How to go from being a data analyst to a data scientist

With so much data flowing in every day, there is a huge need for trained professionals who can derive meaningful interpretations from this data. So much can be done with this data at your fingertips: analysis, visualization, modeling, predictions. All of this cannot be done by one person. All of these require different skills in the data and analytics industry. Data analyst, business analyst, data engineer, and data scientist – these job titles, to a stranger, might sound very similar – all work with and analyze data. But in reality, these job profiles are actually quite different. Yet there is a lot of overlap in these areas and acquiring and mastering the required skills can help improve their job prospects and take on a more challenging role.

Often, professionals who enter the analytical space as data analysts wish to evolve into the role of a data scientist. The work of a data scientist is more challenging and rewarding, which has resulted in a huge increase in the number of professionals flocking to this field.

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Role of a Data Scientist Vs that of a Data Analyst

Some of the basic functions that a data analyst performs include:

  • Data extraction from primary and secondary sources
  • Interpret this data to study their patterns in order to solve business problems using statistical tools
  • Clean up data to remove information that is not useful
  • Use insights inferred from data to provide reports that can help make business decisions

A data scientist, on the other hand, has the following responsibilities:

  • Build models to solve business problems according to business needs
  • Create algorithms and machine learning methods to test data
  • Use various visualization methods to present the data and the different results of the data
  • Synchronize information from data, dig deeper into it to provide ways to solve the business problem to be solved

Data Analyst at Data Scientist. How to make the transition?

Before delving into the different ways to transition to a more difficult role of data scientist, it should be made clear that this is not an overnight process. Being a data scientist requires a combination of different skills, including a solid grasp of mathematical and statistical concepts, a good grasp of programming languages ​​and, most importantly, an understanding of a particular business problem and how to solve it through the data analysis and prediction.

Here are some steps you can take to begin your transition journey:

Develop your basic knowledge in the field

Before you even think about making the transition, you need to be very clear about what a data scientist is doing and do some soul-searching about what needs to be done to fill in the gaps needed for the transition and what skills the person now has. A data scientist not only manages data, but provides much more in-depth information. In addition to acquiring the right mathematical and statistical know-how, training yourself to examine business issues with the mindset of a data scientist and not just as a data analyst will be of great help. This means that while examining a problem, developing your critical thinking and analytical skills, digging deeper into the problem at hand, and finding the right way to approach the solution will shape you for the future.

Improve your coding skills

A data analyst might not have great coding skills, but surely should know it well. Data scientists use tools like R and Python to derive interpretations of the massive data sets they process. As a data analyst, if you are not good at coding or are unfamiliar with common tools, it would be wise to start taking some basic courses on those and then use them in courses. real applications.

Take introductory courses in data visualization, ML and deep learning

Besides learning certain tools, entering the world of machine learning, deep learning and decision trees would only add to its growth. Of course, no one expects you to become a pro from the start, but developing your interest and deploying such algorithms in projects will definitely benefit you in your career.

See also

Sachin Birla, who works as a data scientist at EY, says: “Typically a data analyst only works with tabular forms of data, but nowadays we are seeing an increase in image data and of text. For image and text data, traditional machine learning algorithms fail, and new algorithms or deep learning models are becoming popular. So, if you are planning to make the transition to data science, you should learn machine learning as well as deep learning algorithms. Apart from that, you should have a good knowledge of databases, basic math, algebra, statistics, and Python programming. Thus, the combination of all the given skills will make you a good data scientist.

Explore your skills outside of work

Participating in hackathons, contests, and Kaggle competitions will help build your confidence and understand whether you can truly apply the concepts in real world scenarios. Even if you don’t perform very well at first, keep pushing harder. More and more practice and participation will have long term effects.

Learn how to develop a “data scientist mindset” at work

A great way to develop this would be to learn from the data scientists who work with you. Try to think with them and find out how they approach problems as well. Getting a feel for their thought process while creating algorithms would help you understand the nuances of the work and develop your thinking skills.

Always stay up to date

Data science is a constantly evolving field. You always have to keep learning and staying current to stay relevant here. A great roadmap for a budding data scientist would be to follow data science leaders on social media, read the latest on-going research, connect with other data scientists, and attend data science conferences to stay motivated on their transition journey.

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Sreejani Bhattacharyya

Sreejani Bhattacharyya

Sreejani Bhattacharyya is a journalist with a postgraduate degree in economics. When not writing, she finds herself reading about geopolitics, economics and philosophy. She can be contacted at [email protected]

Sean N. Ayres