Top 10 Skill Required to become a Successful Data Scientist

Do you love to solve problems and determine easy solutions? Perhaps you have what it takes to become a Data Scientist.

Today, data is so much more than just information — it forms the crux of businesses. It covers everything — cookies, encrypted information, security codes, browsing/buying patterns, and so on. As a field, Data Science is drastically changing with time, and if you are tech-savvy, you must know that to succeed in an in-demand field, you must put in your time and effort to acquire the necessary skills.

Data Science is a blend of research with modern technologies. To clarify, let’s take an example of Java when it was newly launched. As soon as it came into the market, the companies were looking for candidates skilled in Java. Similarly, Data Science is a vital tool in this modern era, but what is even more crucial is people with relevant skills.

Companies are constantly advancing their skills in technology to meet the rolling competitive market and maximise their profits. In such situations, data science has come up as a weapon of the forthcoming technological era by setting the market standards and triggering global investors. There is no doubt that Data science is a tool of the present and the future, driving innovation across all sectors.

However, since the market is dynamic, it gets difficult to determine the latest tools and in-demand skills that companies demand from professionals. Thus, today, we’ll highlight some of the most trending data science skills companies are looking for in 2021.

But before that, let’s get familiar with Data Science.

What is Data Science?

Data refers to information. However, unstructured data is meaningless. Data science aims to extract meaningful information or patterns from seemingly insignificant data. This is done with the help of algorithms. These algorithms are, in turn, created with a knowledge of statistics, programming languages, business skills and communication. Depending on a person’s skills, they may work in any of the various inter-disciplinary roles associated with data science. This includes big data, data mining, data visualisation, machine learning and more.

Who is a Data Scientist?

There are several definitions to define a data scientist on the internet. We can define a data scientist as the person who performs data science using different data models. Data scientists deal with information from the science field and compute a mathematical or statistical analysis to find its solution.

They work to crack complex codes and machine problems with scientific discipline in a structured manner. Moreover, they use the latest technologies to reach feasible solutions. Data Scientists extract information from raw data and present the insights in a way that’s legible to all the stakeholders involved.

10 Data Science Skills you must be familiar with

1. Python

Python, at present, is the standard programming language for the technology sector. It has become the environment for optimal scientific computing unmatched anywhere. Moreover, the better part is the adoption of the Python language is easy to grasp. For those uninitiated, Python will come up as a helpful tool in the technological future. Over 50% of data scientists have a detailed knowledge of programming in Python or R language.

Solving problems using Python can be easy. Using several tools, you can reach a large audience of data scientists. These tools are:

• NumPy
• PySpark
• Math
• Pytorch
• Scipy (Scipy. stats)
• Keras
• Pandas
• Plot.ly
• TensorFlow
• Matplotlib/Seaborn
• Sklearn

2. Analytics

Analytics play a vital role in solving a problem with proper research. However, the technologies are not categorised directly for analytics. It depends on the foundation of complete visualisation and the requirements of the situation at hand. Statistics is a must-to-know course to achieve the best analytical process. It will help in defining a set of libraries focussing on data visualisation.

Statistics is a necessary foundation to get into the details and extract insights from a given set of data. Also, it will help in quantifying uncertain datasets and analyse the statistical behaviour of the dataset. Therefore, it is necessary to attain knowledge of statistics for a data scientist.

3. Data wrangling

In Data Science, the data you deal with can often be complex and confusing. Therefore, it is necessary to break down the complex data problem into simple data sets. It is crucial to understand how to handle errors in a data set. With the help of data wrangling, you can process error-free data for analytics and problem-solving.

4. Machine learning

Data scientists shoulder multiple responsibilities. Assigned with the crucial work of identifying and solving modern-day business challenges, they need to find innovative solutions to overcome them. Machine Learning algorithms and data-driven models can help you solve the task at hand and optimise its efficiency.

Machine learning deals with “training” machines to understand human interactions and apply them in various situations. This is done by feeding existing patterns into the device. The device can process data in real-time to predict data patterns and produce accurate data-oriented results based on the algorithms. A simple example of machine learning is a device that can pick out and recognise human faces after studying thousands of images of human faces.

5. Linear algebra and calculus

A well-versed knowledge of linear algebra and calculus helps make alterations in the algorithms for efficient and accurate results. Learning Algebra and Calculus gives support to make minute changes in the program according to the data models.

Many companies like Google, Amazon, Netflix, etc., look for data scientists with a good grasp of linear algebra and calculus.

6. Dev-ops

A data science skill considered unimportant is dev-ops. Data models, set to solve problems, sometimes need to respond to the virtual environment. As a result, dev-ops becomes a crucial part of data science management. Moreover, these skills can help to solve complex theories in programming to achieve optimal solutions.

You might face difficulty implementing in-hand dev-ops if you do not know how to place things together. Once you get familiar with dev-ops, you will find it easier to solve complex theories in less time.

7. Storytelling skills

Storytelling is a vital skill of data scientists that you must learn. It becomes necessary to enhance storytelling skills to develop data series. You are the problem solver of your company, and everyone depends on you to find clear-cut solutions for complex problems. It takes you down to convert quantitative solutions into a language everyone in your company understands.

Your responsibilities push you to translate results in a language the company uses. The data in hand is your character, and you need to create the storyline around it. The aim is to ensure that your information is understood by everyone — both technical and non-technical members. This is where your storytelling skills should kick in to help you communicate clearly with your colleagues, stakeholders, and partners.

8. Software Engineering

You can accomplish your dream of becoming a data scientist if you learn to channelise your learnings to increase the output speed. To maintain the growth of your company, you must write good quality codes. The process of production is crucial, and it is necessary to generate error-free codes.

Having a basic knowledge of software engineering lifecycle models can help managing complexities that arise while executing a data science program.

9. Big Data

In the last few decades, technology has grown tremendously. It has increased the number of data produced every second. The internet, digital platforms, Internet of Things have boosted data. The Big Data Analytic tools help organisations manage and store such large volumes of data and tackle data over-flow. Several big data tools have entered the tech market. Some of these tools are Hadoop, Spark, Flink, Apache, etc.

10. Model Deployment

Data Science is becoming a key component in every industry. As a data scientist, it gets necessary to understand and gain knowledge about your work. One crucial data science skill is model deployment but holds an under-rated impression on many data analysing organisations. The machine engineers deploy product models, but you need to develop them to improvise your data science skills.

Take an example of a product business, you need to present a project to your seniors, and they loved it. Now, what will be your next step? You have achieved your goal to develop a successful project. But, remember it has to be used by multiple people who are not data scientists. In such situations, model deployment comes into action. Even when your organisation does not require model deployment skills, try to gain knowledge about its basics. It will help you to grow in the data science field.

To Conclude — upskill to move up in your career!

These top data science skills can help you in landing high-paying data science roles in established companies. As every industry starts leveraging data science technologies, it has become the industry standard to possess data science skills.

Remember to remember that mastery of these skills comes with time. Till then, continue to gain work experience and work on your own projects. They are all making you more skilled. Would you like to resume your quest to mastering data science? Check out Executive PG Program in Data Science from IIIT-B & upGrad.

Data is the new currency of the 21st century, and if you wish to be a part of one of the biggest industrial revolutions — the digital revolution — you better take charge and develop data science skills. upGrad can be a good starting point for you!