Back in the 1990s, having a bank job was hot. And in the early 2000s if you were a software engineer, it was considered awesome. Just 10 years ago data scientist job was the sexiest job.
As time rolls, new jobs become the new sexy. Then why are we talking about data science now? Well, this job is still relevant even with the advent of AI. Because every business today relies heavily on huge data, which makes data-driven decision making, a valuable skill.
If you’re fresh out of college and thinking of what career to choose – a career in data science can be one. Let’s go through everything you need to know to start a career in data science.
Basic core skills you need to be a data scientist
Learn to code
If someone you’re starting your data science journey today, the first thing you should do is learn Python. It’s the go-to language in the business world, driving most data science teams.
Become friends with math and stats
Many people get stuck here. You don’t need to solve calculus or probability problems by hand. Knowing how an algorithm works is often more useful than solving complex equations.
For instance, if a company asks you to sift through millions of job applications to identify fraud, it’s not about complex math but rather understanding the problem. Knowing whether it’s a classification issue, or a clustering problem is crucial.
Get comfortable with data transformation
Companies rely on SQL (Structured Query Language) to query from huge databases. For example, customer data might sit in one place, streaming habits on Disney+ Hotstar in another.
Organisations love it if you can tie that data together meaningfully. Writing SQL queries and understanding how to navigate different databases helps you gather such insights.
Master a single tool for data visualisation
Data visualisation is crucial in any data science role. Depending on the company, you might use tools like Power BI or Tableau. The good news? Mastering one tool is enough. Once you grasp the basics of one, switching between tools is simple.
Presenting your analysis clearly gives you an edge. If you discover why customers prefer a certain product, you need to translate that into clear visuals to make it impactful.
Explore machine learning
The next step is machine learning. Focusing on the basics helps. For instance, see if you understand the difference between a decision tree and a random forest. Knowing how to evaluate and explain your models is key.
You might build a forecasting model that predicts a billion in sales for next week, but can you explain that to a businessperson in simple terms? This is where model explainability comes in.
Discover the magic of big data
Think of big data like a massive puzzle. Tools like Hadoop help you piece it together. Understanding the big data ecosystem lets you take your programming skills to the next level.
Whatever you’ve picked up—machine learning, data visualisation—needs to fit into this bigger picture. Mastering big data means applying all those skills at scale, and that’s where the magic happens.
Soft skills: The art of storytelling in data science
Let’s shift gears and talk soft skills. In big teams like those at Tesco, everyone knows how to code and build models.
But the ones who stand out are those who can communicate effectively. If you can’t clearly explain your insights, your technical skills won’t keep you relevant for long.
The first key skill is storytelling through data. Craft a narrative that grabs attention and makes your recommendations stick. A good story effectively conveys the key message.
Next, business expertise. Your data models stand out when you know how to pitch their value to the audience.
Telling a business executive that your model is 99% accurate might not spark interest, but if you say it’ll save ₹50 million, you’ve got their attention.
Data science jobs are indeed on the rise
The demand for data science roles is skyrocketing, with jobs growing by 18% annually. As companies rely more on data-driven decisions, there’s been a 50% jump in data science assessments.
It’s no surprise that data science courses are also seeing a 32% rise in enrollments over the past year, as more people look to build skills in this rapidly growing field.
Here’s how much you can make
| Experience level | Minimum average per annum | Maximum average per annum |
| 0-3 years | ₹ 3,41,478 | ₹ 6,29,319 |
| 4-6 years | ₹ 9,46,087 | ₹ 15,81,108 |
| 7-10 years | ₹12,95,933 | ₹20,80,097 |
| 11-15 years | ₹ 18,81,694 | ₹ 25,04,992 |
| 15+ years | ₹ 21,59,677 | ₹ 40,10,645 |
How to land your first job
Landing a job is a lot like trying to get into a club. You show up, there’s a massive queue, and getting in is tough. Companies have it the same way—tons of applicants, but only a handful of jobs. You apply online and hear nothing in response. It’s tough, no doubt.
But just like clubs, there is more than one way to get in. Let’s look at them one by one
Internships: Your first step to full-time
Do as many internships as you can—internships are a great stepping stone to a full-time role. They let companies see how you work and if you really know what you say you do.
Plus, they can easily turn that internship into a full-time offer if you impress them. Platforms like Zuno and foundit offer curated opportunities for internships and early career roles, so make the most of them.
Use the power of networking
Networking opens doors in unexpected ways. Go to conferences, talk to people, and build connections.
The more people you know, the easier it becomes to land a job or progress in your career. Never underestimate the value of a good conversation!
Build a personal portfolio
Your portfolio is your ticket to standing out, especially if you’re new or switching fields. Don’t worry about not having professional projects. You can create your own. Take a public dataset and predict housing prices using Python or analyse Amazon customer reviews.
Show the full process: data gathering, cleaning, building a model, and turning it into insights with clear visuals. This highlights both your skills and the ability to effectively communicate your findings.
Conclusion: Get ready to act
Getting into data science does not have to feel like cracking a secret code. Master the essentials—coding, problem-solving, data storytelling—and you’re all prepped up to putting yourself out there.
Focus on taking action. Build your skills, tackle real projects, and let your portfolio do the talking. Internships and networking are your fast tracks to the inside.
With demand for data scientists skyrocketing, now’s your time to step up, get noticed, and make your mark in this fast-moving field. Watch the webinar here to know more!
