5 Data Science: Specific Skills vs. Generic Skills

• Specific skills or industry-specific skills
• Generic skills or non-technical soft skills

Generic Skills

When a person tries to persuade his friends, superiors, or customers, he displays his personality traits, personal beliefs, and interpersonal skills. A Data Science course gives students the technical know-how and hard skills they need to succeed in their careers, such as coding. However, if he lacks strong soft skills, such as effective people management and negotiating, he can find it very challenging to explain his technical concepts to his superiors. His inability to persuade and negotiate will make him poor at leading project teams. Because the project participants lacked the soft skills to oversee their execution, many excellent, theoretically sound initiatives were put on hold. Soft skills are used often at work by a strong data scientist, who also has higher career mobility.

Some of these skills are mentioned below:

Effective Communication: A Data Scientist analyses complex structured and unstructured data to derive meaningful insights. He has to code machine learning algorithms, build prediction models and perform different types of analysis. It becomes imperative to communicate the results thereof effectively to senior management. Often several good analytical ideas have been rejected by senior management because the data scientist lacked the communication skills to present it effectively. The communication skills that a data scientist must append to his repertoire are presentation skills, persuasion skills, and negotiation skills.

In a professional setting, a data scientist must lead a group of data analysts and interns who are less experienced in putting his ideas into practise. The team leader cannot express his ideas to his subordinates properly if he lacks strong communication skills. Finally, the project will provide unfavourable results since the subordinates will use a flawed analytical strategy.

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The most vital generic skill is effective communication.

Critical Thinking: A critical thought process involves a logical sequencing of steps in problem-solving. A critical thinker is an individual who is able to discern the most logical and effective way to solve a problem. This critical thinking must be habituated in all data scientists. It is mandatory for success on the job. It is very necessary to identify the root cause of any problem and devise the required countermeasures by understanding its correlation with the business outcomes.

Problem-Solving Skills: For data scientists to tackle challenges in the present, they must be creative. The first stage in problem-solving is to define the issue and pinpoint its underlying cause. However, because every organisation functions in a particular environment, the answer must be special. To advance in his profession, a data scientist should develop unorthodox problem-solving techniques.

Acquiring Business and Marketing Acumen: A data scientist must possess sound technical knowledge of his field. Besides this, he must develop an understanding of the industry that he is employed in. He has to understand the problems that his business has to solve and find new ways to leverage data available to solve those problems. He must have a clear understanding of how the business operates. This will enable him to suggest appropriate data analytics solutions. It is important for him to understand how the solutions that he provides as a result of his analysis and algorithms impact the topline and bottom-line of the business.

A data scientist must conscientiously develop knowledge of the industry and business in general by reading reports, journals, and undertaking online courses.

Develop a Sense of Curiosity: A data scientist, as the title indicates, is essentially a scientist who studies the patterns and secrets of data. Like a scientist, a data scientist should always be curious. A data scientist's job is to disprove received wisdom and unearth hidden truths from the data gathered. Curious thinking brings fresh, previously unseen points of view to the fore and aids in the creation of effective company plans for enhancing market performance.

Specific Skills

Specific skills necessarily imply the technical skills which a data scientist must possess in order to make a successful career in the field. Although the generic skills are important for the candidate, without the specific skills, they are of no use. Technical knowledge is the backbone of your career, so without technical knowledge, there is no question of getting a job, let alone the chance to apply those generic skills. Some of the key specific skills required for a Data Science career are listed below.

Data Visualization - Critical thinking and effective technical communication are the major components of this competence. A data scientist works with several data analysts who examine both organised and unstructured data. The ability to explain technical analyses to non-technical personnel, such as sales and marketing professionals and even HR, is a must for a data scientist. As a result, you must produce an effective and straightforward graphical representation of the analysed data. Consequently, a data scientist must be skilled in the usage of programmes for data visualisation, such as Tableau.

Data Preparation - There is a large amount of data that can be processed, but not all of it is beneficial and required. Before moving further with the data processing, it is crucial to correctly separate the data and analyse it in accordance with its kind and significance. The tiniest inaccuracy in this stage can completely wreck the data processing process and lead to problems in the final result because this procedure is so precise. The outcomes could occasionally be deceptive.

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Mathematical and Statistical Application - Apart from program coding, the application of mathematical and statistical analytic techniques is important for identifying the underlying algorithms and logical patterns in a problem. There are various consumer patterns hidden in the data stacks which can only be identified using mathematical reasoning. This helps a data scientist to take a more critical view of the problem and identify all the underlying aspects of the problem.

Professional Coding Techniques - The fundamental competency of data science is this one. The industry uses a wide variety of coding languages on a daily basis. In order to assure the development of a more specialised skill set and to improve professional coding competence, it is crucial for the data scientist to choose a suitable coding language and to specialise in that particular language.

Efficient Application of Machine Learning and AI - The current trend is Industry 4.0 where robots and robotic process automation is replacing human jobs. Artificial Intelligence solutions are being adopted in every sector to reduce dependence on human intervention. A data scientist must possess Artificial Intelligence and machine learning. A data scientist should be capable of incorporating AI and machine learning into the work process to reduce human workload and gain an advantage by producing more accurate and precise results using available technology.

Therefore, remember to include both your general and particular talents while writing your CV. Generic skills will define the general quality of your performance in your field, whereas particular skills will indicate your knowledge and ability to handle the work.