How to go from a data analyst to a data scientist

The year 2020-21 has seen drastic developments in data science with giants like Twitter, Apple, Tesla and companies like Netflix, TikTok, Binance and Spotify seeking professionals with expertise in writing stories. ‘ML algorithms or with machine / deep learning expertise. India has also been part of that transition, with more than 135,000 data science jobs available in the country through the first week of June 2021. That’s right. In the aftermath of the pandemic, businesses around the world are striving to formulate intelligent, data-driven decisions to achieve return on investment.

The journey from data analyst to data scientistt

The skills you learn as a data analyst are the stepping stones to start this transition. As an analyst, you already know how to manipulate data to reveal patterns that might have been missing in any process. However, what if you could see through the data you manipulate and design your models to analyze a lot of unstructured data as well? The journey to becoming a data scientist is a huge learning curve as this field requires a combination of many skills. You might be great at dealing with structured data by collecting, processing, and applying algorithms as an analyst. However, as a data scientist, the scope of your analytical skills expands with complex and unstructured data. If you are aiming for such a transition, you need to start applying data science as an analyst in your current role. Pay attention to your presentation skills and highlight key areas of a project where your data science skills can be best.

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Data scientists are endowed with critical thinking skills that go beyond numbers and structured data. A solid foundation in programming languages ​​combined with mathematics gives data scientists the freedom to dissect the biology and chemistry of any network.

Data scientists create their own framework to manage multiple datasets, structured or not. With knowledge of R and Python, data science gives them the freedom to answer larger unknown queries. Often, data scientists are also theoretically well-versed in aspects of artificial intelligence, given their in-depth knowledge of machines and deep learning.

Questions you must answer before making your choice

But think about rethinking your choice. What do you want to be – a data analyst or a data scientist? Do you need such a transition? Why do you need this change of data scientist? The most important question that could haunt most analysts would be “how do you want your career graph to go?” This is where the big difference comes in. With a choice of path that will make you a data scientist, your career becomes more difficult with new opportunities to design learning models that will set your skills apart from the herd.

Make time to study research papers from leading data scientists. Most of them will be readily available on the Internet for free. Find your interests and topics of your inclination in the field, and take notes. When you spend a lot of your time understanding data science, you need to validate your learning with facts. You will find such facts when you read the works of prominent computer scientists and computer scientists like Geoffrey Hinton, Rachel Thomas and Andrew Ng, among many established experts who have contributed to data science with their studies in ML, neural networks and tools. template design. . Even the world’s foremost data scientists are still learning, so there is plenty of time to explore your interests in data science. It is best to create your own base from scratch for such a transition.

A step-by-step guide

Source: Kaggle

Here are 10 initial steps you can take if you choose to transition from the role of data analyst to that of data scientist:

To start

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  1. You can start with R and Python and acquire the skills to write your own algorithms. These help the systems of any organization that aim for continuous intelligence to propel the business.
  2. If you have been a data analyst, you can understand the importance of charts which have proven to be one of the most effective deep learning tools. Refer to the tutorials for Neo4J or GraphX, among other graph database management systems.
  3. Find out the what and how of GitHub.
  4. Take part in hackathons, Kaggle competitions and other similar events. You can exchange ideas in your field and find the right approach to become a data scientist.
  5. Ingenious crash courses from websites like Coursera and Udemy can improve your coding.
  6. Learn the concepts of data visualization and web applications.
  7. Acquire knowledge in relational databases like Postgres and MySQL.
  8. Learn about distributed computing concepts like Spark and Hadoop.
  9. Study cloud-based developments happening around the world. Amazon’s AWS, Google’s GCP, and Microsoft Azure may be the primary areas of your study.
  10. Find your place on the team where you can implement what you learn. Use data science problem-solving skills to establish the results you got from analyzing data sets for your current business activity.

Once you have connected all the nodes to “why switch from data analyst to data scientist” you need to identify the skills gap and train yourself in the respective areas. Your experience is also a crucial aspect because you will start from scratch as a data scientist. However, if you have specialized knowledge in writing ML algorithms or coding through certified courses, you are halfway through the first step in becoming a data scientist.

Be proactive in your approach to learning by participating in various data science events; Think critically about the skills you have as an analyst and improve your analytical skills with problem-solving exercises. You should also seize the opportunities to market your skills in the current organization by applying data science to the given problem or process that will provide you with the exposure you need for the transition. You may very well come into contact with like-minded professionals who might pursue the same idea but with a different and better approach.

Such a transition doesn’t happen overnight, so feed your curiosity about becoming a data scientist with training, learning and participation.


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Gourav Mishra

Gourav Mishra

I am a journalist. Photographer by passion and writer by profession, I like to write on various disciplines. Writing and words are my way of knowing the world in a transparent way. I’m here to bring nuances of tech to the world, while being a tech journalist at AIM.

Sean N. Ayres