skills and Knowledge

Understanding the fundamentals of machine learning requires at least a basic set of programming skills. This is the only way in which you will be able to communicate with machines and computers. There are plenty of programming languages used in Data Science, specifically, such as Python or Julia. You can do online research or start taking courses on these, to understand and gather as much information as you can.  

5. Structured Query Language

Structured Query Language (SQL) is the language that you will find in almost all databases. Having a good understanding of SQL is a huge advantage, as it means that you understand exactly what the data means. Queries retrieve the data based on specific criteria. If you understand this concept and know how to run queries, you will, for sure, be able to have all of the data handling at your fingertips.

6. Data Visualization

We keep talking about how important knowing how to extract and read data is, but we shouldn’t leave out the fact that an important role of a data scientist is to know how to also visualize all the data. This means that you should know how to build a story out of visualizations. To be more specific, you should be familiar with charts, histograms, and even the more advanced ones, like waterfall/thermometer charts, etc. There are, of course, numerous tools that can be used during this process.  

7. Data Cleaning

Last but not least, it is important to also master the process of data cleaning. A data scientist not only has to be able to understand the databases for himself, but also to be able to help others understand them. This being said, you should know how to keep the databases clean and organized, so that your work will always be efficient.  

All in all, you should remember that, as a data scientist, it is not enough to be skillful in one domain only. You should always be open to learning new processes and techniques, as this domain is evolving day by day.

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