As more and more companies are trying to become data-driven, it looks like each of them will need to employ data science, making the demand for data scientists even greater. The world is becoming connected increasingly and a huge amount of data is being generated every single day and businesses are trying their best to make use of this data to rise above the competition.
However, it’s not easy to become a data scientist. One needs to have an adaptable and definite set of skills. It requires a perfect mix of structured thinking, problem-solving and a lot of technical skills in order to become a successful data scientist. If you’re planning to become a data scientist, read on as we’ve put together some essential things that you’ve to keep in mind to become successful in your endeavor.
1- Good academics
Educational qualifications play a crucial factor in being a data scientist. Organizations often prefer candidates with a Master’s degree in the field of computer science, mathematics, statistics etc.
Also, there’re some research-oriented companies that look for data scientists who come with a PhD. So, if you’re just starting out, it’s wise to focus on building your educational qualifications.
2- Robust programming skills
To become a successful data scientist, your programming skills have to be at an exceptional level. Among other programming languages used in the field, Python is the most preferred and widely-used one. It’s Python’s adaptability that has helped it gain this position. You can use it for almost every step involved in the process of data science. You can work with different sets of data and create datasets.
Good knowledge of R is also preferred for data scientists. R is widely used to solve various statistical problems. However, if you’re not comfortable with programming, it may be a little difficult to master it because of its steep learning curve. If you’re not coming from a tech background, programming as a whole may seem to be extremely difficult.
There’re several courses offered by reputed institutes that can easily help you get started. Just don’t expect to do super cool stuff from the very beginning because that doesn’t happen. But once you’ve overcome the initial challenges and remain consistent, you’ll surely be able to master them.
3- Strong business intelligence skills
As an aspiring data scientist, you should focus on developing strong business intelligence skills that is one of the essentials of the field. These skills need the ability to communicate your findings to business decision makers. Engaging these people in a manner which captures their attention both logically and emotionally has become imperative for data scientists.
In any data-driven organization, a massive amount of data is produced on a regular basis that has to be interpreted to decision makers in an easily consumable format. Pictorial representations in the forms of charts and graphs are naturally more consumable to people than just plain numbers.
To become a successful data scientist, you should have robust communication skills together with the ability to use data visualization and data management tools. So, try to become familiar with tools like D3.js, ggplot, Tableau, matplotlib etc to be able to represent complex things in a simple manner. It’s also equally important to work on your communication skills. Though these are usually the least talked about skills a data scientist needs, they’re extremely important.
You can master multiple tools and latest techniques, but if you fail to communicate your analysis properly to the decision-makers of your company or your client, it’ll raise a question on your expertise. One effective way to overcome this is if you’re working as a data scientist, find someone from a non-technical department and try to explain data science terms to him/her. It’ll help you gauge your progress to a good extent. There’re lots of resources available on the web, so with a good amount of practice, you should be doing good.
4- True expertise in machine learning
In today’s data-driven tech world, machine learning has become one of the heavily demanded skills for data scientists. To be proficient to deal with a massive amount of data on a regular basis, focus on learning machine learning techniques and methodologies like ensemble methods, k-nearest neighbors, random forests, among others. You can carry out these techniques further with the help of R and Python libraries.
Also, it’s extremely important to understand that the datasets you usually work with in machine learning competitions are usually clean and they’re different from what you’ll be working with in real-life projects. In real-life projects, you’ll have to deal with unclean and messy data. It’s a difficult part and eventually becomes a part of your routine. There’s one thing you can do to overcome this hurdle is by reaching out to working data scientists and knowing about their experience. Of course, having a great score in a competition can greatly help you in measuring your learning progress, but the employers will want to know how you can leverage your knowledge in a real-life scenario.
5- Don’t try to learn multiple things at once
This is a common mistake done by many aspiring data scientists. There’re lots of tools used in the data science field and these people tend to focus on multiple things once at a time but they often end up mastering none of them. Ideally, you should pick one tool and get a solid understanding of it. For example, if you’ve started learning Python, don’t try to focus on learning R before you’ve mastery over Python. There’re lots of resources available that can help you learn each tool. So, take help of them and keep your patience.
6- Study and practice as much as you can
Despite what you may find in various posts, it’s never easy to become a data scientist. You’ve to devote a whole lot of quality time to become a successful one. You can always start with simple things and develop on that but you’ve to make sure that you spend extensive and quality time studying and practicing.
Data science as a field is huge so there’re certain areas that need to be studied over and over again. You should understand that without adequate practice, your study will never get complete. With practice, more questions keep on coming up and you’re compelled to study again to clear them out. Also, there’re lots of existing concepts to learn in the field and there’re new ones coming up almost regularly. So, you’ve to keep yourself abreast of industry trends and changes. Visit forums for data scientists, read articles, books to make sure you stay on the same page with those happenings.
7- Join a community
Data science communities can greatly help in throughout your journey of becoming a data scientist. As we’ve discussed earlier that data science is a huge and difficult field, chances are that you’ll burn out quickly and may spend a huge amount of unnecessary time attaining your goal.
However, with buddies and mentors, you could sail through this. Don’t go months of wasting time on a concept that someone could have helped you understand in a few hours. In data science communities, there’re people who’ve already done what you’re trying to do now. On the other hand, you shouldn’t look for help too fast when you haven’t tried well. Remember that there’re lots of things that you can learn from your own study, research and mistakes.
8- Do projects
To become a successful data scientist, you need to implement your learning. Take up real-life projects and try to understand the architecture behind them. In the data science field, hands-on experience matters a lot and large organizations often prefer applicants with this. In this context, communities can again help you to a good extent. If possible, try to collaborate on projects with fellow members. It’ll not only give your learning a boost but will also help you in establishing your expertise as a data scientist among your peers.
With the growing competition in the business world, the task of finding an effective data scientist has become difficult these days. As you can see, a data scientist needs to have a mixture of multidisciplinary skills like the ones discussed above. So, if you want to hold the century’s hottest job someday, start your journey now if you haven’t already. Just remember that becoming a data scientist isn’t an easy goal to accomplish, you need to remain consistent and stay focused to become successful.
Final takeaway
While certifications and degrees are surely valuable, relying on them solely may not be able to take you much further. This is because understanding a data science project lifecycle, dealing with deadlines, handling clients etc – all are valuable parts of becoming an effective data scientist.
So, try to apply your knowledge outside the classroom whenever possible. For example, you can maintain a blog where you consistently write about your analysis, post them on data science forums and ask for feedback. This’ll help you learn a lot and will benefit you greatly when you look forward to advancing your career as a data scientist.
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