10 skill sets every data scientist should have

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The demand for talent in data science is growing and with it comes the need for more data scientists to join organizations. While the application of data science is its own field, it is not relegated to an industry or line of business. Data scientists can have an impact anywhere in any organization.

Non-technical skills
These skills won’t require as much technical training or formal certification, but they are critical to the rigorous application of data science to business problems. Today, even the most technically skilled data scientist needs to have the following soft skills to get ahead.

Critical thinking

With this skill, you will:

data scientist

Objectively analyze questions, hypotheses, and results.
Understand which resources are crucial to solving a problem
Look at problems from different points of view and perspectives
Critical thinking is a valuable skill that easily adapts to any profession. For data scientists, it’s even more important because in addition to getting information, you need to be able to ask questions appropriately and understand how those results relate to the business or direct the next steps to translate them into action.

Effective communication

With this skill, you will:

Explain what data-driven insights mean in business-relevant terms
Communicate information in a way that highlights the value of the action
Convey the investigation process and the assumptions that led to a conclusion
Effective communication is another skill that is sought after everywhere. Whether you’re in an entry-level position or a CEO, relating to other people is a useful trait that helps you get things done quickly and easily.

Proactive problem solving

With this skill, you will:

Identify opportunities and explain problems and solutions
Know how to address problems by identifying existing assumptions and resources
Put on your detective hat and identify the most effective methods to get the correct answers
You cannot be a data scientist without the ability or desire to solve problems. That’s what data science is all about. However, being an effective problem solver is as much a desire to dig to the root of a problem as it is knowing how to tackle a problem to solve it. Troubleshooters easily identify tricky problems that are sometimes hidden, and then they can quickly move on to thinking about how they will tackle it and which methods will provide the best answers.

Intellectual curiosity

With this skill, you will:

Conduct the search for answers
Delve into superficial results and initial assumptions
Think creatively with an urge to know more
Constantly asking yourself “why” (because one answer is usually not enough)
A data scientist must have intellectual curiosity and a passion to find and answer the questions presented by the data, but also to answer questions that were never asked. Data science is about discovering underlying truths, and successful scientists will never settle for “enough” but will keep searching for more answers.

Business sense

With this skill, you will:

Understand the business and it’s special needs
Know what organizational problems need to be solved and why
Translate data into results that work for the organization
Data scientists do double duty: They must not only know their own field and how to navigate the data, but they must also know the business and the field in which they work. Knowing how to handle data is one thing, but data scientists must also have a deep understanding of the business, enough to solve current problems and consider how data can support future growth and success.

Ability to prepare data for effective analysis

With this skill, you will:

Provision, collect, organize, process, and model data
Analyze large volumes of structured or unstructured data
Prepare and present data in the best possible ways for decision-making and problem solving
Data preparation is the process of preparing data for analysis, including data discovery, transformation, and cleaning tasks, and is a crucial part of the analysis workflow for analysts and data scientists alike. Data preparation tools like Tableau Prep Builder are easy to use for all skill levels.

Ability to leverage self-service analytics platforms

With this skill, you will:

Understand the benefits and challenges of using data visualization
Have a basic understanding of market solutions
Know and apply best practices and techniques when creating analyzes
Have the ability to share results through dashboards or self-service applications
This skill is in line with non-technical skills because it relates to critical thinking and communication. Self-service analytics platforms help you show the results of your data science processes and explore the data, but they also help you share these results with less technical people. When you create a dashboard on a self-service platform, end users can adjust the parameters to ask their own questions and assess their impact on the analysis in real-time as the dashboards are updated.

Ability to write efficient and maintainable code

With this skill, you will:

Deal directly with programs that analyze, process, and visualize data
Create programs or algorithms to analyze data
Collect and prepare data through API
This ability is almost a must. Since data scientists live immersed in systems designed to analyze and process data, they must also understand the inner workings of those systems. There are many different languages used in data science.

Ability to apply mathematics and statistics appropriately

With this skill, you will:

Understand the strengths and limitations of different test models and why they fit a given problem

Ability to leverage machine learning and artificial intelligence (AI)

With this skill, you will:

Understand how and when machine learning and artificial intelligence are right for business
Train and implement models to implement productive artificial intelligence solutions
Explain models and predictions in useful business terms

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