The 12 Analytical Skills for a Successful Career in Data Science

As the field of data science expands and provides many rewarding career opportunities, it also becomes more competitive as more professionals enter the industry seeking long-term employment.

To stand out in this competitive environment, it is essential for job candidates to acquire the skills and training desired by top employers and to possess the analytical mindset that is crucial for success in the field of data science.

In this context, this article aims to explore the key analytical skills required for a career in data science, and why they are so important.

Analytical Skills

Employers seeking professionals with strong data analytics skills look for candidates who can approach complex issues from multiple perspectives, and use observation, research, and logic to develop effective and efficient solutions.

These analytical skills are not innate but can be developed over time through trial and error.

Anyone can learn data analytics skills, including the process of collecting and observing information and using it to analyze an issue, and develop a critical-thinking process that leads to innovative solutions.

Ultimately, the goal of analytical thinking in the field of data science is to improve processes, increase productivity, and drive greater success.

Importance of Analytical Skills

Analytical skills are highly valued by employers as they enable individuals to effectively evaluate and solve business problems.

These skills are particularly crucial in the field of data science, where a thorough understanding of data and the ability to extract insights is essential. Possessing strong analytical abilities can set a candidate apart in the job market.

Analytical Skills To Become Data Science

1. Big Data Collection and Statistics Skills

Statistics is a science that focuses on the development and methods for collecting, analyzing, interpreting, and presenting empirical data.

One part of statistics is probability. This term is closely related to statistics because it is used to calculate the probability that an event will occur.

Mathematical analysis is a part of mathematics used to calculate limits and relevant theories, such as integrals, derivatives, infinite series, and other mathematical functions.

In addition to understanding statistical theory, a prospective data scientist must also understand data analysis and storage tools such as Oracle, MySQL, Microsoft, SQL Server, and Teradata.

2. Programming

The next data scientist skill is related to programming. Quoting Springboard, some of the languages they usually use are:

a. Python

Not only developing but Python is also used in the world of data science. You can use it for:

b. R

Next, R. R is also commonly used for data manipulation and graphing.

Even though it’s more common in the academic world, you should consider this language. The word Edureka, and R is used by Google, Firefox, and other technology giants.

3. Data Visualization

The fourth skill that you must master is visualizing data. Good data scientists have to realize that data contains a certain message and they have to translate that message.

The data that has been analyzed must be presented in a form that is easy to understand when someone reads the report.

Therefore, the ability to visualize is very important because it can have implications for corporate decision making. Some tools that can help in visualizing data are Tableau and Power BI.

4. Data Wrangling

Neat and organized data is certainly easy to process. Unfortunately, in companies, many datasets could be more organized.Some information needs to be completed, duplicated, or even wrong. That is why data scientists must have data-wrangling skills.

Launching SolveXia, this skill allows you to process rough data. Finally, valuable information can be extracted from there.

5. Database Management

Becoming a Data Scientist will always be related to data. The data processing process consists of data collection, data grouping, data processing, analysis, and data presentation.

To do that, where does the data come from? Data collection can be done by accessing data from the database. However, if the database that is built is not good, it will complicate the process.

One of the tasks of a data scientist is ensuring good infrastructure and data management. Therefore a Data Scientist must understand databases.

Tools that are usually used for databases include Microsoft SQL Server, MySQL, Oracle, and NoSQL. To become a Data Scientist, you must at least master SQL. You can start by understanding the syntaxes often used to process data.

6. Data Storytelling

This is a quote from Stephen Few, a data visualization expert. Few said that numbers carry an important message. However, you must package the message to be clear and convincing.

Data storytelling itself is a technique for conveying messages. Its distinctive feature is visualization, supplemented by a brief explanation. This makes the information easier to understand.

Quoting Preschool, when other people understand information, that information can be acted upon. Finally, there are concrete steps or decisions taken. Data analysis and processing are not in vain.

That is why this skill is important for a data scientist to master.

7. Business Management

Not only dealing with data a data scientist also needs to have strong business acumen skills. With it, you can distinguish potential problems and challenges that need to be solved for organizational growth.

This skill is important so your company/organization can explore new business opportunities.

8. Intuition Data

This skill can actually be obtained with the right experience and training. Data intuition is something other than something that can be measured.

Big data sets are not always valuable or of particular value to a company. A knowledgeable data scientist knows when to look at the wider lens to get the necessary information.

9. High Curiosity

Data science is a field that is always evolving and rapidly changing. Therefore, you must always be curious and want to continue learning.

Apart from that, you also have to have an open mind. Because with an open mind, you will receive all input and improve yourself.

10. Communication

The qualification to become a data scientist is, of course, having to understand how to extract and analyze data.

However, for the data scientist’s task to run optimally and for the company gets the desired results from data analysis, communication skills are needed to convey these findings.


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