The year 2020-21 has witnessed drastic developments in data science with giants like Twitter, Apple, Tesla, and companies like Netflix, TikTok, Binance, and Spotify hunting for professionals with expertise in writing ML algorithms or with expertise in machine/deep learning. India has also been part of this transition, with over 135,000 data science jobs available in the country until the first week of June 2021. That’s right. Post pandemic, businesses across the globe are working towards formulating intelligent data-backed smart decisions, to achieve the ROI.
The Journey from being a Data Analyst to a Data Scientist
The skills you garner 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 gone missing in any process. However, what if you could see through the data you are handling and design your models to analyze extensive unstructured data as well? The journey to being a data scientist has enormous learning as this field demands a combination of many skills. You might be excellent in handling structured data by gathering, processing, and applying algorithms as an analyst. However, as a data scientist, the scope of your analytical skills broadens with complex and unstructured data. If you aim for such a transition, you have to start applying data science as an analyst in your current job role. Pay attention to your presentation skills and highlight key areas in a project where your data science skills can deliver better.
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Data scientists are equipped with critical thinking that reaches beyond numbers and structured data. A strong base in programming languages combined with Mathematics gives data scientists the liberty to dissect the Biology and Chemistry of any network.
Data scientists build their own framework to handle multiple datasets— structured or unstructured. With knowledge of R and Python, data science allows them the freedom to answer bigger, unknown queries. Often, data scientists are also theoretically well-versed in aspects of artificial intelligence — given their in-depth knowledge of the machine and deep learning.
Questions you need to answer before making the choice
But, consider 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 shift of being a data scientist? The most important question that might haunt most analysts would be ‘how do you want to see your career graph grow?’ This is where the big difference comes in. With a choice of path that will make you a data scientist, your career becomes more challenging with new possibilities to design learning models which will set your skills apart from the herd.
Keep aside time to study research papers by prominent data scientists. Most of these will be readily available on the internet free of cost. Find your areas of interest and subjects of your inclination in the field, and take notes. When you spend large sections of your time understanding data science, you must validate your learning with facts. You will find such facts when you read the works of prominent computer and data scientists like Geoffrey Hinton, Rachel Thomas, and Andrew Ng, among many established experts who contributed to data science with their studies in ML, neural networks, and tools for designing models. Even the most distinguished data scientists of the world are still learning, so you have ample time to explore your interests in data science. It is better to create your own base from scratch for such a transition.
A Step-by-Step Guide
Here are 10 initial steps you can take if you choose to transition from a data analyst to a data scientist’s role:
- You can start with R and Python, and learn the skills to write your own algorithms. These help systems in any organization that aim for continuous intelligence to propel the business.
- If you have been a data analyst, you can relate to the importance of graphs which have proved to be one of the most efficient tools in deep learning. Refer to tutorials by Neo4J or GraphX, among other graph database management systems.
- Explore the what’s and how’s of GitHub.
- Participate in Hackathons, Kaggle competitions, and other such events. You can exchange your thoughts in your domain and find ways to the right approach towards being a data scientist.
- Resourceful crash courses by websites like Coursera and Udemy can enhance your coding.
- Learn concepts of data visualisation and web apps.
- Acquire knowledge in relational databases like Postgres and MySQL.
- Train yourself in concepts of distributed computing like Spark and Hadoop.
- Study cloud-based developments happening around the world. Amazon’s AWS, Google’s GCP and Microsoft Azure can be the core areas of your study.
- Find your spot in the team where you can implement what you learn. Use problem-solving skills of data science to establish the results you achieved in analysing datasets for the business of your current company.
Once you have connected all the nodes to “why the transition from being a data analyst to a data scientist”, you have to identify the skill gap and train yourself in respective areas. Your experience is also a crucial aspect as you will start afresh as a data scientist. However, if you have specialised knowledge in writing ML algorithms or coding through certified courses, you are halfway into the first step of being a data scientist.
Be proactive in your approach to learning through participation in various data science events; think critically of the skills you have as an analyst and enhance your analytical skills with problem-solving exercises. You also have to grab the opportunities to market your skills in the current organisation by applying data science to the given problem or a process that will provide you with the exposure you require for the transition. You will be able to very well connect with like-minded professionals who might be pursuing the same idea but with a different and better approach.
Such a transition does not happen overnight, so feed your curiosity to become a data scientist with training, learning, and participation.