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10 Must-Have Skills for a Data Scientist

The first time I heard the term “data science” was after I graduated from the university, about four years ago. And at that time, I wasn’t so interested in what it was about. However, when I got a job in a data science-affiliated company, my interest was piqued and I wanted to know what the buzz was about.

Today, Data science is one of the buzzwords in the tech world. And it is a core aspect of technology, which is valuable in today’s world.

Now, there are important skills to learn as a data scientist, which help you carry out tasks in this field. But before that, let’s start from the beginning…

What is Data?

Data is a collection of facts, statistics, etc. which are used for reference. It refers to a raw format of knowledge, from which meaningful information can be gotten, after analysis.  Data is usually written in form of the letters of the alphabet, numbers, symbols, etc.

What is Data Science?

In simple terms, data science has to do with the analysis, extraction, visualization and storing of data, to find out meaningful information, which can be used to make informed decisions in business, finance, customer service relations, etc. Data science involves finding patterns in structured or unstructured data, to find areas where meaningful information can be extracted to make good decisions.

Data science can be applied in every sector, to help business owners make good decisions, based on facts and statistics. It takes away the use of assumptions when taking actions in business, and ensures accuracy in decision making.

“The temptation to form premature theories upon insufficient data is the bane of our profession: – Arthur Conan Doyle.

This statement suggests that data science is core to making great decisions in every profession.

More: How To Be A Better Data Scientist – Tips For Newbies

10 Must-have Skills for a Data Scientist

In every profession, skills are needed to carry out the various tasks in them. For example, in the education field, skills such as communication, class control, etc. are used in the teaching-learning process.

Thus, in data science, there are important skills that data scientists employ in carrying out the various activities in data science. These skills are used for the extraction, cleansing, visualization, and storing of data. They include:

Probability and Statistics: 

One of the core skills in Data Science is Probability and Statistics. They form the basis of the field. Probability has to do with the extent to which something is likely to happen, while statistics have to do with the collection, description, analysis and interpretation of data to solve problems.

Now, data science involves the use of systems to retrieve insight from data and make viable decisions. Hence, inferences and predictions need to be made in the process of extracting this knowledge, and this is where probability and statistics come in. They help in making predictions and estimations in data science.

Roles of Probability and Statistics in Data Science:

  1. Help to explore data better
  2. Discover the inter-relatedness between variables
  3. Make predictions based on past trends
  4. Check for irregularities in the data


Programming is a crucial skill to acquire, as a data scientist. It is an important skill used in transforming raw data into insights to use in making decisions. Programming is extremely important, as it plays several roles in data science, depending on the nature of the problem being tackled.

Python is a highly favoured programming language in data science. This is because it can be used to obtain, clean, analyze and visualize data. Other programming languages that can be used in data science, include R, Scala, MATLAB, Java, SQL, etc.

Roles of Programming in Data Science:

  1. Using querying programming languages, such as SQL to obtain data
  2. Cleaning data
  3. Analyzing data

Machine learning:

Another important skill in Data Science is Machine Learning. This involves automating the process through which data is analyzed, without the help of a human. It is a fast way to analyze a larger set of data and make predictions without human efforts.

Now, in using machine learning in data science, there are various steps to get through. These steps include:

Gathering data: This involves the collection of the data to be used in solving the problem at hand. The data must be reliable and relevant, as its quality would impact the end result.

Preparing data: After data has been collected, it should be prepared. And this involves ridding the data of errors and corrupt data points. In preparing the data, the dataset is to be split into two. While one set is used to train the model, the other set would be used to test and evaluate the model’s performance.

Training the model: Here is where the learning starts from. The data is inputted into the machine, to make it predict an output, and then the predictions are made without human efforts. Now, in some situations, the prediction may not match the desired results. However, with practice, the machine would give better results.

Testing data: Here you are to evaluate or test the performance of the machine, using the second dataset that you set aside while repairing the data. It is important to use a dataset that has not been used before, in testing the model, so that you know how the model would work in real life.

