Dependency on data is continuously growing, and with it comes the need for more data scientists to fill the demand.

The application of data science is not bound to any specific industry or business line; every business on the earth needs data science.

If you’re in pursuit of being a data scientist path, you know that having proper knowledge and skills is the first step.

Having technical skills is not enough! You must also have soft skills that will help you climb the career ladder. Having these skills will help anyone stand out from the crowd of tonnes of other aspirants as the field grows.

Non-Technical Skills

These soft skills won’t require any technical training or formal certification. Every aspiring data scientist must have these skills if they wish to thrive in data science today.

1) Critical thinking

Critical thinking is a priceless skill that you need regardless of your career choice. It’s even more vital for data scientists because data scientists must appropriately frame questions, understand the findings, and move on to the next steps.

Critical thinking aids in objectively analyzing questions, hypotheses, and results and look at problems from a different perspective.

Critical thinking in data science means that you have to see the problem from all angles and constantly stay curious.

2) Effective communication

Again communication is a skill that is required just about everywhere. Whether you’re in an entry-level position or top management, you must have excellent communication. Having great communication skills helps you connect with other people and get things done efficiently.

Data scientists are expected to analyze data and clearly explain the findings to both technical and non-technical audiences.

Good communications skills will help you to explain insights in business-relevant terms and communicate the insights in such a way that highlights the value of the action. Until they present the results to the management in a proper way, it all would be useless.

3)Proactive problem solving

You can’t be a data scientist unless you have the skill or desire to solve problems. Data scientists must find tricky issues that are sometimes hidden and then quickly whirl to find the methods that will provide the best answers.

Being a data scientist, you have to identify various opportunities and problems and find their solutions simultaneously.

You must know how to approach these issues with available resources. Put on your detective’s hat and identify the most effective methods to use to get the correct answers.

4) Intellectual curiosity

Data scientists have to dive deeper into results and initial assumptions to find the main crisp of the data.

A data scientist must have the curiosity and creative thinking with a drive to know more and drive to find hidden problems, answer questions, and find hidden information. Data science is about discovering underlying trends and insights. A good data scientist will never settle for “just enough” but stay on the hunt for deeper information and answers.

5) Business sense

What if I tell you that data scientists are also industry experts? They must have a bit to bit knowledge about the field they are working in, whether it’s healthcare, sports or any other industry. Unless they have proper knowledge, they won’t understand the problems in the right way.

In a nutshell, Data scientists perform double duty. Data scientists should deeply understand the field they are working in, to solve current problems and think about how data can support future growth and success.

For example- Let’s say John is a data scientist working in google. So, unless he wouldn’t have proper knowledge about how google serves its customers or what affects the customers’ demand and needs, he won’t be able to properly assess the problems no matter how good the data is.

Data science is more than just playing around with numbers; it is applying different skills to solve a particular problem.

Related: Correlation vs Covariance

6) Storytelling

Storytelling helps data scientists to convey the results in an easy manner. Storytelling takes data visualization to a whole new level, allowing decision-makers to see things from a new perspective.

With the help of storytelling, data scientists weave a descriptive yet simple and informative story around the numbers, figures and trends to make it easy to understand.

7) Adaptability

One skill that is predominantly required in data science is adaptability. Technological innovation is climbing up rapidly, so professionals must also adapt to the latest technologies as quickly as possible.

As a data scientist, you have to stay on your toes when it comes to new technology or business environment changes and respond to it.

8) Prioritisation

As a data scientist, you will be bombarded with thousands of questions and task which may or may not be necessary.

So, a good data scientist must always have his priorities straight. You will need to decide which tasks and questions are actually worth responding to and how much effort is worth putting into those tasks.

Now, let’s talk about the technical skills you must have as a data scientist aspirant. These technical skills are a must-have if you want to pursue a career in data science. All of your work is going to revolve around these technical skills only.

1) Data preparation

Data preparation refers to gathering, arranging, process, and modeling the data. With data preparation, data scientists can analyze large amounts of structured or unstructured data and present it in the best forms for decision-making and problem-solving.

It is a crucial part of the analytics workflow for data scientists. No matter what tools data scientists use, they need to understand data preparation tasks and thier relatability to their data science workflows.

2) Coding

I can’t explain the importance of coding in data science in words! Data scientists need to deal directly with the programs that analyze, process and visualize data. Data scientists also create programs or algorithms to parse data and collect and prepare data through APIs.

There are tonnes of programming languages used in data science. Choosing a particular language totally depends on the role you are taking in or the industry you will be working in. However, the most popular ones are Python and R.

3) Maths and Statistics

Just like coding, mathematics and statistics play a vital role in data science. Having a solid knowledge of statistics enables data scientists to understand data and think critically about the data’s values.

Maths and statistics are used in exploratory data analysis and identify meaningful patterns and relationships in the data set.

Data scientists apply rigorous statistical thinking to extract meaningful insights from the data and to understand the robustness of various models.

4) Ability to leverage machine learning and artificial intelligence (AI)

This will help you to understand how and when machine learning and Artificial Intelligence can be appropriately used for the business. However, machine learning nor Artificial Intelligence will replace your role in most organizations. But making use of them will only enhance the value you deliver and help you work better and faster. Recently one famous data scientist quoted: “To realize the promise of Artificial Intelligence and machine learning, you need a number of quintessentially human skills. The biggest challenge in Artificial Intelligence is knowing whether you have the correct data or not.

The market is crowded with institutes selling different courses, but nobody actually teaches how to apply these skills in real life. Theoretical knowledge can be gained by doing courses, but the learning would not be complete until everything is applied to practical problems.

5) Deep Learning

To put it simply, deep learning is an advanced form of Machine Learning. With deep learning, you can overcome the limitations of traditional Machine Learning techniques and make the best out of your data. Digital assistants like Siri and Alexa, Auto-driving cars are all fruits of deep learning.

Data scientist is something that is highly technical and having disciplines like deep learning can be very helpful.

6) Data Integration

Combining all the residing from different sources to provide a unified view is called data integration. Every Data Science aspirant should have proper knowledge of data integration. Data integration allows businesses to analyze data precisely for business intelligence. Consider this as an added skill set that can help you land in big organizations like Amazon and Google.

7) Data Munging

Every activity that data scientists do to turn the raw data into clean and structured data is called data munging. Data scientists usually take the help of ‘R’ and ‘Python’ packages to do it. As a Data Science practitioner, you should understand all the essential aspects of the dataset and remove all the redundancies.

Conclusion

Data science is indeed a very rewarding career opportunity with a promising future. It is not necessary to learn everything initially, but with time, you will have to master everything if you want to secure a good position in your company.

Data science is an ever-evolving field. With every technological and business environment change, tones of things change in data science hand in hand. Learning is never-ending in the field of data science, you master a particular skill or tool, and it gets run over by a new tool the very next day. So, it’s crucial to keep upskilling on a regular basis and stay updated with the latest trends and innovations.

Author Bio

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning.

PMP Certified

ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe Currently authoring a book on ITIL “ITIL MADE EASY”.

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices

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