Data Science: Non-Technical Skills for a Data Scientist

Data science is an interdisciplinary field that extracts knowledge and insights from structured and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, big data, and a business understanding.

I like to think that Data Science is about combining programming, statistics, machine learning, and AI, computer science, to find interesting insights from large data sets. Then, package it and present it nicely to various colleagues and management within the company, to move from insights to actions.

As data science is a broad and general term (with no clear definition), therefore, the skill description for what a data scientist needs is quite broad.

Before we look at some of the requirements and skills that are good to have, remember that a data scientist doesn’t have to be an expert in all these fields, but preferably have profound knowledge and experience in one or two of them, and some basic working knowledge in the others.

A data scientist work with several components (although may not be an expert in all fields) related to:

• Data Engineering
• Mathematics 
• Statistics
• Data visualisations 
• Software Development
• Machine Learning
• Business Understanding

The power of data science is to combine these different areas.

If you want to learn more about the technical skills a Data Scientist need, check out our post: Technical Skills to become a Data Scientist

 In general, a Data Scientists require: 

• Critical Thinking and Problem Solving
• Business Understanding
• Communication and Storytelling skills
• Curiosity

Let’s look at them a bit closer

Critical thinking and problem-solving skills are valuable skills relevant to any career. For data scientists, it’s even more important because, in addition to uncovering insights, you need to properly frame questions and understand how those results relate to the business and drive the next steps that convert into action.

Problem-solving is involved in nearly every aspect of a typical data science project from start to finish. Almost all data science projects can be viewed as one long problem-solving activity.

For a data scientist, problem-solving skills are valuable too:

• Analye questions, hypotheses, and outcomes objectively
• Determine which resources are required to solve a problem
• Analyse challenges from different angles and viewpoints

Three components of problem solving for a data scientist

1. Defining the right question: It might sound easy and trivial, but it can be the most challenging part to specify the key question or problem correctly to focus on the right things. For a data scientist, it’s quite rare that your colleagues and/or customers know exactly which situation they’re trying to solve, they might have a broad overview, but you often need to make it clear and define what you are going to try to uncover.
   
2. Formulating and evaluating hypotheses: Hypotheses is a statistical technique that helps data scientists  test the validity of their statements about the real-life events
   
3. Drawing conclusions: This involves careful and thorough analysis of the different possible courses of action and then coming up with the possible conclusions and recommendations

Data scientists should have a thorough understanding of the business to address present issues and assess how data may assist future development and success.

A data scientist should be able to comprehend the company and its particular needs, understand what organizational issues need to be solved and why, and ultimately, be able to transform data into outcomes that are useful to the organization.  

But how do you get that business understanding? And how do you know the insights gained from data are valuable to the business or not?

Well, there is no short answer to that and very individual how you learn, but what we can say is that we could describe it as there are three levels of business expertise

1. General Business Knowledge: The term “generic business knowledge” refers to information that is shared by all businesses, regardless of industry or firm. For example, general strategic management models such as the balanced scorecard, SWOT analysis, or perhaps, logistics flows, manufacturing, etc
2. Industry-Specific Knowledge: Industry expertise will vary depending on the industry in which the company operates. For example, suppose you work in finance, you must be concerned with laws and compliance, accounting standards, or if you work in the healthcare industry, how human medicine testing should be done, and the necessary approvals
3. Company-Specific Knowledge: Unique knowledge for the company you are working for. This could be, for example, what is the company’s competitive advantage, internal way-of-working and structure, the business model, revenue model, target market, business process, etc.

Another ability that is critical in data science is effective communication. Communicate information in a way that emphasizes the importance of the actions you recommend and defines data-driven insights in business-relevant terms. 

Data scientists in business must be skilled at data analysis and must effectively and fluently convey their results to both technical and non-technical people.

A good presenter will use storytelling techniques in the presentations. Data storytelling is the practice of building a narrative around data and its accompanying visualisations to help convey context and the meaning of data in a compelling fashion.

In other words, use the data to paint the picture and effectively use data to tell your story.

The three key components to data storytelling:

1. Data: The data serves as the foundation of the data story 
2. Narrative: ​​Also called a storyline, is utilised to communicate data insights, the context around them, and actions you advise and hope to inspire in your audience.
3. Visualisations. Visual representations of your data and narrative may help tell the message in a clear and impactful way. These visualisations might take the form of charts, graphs, diagrams, etc. 

Not only will curiosity keep you driven to continue your learning in the long run, but it will also help you know what questions to ask when you are diving into a new set of data. In most cases, data reveals various insights that might be interpreted differently.

A data scientist must be able to go under the surface of data to uncover and comprehend hidden insights and patterns. In other words, have that curious drive to dig deeper and find those hidden gems of insights