With the emergence of data science, the present business domain has become logical like never before. They now correlated occurrences and events rationally to identify the cause of problems and come up with possible solutions. As a massive amount of data is being generated by organizations, the search for skilled data scientists has increased exponentially.
If you decide to learn data science just for the sake of landing a good job, you may lose steam midway unless you are passionate enough about the domain, have a clear idea of what you want to do, and a roadmap that would let you achieve your goals. If you love the wide variety of data that gets generated today and want to learn how it can help you take real-world, data-driven actions, data science is the field you should consider to get into. But before answering how long it would take you to learn data science to land a good job, it’s important to know where you are in your journey and what skills you need to acquire to meet your career goals.
Unquestionably, the career as a data scientist is one of the most promising options these days and it’s driving a huge number of enthusiasts to become a part of this community. Data science courses are being taken up by people across the globe. In addition, enthusiasts are also making transitions – coming to data science from different departments. However, the rise in the popularity of the career as a data scientist isn’t only generating lots of opportunities but creating a lot of competition as well – for both aspiring data scientists and those who are already working in the field. So, these days, the major question is how ‘to become a top-notch data scientist’ to stand out of the pack.
Data science features significantly when you talk about high paying future jobs. To understand what’s driving this trend, which experts say will only grow bigger with time, you have to know how the technology landscape has experienced tremendous growth and undergone a sea change over the last few years, thanks to a high degree of reliability on computer programs, data-based analysis, and digital technology. Working with huge sets of data and making data-driven decisions, in particular, have become a norm in most sectors – from banks and fintech to transportation, IT, and more. After all, almost each interaction with technology has data at its core. Thus, be it your online purchase at marketplaces like Amazon, streaming recommendations for you on Netflix or Amazon Prime, your Facebook feed, or even the facial recognition necessary to sign into your phone, almost everything leverages data to perform certain operations. No wonder why data science is the flavor of the season. Experts in the domain of data science predict that in several companies and industries that are less tech-forward than others, where data science today plays a supportive (or back-office) role in the business, the scenario will change soon in the future as these companies and industries get ready to access a huge amount of data – both structured and unstructured, as well as feedback loops. And that’s what makes the job of data scientists one of the most coveted ones in today’s world.
Data modeling stands for the process of developing a descriptive diagram that demonstrates relationships between different types of information which are to be stored in a database. It’s a theoretical presentation revolving around data objects and associations between different data objects. Data modeling is an important skill for every data science professional, whether one is architecting a new data store or doing research design for his/her organization. To excel in this key component of data science, one needs to have the ability to think systematically and clearly about the major data points to be stored as well as retrieved, and how they need to be related and grouped.
There’re lots of people who closely associate data with privacy issues, data leaks, and profit maximization techniques. Also, terms like data science, analytics, statistics, databases etc are ones that most people associate with the professional world exclusively. However, all these and many of their associated ones aren’t meant for data science professionals only. In reality, most people experience the role of data science in their day-to-day lives and in almost every situation. From new friend suggestions by Facebook to Google’s help to complete a search phrase to television shows predicted by Netflix in accordance to your preferences, and many more – data science is being used by common people in almost every situation.
The data scientist is considered as the best job in the USA for 2019 with $108,000 as a median base salary, plus an attractive number of predicted openings. According to the prediction made by the IDC (International Data Corporation), in 2020, worldwide revenues for business analytics and big data will reach over $210bn. These statistics are more than enough to motivate someone to learn data science. If you need some more reasons to learn data science, it’s associated with a huge number of new applications and industries that emerge from the judicious use of massive amounts of data. From self-driving cars and robots, bioinformatics, speech recognition to object recognition, neuroscience, and many more – data science is central to the entire organization dealing with any of these.
The domain of data science has been at the focal point of discussion for quite a few years now and there are no signs of it slowing down. As more and more businesses, organizations, and companies are waking up to the importance of extracting important insights from the pile of data that they are sitting on, the demand for data scientists, data engineers, and other experts in the field has increased significantly. No wonder that while there’s an increased focus on bringing such data science talent onboard, a whole new set of data science titles and roles too have been created to address the needs of the market.
If you have an analytical bend of mind, you may consider a career in the field of data science. Though there are many options to take your pick from, most aspirants have their eyes fixated on the coveted job of data scientists. But who exactly are data scientists? You may call them a new class of analytical data expert, who possess the technical skills necessary to solve complex problems. At the same time, these people also have the inquisitiveness to search for problems that need to be solved. Thus, when defining a data scientist, you can say it’s someone who blends some expertise of a computer scientist, a mathematician, and a trend-spotter. What’s the beauty of the field of data science is that it has numerous applications in various domains. Thus, you will find that there’s a high demand for data scientists in both the business domain and the field of IT, which make these professionals highly sought-after and well-paid. No wonder why a majority of aspirants looking to make a mark in the field of data science aim to become data scientists.