Data science continues to gain unprecedented attention in the job market as concepts like big data, data analytics, data visualisation and machine learning have started making more sense to people. Today, for a company to excel, it needs to have a clear understanding of how their customers behave – and that is only possible by understanding their customer data.
Take the example of Amazon, the e-commerce giant. Their beloved product recommendation system is a result of sophisticated data-science algorithms. These algorithms can predict with enough certainty whether you’re going to purchase the product or not. If these predictions point in a positive direction, Amazon moves that product to a storage unit near you so that you can get faster delivery if (or when) you do decide to buy the product.
Not only Amazon, many companies today thrive on data to provide you a better experience overall. And for this, they’re always on a lookout for a skilled data scientist. No wonder, for the last few years, data science has been among the highest paying jobs in the market. So much so that the Harvard Business Review named it the sexiest job of the 21st century.
However, to fully appreciate the importance of data science, you’ll need to understand something.
What Problems Does Data Science Solve?
Let’s look at what are the problems that data science so accurately solves:
- Finding context in large volumes of text: Suppose you run a public consultation service, and want a way to go through a lot of responses to find out the common context. For such a problem, natural language processing comes into use, which is a subset of data science.
- Predicting what will happen: Did you know that Target, a US-based retail store, predicted that their customer – a teenage girl – was pregnant? And that too, just by looking at her purchase history. Talk about making predictions using data!
- Classifying and categorising items: These models come in handy when you are training your algorithm. Using classification and clustering, you can make your system’s knowledge base much more robust. And how do you do this? Using data science.
- Finding outliers from your data set: Suppose you are a bank that needs help identifying potentially fraudulent transactions from the millions of transactions that occur on its server every hour – data science to your rescue!
- Understanding your data: If you have big data, you’ll more often than not have a story that goes with it. A story that’s written using visualisation and patterns. Understanding that story is a vital task in growing your business. And, to do that, you need data science.
Keep in mind that the data we’re talking about is in terabytes, and not one excel sheet that needs to be analysed. These problems fall into different brackets – each of which comes under the umbrella of data science.
The job opportunities in data science are numerous, and encompass these major roles:
- Data analyst
- Machine learning engineer
- Data science generalist
- Big data architect
Each of these job roles has varying compensations and responsibilities, but one thing is common to all – their demand in an organisation. Each of the above-mentioned job profiles plays a crucial part in any data analytics team. And, depending on what the company’s looking to do with their data, they’ll mostly require people for most of the job roles mentioned.
If you don’t believe us, you’ll definitely believe what IBM claims. According to them, the demand for skilled people in this domain is likely to soar to 28% within next two years. If we talk about India, the companies in Bangalore are willing to pay anything from ₹20 lakhs per annum and above if you fit their needs. Talk about a hefty salary!
Why? The reason is simple – there is a major lack of adequately skilled people in the field of data Science.
How To Set Foot In The World Of Data Science
The above-discussed statistics can leave anyone stumped. And, if you’re someone with a flair for data, you’ll most probably have thought of switching your career already. If that is the case, let us tell you – it might not be that easy, but it’ll be worth it.
If you’re just starting off, here are some tips that’ll come handy for you:
- Choose the right role: Analyse and introspect thoroughly. Getting stuck in the wrong position is worse than being in a strained relationship.
- Enroll in courses and follow them religiously: If you’ve not been following the article carefully, you must have missed a keyword – skilled. You need to upskill yourself in this domain continually.
- Choose a language and stick to it: Python vs R has been an ongoing debate, and there is no end to it. However, if you’re just starting, we advise you to pick any one of these languages and stick to it till you find something that can’t be done using the language you choose. Then, you need to switch to the other.
- Join a peer group: There are various Facebook communities where you can find skilled data scientists and discuss things with them. This is the fastest way to upskill yourself – by talking with the best in the business.
- Focus on building something, and not just theory: This goes without saying. Most likely, you’ll be building applications or algorithms during your job in this domain. You’ll not be taking university exams. The theory is useful to understand the core concepts, but you should look to get your hands dirty without wasting much time if you’re to truly succeed.
A throwback to the time when computers had started to get mainstream – do you remember the sudden rise in demand for skilled computer scientists? In those days, people who jumped ships to computer science before it became mainstream had a better and more successful career. Likewise, data science is the new thing these days. So, if you’re thinking of changing your career path to data science, don’t take too long to think. Get, set, and go!