Data Science has been a trending field for a while now. Harnessing the power of data to understand hidden patterns and make important business decisions has become a norm for companies that work extensively with data. This boom has benefited everyone, including the budding software engineers looking to venture into this field and build a solid career or senior managers who want to increase the ROI of their respective departments. Latest developments such as GenerativeAI and ChatGPT are making headlines all over the globe due to their endless applications and tangible possibilities. This advancement in technology is bound to turn heads and make everyone curious as to why data is becoming so popular and mainstream. Professionals from diverse career paths such as software engineering, data analysis, business analysis, consulting, and managers are keen to know and understand how data can play a crucial role in their current job roles. Well, if you are interested in transitioning into this amazing field, I am here to help you do the same in some easy steps. Let’s find out how can one become an ideal data scientist!
Data is the new oil for companies
Who is a data scientist?
A data scientist looks after the data mining part of any given dataset. They need to first extract data from a relational/non-relational database and save it in a format favorable for analysis. Next, they need to scan and go through that data to check if the data is consistent or not. If not, they need to perform data wrangling. Once the data is at their disposal, they need to work their magic and use machine learning algorithms which can help them derive insights from the data and produce promising predictions. These results are later presented in the form of charts for visualizing and deciding which factors can help the company increase its ROI and provide value to its product. All these actions can get daunting at times but with practice and real-world applications, one can easily learn these skills and become a good data scientist.
Why should you become a data scientist?
By 2030, the amount of data generated from different sources will be exponential and overwhelming. It is expected to cross 1 yottabyte which is equivalent to 1000 zettabytes which is roughly around 1 quadrillion gigabytes! This kind of data, in any form, can help shape the IT industry by creating more jobs and will help shape the future of Silicon Valley of India. At the moment, data scientists and data engineers are a trending market in the data field. They earn somewhere around 8-10+ lpa on average and can go upwards of 25-30 lpa depending upon their skills and experience. Data Scientists with a minimum of 10 years of experience are expected to earn around 60-70 lpa.
Educational qualifications required for a data scientist
You might be curious as to what academic background one needs to get into this field. First of all, today many students while pursuing their undergraduate days are picking up data science skills via free online resources available. These resources are significant enough to leverage your skills with data science and make you industry-ready for an internship in this field. Data Science is a field that moves fast with advancements and the upgrades happen even more at a faster rate.
One can achieve a graduation degree in computer science or any field and still learn data science. The only advantage the computer science guys have is their foundations are strong and they know coding in 1-2 programming languages. Those who are looking to transition into data science can so do by learning a few unique skills and maintaining a portfolio of projects which can help them to showcase their interest in interviews. Doing a master’s degree in data science can certainly help you upgrade your skills and showcase your portfolio. Also, a professional online certification that helps budding engineers learn and understand data science in depth can be useful. Certifications don’t always guarantee a job but are good to have on the resume.
Skills required to become a data scientist
As per Indeed.com, to become a good data scientist, one needs to be consistent and master the following skills:
1. Communication Skills
Data science relies heavily on story-telling. The findings that we analyze from our models make us realize how the data can help us solve our business problems. One must be concise and clear with their understandings from data-driven results and should be capable of communicating it with their client or stakeholders.
2. Critical Thinking and Analytical Skills
Data Scientists often are required to make quick decisions related to a problem. This requires them to have good analytical and problem-solving skills to visualize the problem in front of them from various angles and find the appropriate solution. At times, the solution might feel a bit overwhelming due to the scale of the project which is fine.
3. Business understanding
Companies hire data scientists not to merely perform machine learning on the given dataset. They expect you to be decisive and comprehend the various parameters that are involved in solving a business problem. Data is useful only if it helps businesses to upscale themselves and provide value ot their customers in real-time.
4. Technical Skills
Being a good coder doesn’t imply that you are a good data scientist. Understanding the business angle of the problem and then thinking of various solutions to solve that problem fits the description of a data scientist.
Python programming language is the most commonly used language along with R for data processing. One can argue that a data scientist must have strong foundations in statistics and mathematics to stay relevant in this field.
Machine Learning, Deep Learning, and Natural Language processing play a key role in forming the algorithms in data science. Also, to showcase the findings, we need data visualization tools such as PowerBI and Tableau.
To further upscale our models, we need to deploy them on the cloud architecture such as Amazon Web Services(AWS) or Azure in the future so a bit of hands-on on these technologies will be a bonus. Sometimes to retrieve data from the servers, one must also know the SQL commands to be able to get data for deriving insights.
5. Hackathons and Github
Many of you may already be aware of Github. Its an AI-based online platform that enables developers to create, collaborate, and share their ideas for a project or assignment. They can store their codes online in a repository and help others to learn and create dynamic projects online. Having an account on GitHub and practicing coding every day is a great idea to start for a good data scientist. It will enable them to learn the high-level design of a project through their mentors. There are numerous hackathons live online on websites such as Analytics Vidhya, Hackerearth which will help you understand a business problem and how to tackle it using different approaches. Remaining active in this vast community can be a plus point as it will keep you abreast with all the latest happenings in this field.
How to apply and what to keep in mind
Once you have hustled for I guess some 6-7 months in understanding and practicing the important concepts involved in data science, it’s time to create a good resume and showcase some of the projects that you have created in your portfolio. A diverse portfolio that includes projects from machine learning, data cleaning, deep learning etc. will help you target different domains in data science. You can target domains such as e-commerce, retail, insurance, banking, finance, sales, edtech, entertainment, sports, healthcare, automobile, and countless more.
You need to create a healthy profile on both Linkedin and Naukri and apply aggressively. You need to update your profile almost regularly to get more calls from more companies. In the beginning, don’t worry about the work that you will be getting. Whether it’s computer vision or data cleaning, all have their advantages and will help you progress in this field and help build your portfolio. Keep building more projects and keep amassing the latest developments in data science. Sometimes, it might go overboard but being patient and consistent in this field is the key. The job market might sometimes discourage you from applying and also, applying to different companies can get frustrating at times, but always remember, creating a solid roadmap and following it until you reach your final goal should be the ideal mantra of a good data scientist!
