What skills do data scientists need?

Technical Data Scientist Skills 

Data science professionals undergo extensive training to develop their base in the world of data science. As a matter of fact, data scientist have stringent educational requirements in the IT field. The majority of data scientist specialise in a certain field, such as business intelligence, decision science, and machine learning, to name of you.

Therefore, data science professionals have to have a technical skill set. Given below is the description of some technical skills that a data science professional must have.

1. Data Visualization 

The routine work of a data scientist depends on the data visualization skills. These technical skills allow an analytics professional to turn numerical and sexual data into graphs, maps, and charts. With the help of these illustrations, non-technical people in an organisation can understand trends and data patterns. Therefore, a data scientist must improve their data visualization skills.

2. Python 

In the world of data science, a data science professional must have a strong grip over this programming language. According to data science reports, more than 60% of data science professional use Python on a daily basis. In 2019, Python was marked as the best programming language for data analytics. 

The great thing about this programming language is that it has a huge collection of libraries that allow high level mathematics functions.

3. SQL/NoSQL 

SQL is short for Structured Query Language. For data scientist, having a strong grip on this programming language is a must. With this programming language, data scientist can query and manipulate large-scale data in relevant databases. According to reports, this language is so commonly used that it was marked as the standard language when it comes to relational database management systems. 

The beauty of SQL is that it is easy to use in the world of data analytics. Almost all data science positions require the use of SQL. 

4. Social Media Mining 

As the name suggest, social media mining is a skill that helps data scientist excavate data from social media website, such as Instagram, twitter, and Facebook. After collecting data, data science professionals can identify useful patterns and gain useful insights for businesses. 

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The purpose of social media mining is to help businesses understand social media behaviors and their target customer preferences. Businesses use this type of analysis to prepare their social media marketing strategies. 

5. Fundamental Statistics 

Statistics is the process that helps collect, organise, analyse, and interpret large scale data. The idea is to make it easier for data science professionals to perform daily practices. Data scientist must be familiar with statistical principles that they use to create statistical models. 

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Without fundamental statistics, data scientist cannot understand or analyse the available data. According to experts, a general understanding of fundamental statistics is essential for today’s data scientist.

6. Natural Language Processing/Machine Learning 

Like a humans, computer systems have a language that they understand and follow. As a matter of fact, computers also need a translator. In case you don’t know, natural language processing is part of artificial intelligence that can close the gap between a computer and human. 

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With natural language processing, computer systems can communicate with data science professionals in their own language. For instance, computers use natural language processing to read, analyse, and interpret text. The idea is to measure the sentiment and identify the most important parts.

7. Microsoft Excel 

Data scientist should have a strong grip on Microsoft Excel to achieve success in the world of data science. Microsoft Excel has a programming language called VBA. Apart from this, it comes with a tool pack that helps with statistical modelling and data analysis.

8. High-Level Math 

Data science cannot work without advanced mathematics. As a matter of fact, mathematical philosophies suggest that R is essential for technology development. Although you can become a data analyst without being an expert mathematician, you have to be familiar with mathematical principles to progress in the industry. 

Non-Technical Data Scientist Skills 

9. Teamwork 

You cannot deny the importance of teamwork when it comes to data science. As a matter of fact, if you don’t build a team of professionals, you cannot achieve your desired success. Moreover, if you have a toxic attitude, you may suffer from stress that may lead to failure.

Therefore, if you want to be more productive, you should be part of a team. The idea is to improve your collaboration skills. And these skills will benefit you as well as your team members.

10. Communication 

If you want to become a good data scientist, you should improve your communication skills. After all, if you cannot communicate you findings to other professionals or team members, you cannot make progress or achieve anything. As a matter of fact, communication is a critical skill for data scientist. 

However, most professionals find it challenging to improve their communication skills. Therefore, you may want to improve your communication skills. For this purpose, you can practice simplifying complex ideas into easy to understand ones.

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The Takeaways