10 Essential Skills for a Data Scientist

Data science is becoming more and more important, and the demand for data scientists is exploding. Although its scope remains limited, this discipline is not confined to a single industry, and a data scientist can have an impact at all levels of the company.

If you are planning to embark on a career as a data scientist, the first step is to get the right training. However, beyond the technique, a data scientist must also acquire skills that transcend disciplines. It is by acquiring and developing these skills that you will succeed in setting yourself apart from other candidates and other data scientists in a market that is constantly growing.

Personal skills

These skills do not require technical training or recognized certification but are nevertheless essential to solving business problems using data science. To be successful, even the most technically competent data scientist needs the following personal skills.

1. Critical thinking

Characteristics of this skill:

  • Analyze questions, assumptions and results objectively
  • Understand what are the essential resources to solve a problem
  • Look at issues from a different perspective

Critical thinking is an essential skill for all professions. For a data scientist, it is all the more important as it allows to correctly identify the questions to make discoveries, to better understand how these results are linked to the professions, or to put in place the steps to follow to transform these results into actions.

It is also important to analyze the issues objectively by interpreting the data before forming an opinion. Critical thinking in the field of data science consists of approaching a problem from all angles, taking the data source into consideration, and showing constant curiosity.

2. Effective communication

Characteristics of this skill:

  • Explain the meaning of actionable information in terms relevant to the business lines
  • Communicate information by emphasizing the value of actions
  • Convey the research process and the assumptions that led to the conclusion

Another skill in high demand in all industries is effective communication. Whether you are at the bottom of the hierarchy or a CEO, your ability to interact with others will allow you to achieve results more easily and quickly.

In a company, a data scientist must be efficient at analyzing data, and they must be able to explain their discoveries in a clear and fluid manner to both an informed and lay audience. This essential aspect helps to encourage data literacy at the enterprise level and reinforces the capacity of a data scientist to make an impression. When data provide solutions to business problems or answers to business questions, organizations rely on the creative mind and communicative effectiveness of data scientists to help others understand what action to take.

3. Proactive approach to problem solving

Characteristics of this skill:

  • Identify opportunities and explain issues and solutions
  • Understand how to consider a problem by identifying the assumptions and existing resources
  • Play the detectives and identify the most effective methods to get the right answers

A data scientist must have the desire and the ability to solve problems. This is also the whole point of data science. However, to effectively solve a problem, it is as much about being willing to find the cause as knowing which approach to take. Creative minds can easily identify problems that are not visible, then quickly determine how to approach them and what methods to use for the best results.

4. Intellectual curiosity

Characteristics of this skill:

  • Guide research to find answers
  • Go beyond superficial results and initial assumptions
  • Be creative in seeking to know more
  • Never be satisfied with just one answer

A data scientist must demonstrate intellectual curiosity and the will to find an answer to the problems posed by data, but also to questions that have never been asked. Data science is about discovering deep truths, and an effective data scientist must constantly seek answers.

5. Business sensitivity

Characteristics of this skill:

  • Understanding the professions and their specific expectations
  • Know which business issues to solve and why to solve them
  • Translate data into business-relevant results

Data scientists have a double hat: they must know their own field and know-how to explore the data, but they must also know the professions and their field of activity. In addition to understanding the data, data scientists must have a very good knowledge of the business to solve existing problems and determine how data can promote growth and contribute to the success of the company.

“Data science is not just about breaking down numbers: it is the application of many skills to solve a particular problem in a specific sector,” explains NR Srinivasa Raghavan, Chief Data Scientist World at Infosys.

Technical skills

Data Scientist Role and Responsibilities

These skills are more in demand, and you can usually see them in job descriptions for data scientists. Many are covered in educational courses or vocational training. More and more companies are emphasizing these skills to develop and evolve their analytical teams.

6. Ability to prepare data for effective analyzes

Characteristics of this skill:

  • Find, collect, organize, process, and model data
  • Analyze large volumes of structured or unstructured data
  • Prepare and present data in a way that facilitates decision making and problem solving

Preparing data is about making it ready for analysis. It includes discovery, transformation, and cleansing steps, and it’s a crucial part of the analytical workflow for analysts and data scientists alike. Regardless of the tool used, data scientists must understand preparation tasks and their relationship to data science workflows. A data preparation tool like Tableau Prep Builder is extremely user-friendly and caters to all skill levels.

Also Read : What Is the Scope of Artificial Intelligence in Future

7. Ability to leverage a self-service analytics platform

Characteristics of this skill:

  • Understand the benefits and challenges of data visualization
  • Basic knowledge of the different market solutions
  • Understand and use best practices and techniques to perform analyzes
  • Ability to share analysis results through self-service apps or dashboards

This competence is in the direction of personal aptitudes because it calls for both critical thinking and effective communication. Self-service analytics platforms allow you to show the results of your data science processes and explore data but also share your findings with more lay users. When you create a dashboard on such a platform, users can adjust the parameters to ask their own questions and immediately observe the result of their analysis in real-time.

8. Ability to write efficient and sustainable code

Characteristics of this skill:

  • Use programs to analyze, process, and visualize data
  • Create programs or algorithms for parsing data
  • Collect and prepare data via APIs

This skill makes a lot of sense. Data scientists need to understand the inner workings of systems designed to analyze and process data since they are embedded in it. Data science is based on many different languages, and you must master and apply those that are most relevant to your role, your industry, or your business issues.

9. Ability to apply mathematics and statistics effectively

Characteristics of this skill:

  • Perform exploratory analyzes and identify important trends and relationships
  • Apply a rigorous statistical approach to extract factual data
  • Understand the advantages and limitations of different test models and how they are suited to a given problem

Like code, mathematics and statistics play an essential role in data science. Data scientists use mathematical and statistical models and must be able to apply and develop them correctly. Good knowledge of statistics allows data scientists to take a critical look at the value of different data and the types of questions they can answer. Certain issues sometimes require the design of innovative solutions, which may involve combining or modifying standard analytical techniques and tools. Understanding the underlying assumptions and algorithms is essential to properly using these applications.

10. Ability to leverage machine learning and artificial intelligence

Characteristics of this skill:

  • Understand when and how machine learning and AI are viable business solutions
  • Train and deploy models to implement productive AI solutions
  • Explain models and predictions in a way that is relevant to the business

In most companies, machine learning and AI can never replace a data scientist. Nonetheless, these tools help reinforce the value you create and help you work faster and more efficiently. As Michael Conlin, Chief Data Officer, recently noted, “To harness the full potential of AI and machine learning, you need to have a number of essential human skills. Indeed, the main difficulty in AI is knowing if you have the right data, determining if that data is not showing what it should, and finding data that is satisfactory for the AI ​​before opting for the model. Trained AI that will be most useful.

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