Artificial Intelligence, Machine Learning, Data Analytics, Data Mining, can you recognize all these technical terms? Well, let me tell you that these are the Data Scientist Skills and when combined it is known as “Data Science”. As the name suggests, data science is the study of data to extract meaningful perceptions for the industry. Data scientist skills are of 2 types- technical skills required for data scientists and non-technical skills required for data scientists. In this article, I will explain the Data Scientist Skills needed in 2023, including technical skills required for Data Scientists and non-technical Skills required for Data Scientists.

Before learning about data scientists skills, let us understand a bit about Data scientists. But, before moving on, have a look at Henry Harvin’s Data Science Course.

Who is a Data Scientist?

Data Scientists are analytics professionals who collect the data, understand it, analyze it, and solve complex problems. They need to have an ABCDE skillset to take up the organizational challenges i.e.-

  • A= Analytics
  • B= Business Acumen
  • C= Coding
  • D= Domain
  • E= Explain

Top Data Scientist Skills Needed in 2023:

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Mainly 2 types of skills are required for Data Scientists, Technical skills and Non-Technical skills-

Technical Skills Required for Data Scientists:

1. Machine Learning:

It is one of the fields of Artificial Intelligence, which helps in providing more accurate outcomes. ML is one of the most required Data Scientist Skills that focuses on building systems that can assimilate historical data and make a logical decision

2. Hadoop:

It is an open-source framework that is used to store and process high-volume data. Hadoop is one of the most necessary technical skills required for Data Scientists.

3. Mathematics:

Another one of the most essential Data Scientist Skills is the mathematical knowledge. Since, data science uses algorithms, data analysis, and statistics, therefore having a core knowledge of mathematics helps data scientists to work smoothly.

4. DevOps:

DevOps, as the name suggests, is the combination of Development (Dev) and Operations (Ops). It is a technique that emphasizes on the communication between the development and operations teams This is another one of the most vital technical skills required for data scientists. DevOps provides speedy delivery of higher-quality software.

5. Cloud Computing:

Cloud computing is basically used to store, handle, and control data online. Platforms like Azure and Google Cloud are used by data scientists to process a wide amount of data. Among all the data scientist skills, this one is the most utilitarian.

6. Microsoft Excel:

One of the most heard and known technical skills required by data scientists is Microsoft Excel. It is a software program that uses spreadsheets to display the data, results, records, and analysis.

7. Python:

Python is the most popular, object-oriented, and high-level programming language. This language functions as a strong tool for 333data analysis and thus proves to be very helpful to data scientists. This programming language is one of the most sought-after data scientist skills. 

# Pro-tip: learn more about Henry Harvin’s Python Programming course.

8. SQL:

It stands for Structured Query Language and is one the most serviceable technical skills required for data scientists. It is known that data scientists are supposed to work with data, hence SQL plays a very important role in extracting, manipulating, storing, and retrieving data from the database.

9. Neural Networks:

Another name for Neural Networks is Artificial Neural Networks (ANN). Apart from all the data scientist skills, this one is the most overpowering. It includes a mechanical structure with interconnected nodes. Neural Network displays the raw data using algorithms, and patterns.

Non-Technical Skills Required For Data Scientists:

10. Decision Making:

Choosing the best option from the alternatives available to achieve the organizational goals or managerial objectives is known as decision-making. Data scientists need to possess this non-technical skill and must know how to make a choice. It is one of the most required data scientist skills.

11.  Team Player:

Amongst all the non-technical skills required for data scientists, this one leads the list. As we all know, while working in an organization, data scientists have to deal with a group of people. This skill is one the most essential data scientist skills which helps in collaborating with the team, which further helps conquer the real-world obstacles/hindrances of data science. Data scientists must know how to handle the team and keep the organizational environment positive. It can further provide the best industrial results.

12.  Business Acumen:

Out of all the non-technical skills required for data scientists, possessing the appropriate knowledge about the business plays a pivotal role in the industry. Data scientists should have keenness, and quickness while dealing with business situations and as a result, providing the best outcome. Also, data scientists should know how to provide relevant solutions to customers’ problems/issues.

