Data science is a lucrative field. And if you’re passionate about data, you must have asked yourself what skills do you need to be a data scientist?

Data scientists are some of the most technically experienced senior employees in the technology world. To be successful as a data scientist, you need a combination of both technical and soft skills so that you can be able to function both in leadership and non-leadership positions.

In this article, we are going to explore both the technical and soft skills that a data scientist needs to excel in the profession.

What Technical Skills Does a Data Scientist Need?

As we have already mentioned, to be an effective data scientist, you need a combination of both technical and soft skills. Technical skills would help you to deliver on the job, while soft skills would majorly benefit you in relating with your colleagues and in other leadership decision-making endeavors, etc.

To start with, below are some of the technical skills that data scientists use routinely. We have grouped these skills into three major categories.

Skills for collecting and storing data

For you to have data and act on it, it must come from somewhere. The same data must be consistent and organized in a manner that would reveal the hidden insights. To be effective as a data scientist, you must know how to collect data, manipulate it, and turn it into an effective database. Furthermore, you also need to know how the data will be used.

In technical terms, these steps are referred to as data extraction, transformation and loading. To go past these steps, you must have a good understanding of Excel and other querying languages like SQL.

Most of the times databases are meant to be nice and tidy. However, the collected data from the field may not necessarily be structured. Information such as audio and video, social media posts and customer feedback replies do not necessarily fit into tables. As this data is also not streamlined or numerical, it is the work of the data scientist to transform this data into a usable form that can also be understood by non-data scientists.

Skills for analyzing and modeling data

What skills do you need to be a data scientist? Data modeling and analysis skills.

Before data is analyzed, quantified and organized, it may not mean much or reveal the hidden insights that reside in it. A data scientist will need the skills in Python, Hadoop, R and Spark to help them analyze and quantify data into sets using statistical methods, run tests or create models that can be used across different applications.

The goal of analyzing and modeling data is so that you can generate models that can give us new insights from the same data and help us predict the unknowns.

The tasks that are involved in analyzing and modeling data are diverse and so are the skills needed by a data scientist to accomplish these tasks. At one point, they will need skills in data wrangling and exploration, and at another point, they will need to know how to analyze and model data. All these skills have a heavy foundation in programming and math.

Data scientists with extra skills in deep learning and machine learning are more favored for these tasks.

Skills for visualizing and presenting data

What skills do you need to be a data scientist? Skills for visualizing and presenting data.

As a data scientist, you must be able to transform data from the field into a format that is easily understood by non-data scientists and ensure they can derive useful information and insights from your presentation more intuitively. This can mean converting this data into charts, graphs, or dashboards.

To achieve this, data scientists use either single or a combination of tools such as Tableau, PowerBit, Matplotlib, Bokeh, and Plotly, among others. Although these data visualization tools have very powerful abilities, they are not able to tell you which type of visualization is most appropriate to highlight your findings.

Consequently, you need to know which type of visualization will best suit your data and allow others to derive sense and insights from it.

What Are the Most Important Soft Skills for Data Science?

Soft skills are important across all industries, whether you are in technical or non-technical roles. Unlike technical skills, soft skills are easily acquired even in other areas. This is particularly true especially if you’re making a lateral move into data science.

Teamwork

Teamwork makes the dream work. To be successful in any career, you must be good at working with others. This is equally applicable in data science.

A good head for business

Without a good head for business, data scientists may not be able to translate their technical skills into productive channels. As such, they are required to have a firm grasp of business principles as well as their company objectives.

Data scientists need to have the ability to spot business opportunities for potential growth or increased efficiency that can be explored through a data science approach.

Strong communication skills

Strong communication skills are not only necessary in your personal life, but also for the success of your career. You may be able to present the best charts and graphs, but if you can’t engage with your colleagues to discuss how your organization’s overall strategy can be influenced through data science., then this is a failure.

More often than not, these discussions will not necessarily be with your data science colleagues who easily grasp concepts in data science or its application. If you are dealing with the Board of Directors who have a pretty tenuous grasp of data science, you’ll have to be clear in your communication of the different techniques, objectives, and strategies to achieve the goals ahead of you.

Critical thinking and problem solving

Problem-solving and critical thinking skills must not be a surprise here. For you to solve problems using data, good judgment and objectivity are a must.

Good intuition for data and data architecture

Data science may teach you the “what” and “how” of problem-solving, but without data intuition, you will never know where to look in the data to be able to solve problems. There are no guaranteed roadmaps to problem-solving in data science.

Data scientists must have a high level of creativity and a sense of where to look in the data to reveal hidden patterns, insights, etc., and solve problems or give accurate business predictions. Insights and hidden patterns are always lurking and waiting to be uncovered in any sort of data.

The data scientist, therefore, must be able to know how to use data science to tease these insights and patterns out.  This requires two critical skills— a knowledge of how data is or isn’t structured, and how to manipulate different data structures from a traditional, initial vague idea into a model that is workable and can yield a final business decision.

Unfortunately, this skill isn’t taught in any class and can only be picked up through experience.

What skills do you need to be a data scientist? Well, there you have them. If you already have the soft skills from your previous jobs, then it is time to sit down and earn the technical skills.

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