Making predictions: This is the final stage of using machine learning in data science.

Fields where Machine Learning can be applied, in Data Science

  1. Healthcare
  2. Facial recognition system
  3. Voice recognition system
  4. Airline route planning
  5. Improved Interactive Voice Response (IVR)

More:  Top 5 Data Science Books You Should Read in 2022

Data wrangling:

Data wrangling refers to the process through which you prepare data for analysis. Most times, data received is not usually ready for modelling, thus, it needs to be wrangled.

Thus, the process of data wrangling involves the acquisition and cleansing of data. It also involves transforming data from its raw state into a refined state, to make it usable and more organized.

Examples of some data wrangling tools are excel spreadsheets, CSVKit, Tabula, etc.

Roles of Data Wrangling in Data Science:

  1. Provide organized and actionable data to be used by data scientist
  2. Prevents time wastage in the process of gathering and organizing data for use
  3. Allows data scientists to focus attention on data analysis, rather than data cleansing

Database management:

Data Management refers to the process through which data is gathered, stored, processed and used, in order to get actionable insight to make decisions. Data management is a core skill in Data Science, which ensures that up-to-date and relevant data is stored and available for use. It also allows for ease of locating data when they are needed for analysis.

Roles of Data Management in Data Science:

  1. Helps data scientists to find needed data easily.
  2. Ensures the reliability of data, by minimizing errors.
  3. Protects data from theft, breaches, etc. through the use of encryption tools, etc.

More: Data Science For Beginners

Data Visualization:

This is one of the very interesting skills to learn in Data Science. It involves a graphical representation and display of the findings of the data being worked on. Beyond the scientific part of data science, there is a need to communicate the results of the data in clear terms to a layman. And this is where data visualization comes into play.

Data visualization helps one to understand data and how it functions, as well as to present the final results, which can be used in making decisions.

Some examples of data visualization tools include bar charts, pie charts, histograms, line plots, heat maps, etc.

Roles of Data Visualization in Data Science:

  1. Visualizes data to get actionable insights
  2. Shows the relationship between variables
  3. Gives a visual representation of data
  4. Determine where to place each product
  5. Understand customer behaviours and factors that influence it
  6. Shows areas where improvement is needed

Cloud computing:

Cloud Computing is another important skill for every data scientist. It involves the use of cloud computing products to help data scientists gain access to the resources needed for the management and processing of data. Cloud computing is crucial in Data Science, as it gives professionals, access to platforms such as Google Cloud, Azure, etc.

Roles of Cloud Computing in Data Science:

  1. To acquire data
  2. To wrangle, transform, parse, and mung data
  3. For data mining

More: Why Cloud Computing is Important in Data Science

Now, asides from the technical skills needed by data scientists to carry out their work, they also need other creative and soft skills. These soft skills are important to extract actionable insights from data. They include:


One of the core soft skills that data scientists must possess is communication. Data scientists must know how to interpret and communicate the results of their work to a layman’s audience. This is important so that the actionable insights gotten from their work can be applied in making decisions in the business.

Roles of Communication in Data Science:

  1. Promotes data literacy within the organization
  2. Shines a spotlight on the work of data scientists in the company
  3. Makes the contribution of data scientists valuable and relevant in the process of making business decisions.


To be a successful data scientist, you need to be immensely curious about things. Data scientists don’t just see a problem and gloss over it. They are curious to go in-depth to find answers to pressing business problems and to arrive at actionable solutions.

Roles of Curiosity in Data Science:

  1. Brings the urge to search for new information and solutions
  2. Enhances critical thinking
  3. Gives a new perspective to a problem at hand


Progress is barely made in isolation. Thus, data scientists need to be team players, who can collaborate with other key stakeholders to bring success to a business.

Roles of teamwork in Data Science:

  1. Enhances collaboration
  2. Increases progress

So, which of these do you currently possess? And which would you like to improve on?

Remember, no skill is better than the other. All skills work together to achieve the end goal of data science.

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