13. Communication:

Data scientists should know very well how to convey and explain suggestions, conclusions, and everything related to the profession to non-technical co-workers, consumers, and other divisions related to the business. Communication plays a vital role within any kind of organization. As they say, half knowledge is dangerous, hence the explanation and interpretation must be exact. Having excellent communication skills is one of the most essential data scientist skills.

14. Intellectual Curiosity:

It refers to holding curiosity while dealing with tough challenges. Data scientists are categorized as the most intelligent species. They must have the curiosity within them to deal with the most complicated calculations, inflexible dashboards, and highly mathematical analysis. Data scientists should always be ready to take up such challenges and consider them as their cup of tea. Intellectual curiosity comes under one of the most mandatory non-technical skills required for data scientists.

15. Great Data Intuition:

One of the topmost significant data scientist skills is having the foresight of what may happen next. Data available may not be always accurate, hence such skill is really necessary and helpful. Knowledgeable data scientist always knows what step has to be taken next, and they are more efficient in their work. Although great data intuition doesn’t come easy, it comes only with experience. But once this skill has been achieved by the data scientists, then the results speak.

16. “Out of the box” thinking:

When data scientists use their creative thinking and start working innovatively, then this “out of the box” approach provides satisfactory results and unconventional answers.

17. Analytical Mindset:

The capability of dissecting complex problems, analyzing them, and deriving a conclusion, this is what is known as an analytical mindset, which should be possessed by data scientists.

Career Prospects For Data Scientists:

The need for data scientists is escalating day by day. Data Scientists are highly in demand in industries nowadays, and the career opportunities under this are unlimited. This offers a guaranteed high-paying salary to the candidates, hence making this career option as one of the most sought-after professions. The future under this is studded with career opportunities. There are several career prospects for data scientists, some of which are as under:

  • Data Analyst
  • Machine Learning Expert
  • Data Mining Expert
  • Business Analyst
  • Mathematician
  • Data Engineer
  • Cloud Computing Expert
  • Software Programming Analyst
  • Data Quality Analyst

Henry Harvin’s Data Science Course:

Henry Harvin (HH) Academy is the top-listed educational institute. The data science course provided by HH enables the candidates to master the quintessential skills and subsequently proves to be the topmost training institute. 

Conclusion:

The top listed query about any kind of career is the “salary”. Since, the data scientist job offers you the best pay, opting for this career prospect definitely fits you. If you want to switch to a new profession or wish to kickstart your career. Also, if you have an interest in statistics and you understand algorithms, then a Data Scientist career is waiting for you. In addition to this, if you want to excel in your field, never hesitate to learn more and deep. Go ahead and join Henry Harvin’s data science course to open up the career path for you.

FAQ’s

Q1. Can I become a data scientist without a degree?

Ans. Yes. A data scientist profile does not need a degree. Just join a course and show up for your projects and internship to demonstrate your abilities.

Q2. Which institute provides the best data science course?

Ans. Henry Harvin Education Institute is the top-ranked academy that provides the best data science course, offers internships, and gives 100% placement support.

 Q3. Do I need to have the experience to become a data scientist?

Ans. No. You do not need to have any experience to get into this field.

Q4. Do I need to learn any programming language to become a data scientist?

Ans. Yes. You need to be an expert in Python language to become a data scientist.

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3 Comments

  1. Drasthi jain Reply

    As a beginner, I was intimidated by the technical skills required for data science. However, this course broke it down step by step. I gained proficiency in programming languages like R and Python, which are essential for a data scientist.

  2. Harneet kaur Reply

    I’m thankful for the practical training I received at Henry Harvin. This course taught me the critical skills for data scientists, including data exploration, model building, and data communication.

  3. Mamta yadav Reply

    Taking this course was transformative. I acquired essential data scientist skills in statistics, programming, and machine learning. The hands-on projects were invaluable, providing real-world experience. The instructors were exceptional, guiding us through complex concepts. I’m now confident in my ability to tackle data science challenges professionally.

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