Acquiring the skills required to become a data scientist is extremely essential for anyone looking to get a job in that position. But you should understand that data science is a highly complicated field and it requires a lot of skills to become a data scientist. While it’s practically impossible for anyone to have all the skills related to data science field, there’re some skills that differentiate between a good data scientist and a great data scientist. Here, we’re going to discuss the skills which you should focus upon to become a top-notch data scientist.

Crucial skills to become a top-notch data scientist

1- Continued education

While there’re notable exceptions, in general, data scientists come with a strong educational background that helps them to attain the in-depth knowledge necessary to perform their job responsibilities. Forty-six percent of them come with PhDs while eighty-eight percent hold a Master’s degree at least. The most common fields of study include mathematics, statistics, computer science, and engineering. If you want to become a top-notch data scientist, your education shouldn’t end there. You should try to undertake online training to learn specialized skills that are creating buzz around the data science domain. Then, you can go ahead to get a Master’s degree in any of the fields related to data science. In addition, you should keep on practicing what you learned in a class by exploring data analysis, starting a blog etc to learn more about the topics.

2- Python

Python is the most popular programming language in the data science field. In fact, a large percentage of data scientists prefer to use Python as their primary programming language. In data science processes, it can be used for almost every step involved. You can use not only across large datasets but in creating datasets as well. A huge percentage of data scientists across the globe consider Python as the foundation for performing data analysis tasks. Hence, to become a top-notch data scientist, you should try to become a master of this language.

3- R

R programming language is heavily used in data science for statistical problem-solving. You can use this language to solve almost any data science related problem and thus, attaining a solid understanding of R is crucial to become a top-notch data scientist. Though R comes with a steep learning curve, there’re lots of great resources available on the web that can help gain adequate knowledge. Alternatively, you can join a coding bootcamp as well to gain knowledge and hands-on experience.

4- SQL

Even though Hadoop and NoSQL have become a large part of data science, proficiency in SQL (Structured Query Language) is important to become a top-notch data scientist. SQL is particularly designed to help data scientists to access, communicate as well as work on data. It can also help in transforming database structures and carrying out analytical functions. Concise commands of SQL can not only help to save time but also lessen the amount of programming he/she needs to perform difficult queries.

5- Apache Spark

One of the most widely used big data technologies, Apache Spark is a big data computation framework similar to Hadoop. However, Spark is faster than Hadoop which reads and writes to disk, but Spark caches the computations in memory. To become a top-notch data scientist, you need to become proficient in Apache Spark as it’s particularly designed for data science to help in running complicated algorithm faster. It helps in distributing data processing when you’re dealing with a massive amount of time and thus, saves you time. In addition, it helps data scientists to deal with complex unstructured datasets and it can be used on a single machine or cluster of machines. The strength of Spark lies in its platform and speed, both of which contribute heavily toward carrying out data science projects easily.

6- Hadoop platform

Having experience with Pig or Hive is considered a strong selling point and thus, important to become a top-notch data scientist. Data scientists may encounter situations where they need to send data to other machines or the volume of the data they’ve exceeds the memory of the system, this is where Hadoop helps them. Hadoop can be used to convey data to different points on a system quickly. In addition, it can be used for data filtration, data exploration, data sampling, and summarization.

7- Machine learning

Though a significant number of data scientists aren’t proficient in areas and techniques of machine learning, a solid understanding of them is needed to become a top-notch data scientist. Machine learning techniques like logistic regression, decision trees etc help one to solve various data science problems which are based on predictions of key outcomes. Advance machine learning skills like learning methods (supervised learning, unsupervised learning, and reinforcement learning), natural language processing, time series, computer vision, adversarial learning etc can help a data scientist stand out of the pack.

8- Unstructured data

Ability to work efficiently with unstructured data is crucial to become a top-notch data scientist. Unstructured data refers to undefined content which doesn’t fit into database tables. These include blog posts, videos, customer reviews, video feeds, social media posts etc which are heavy texts lumped together. Sorting unstructured data is difficult as they’re not streamlined. By working with unstructured data, data scientists can untangle insights which can help in effective decision making.

9- Data visualization

A huge amount of data is being generated frequently by the business world and this data has to be translated into a format which will be easy to understand by average people. As people understand pictures in forms of graphs and charts more than raw data naturally, it’s the responsibility of a data scientist to visualize that data with the help of different data visualization tools like Tableau, Matplotlib, ggplot etc. These tools help data scientists to convert complicated results from their projects to an easily comprehensible format. Data visualization enables businesses to directly work with data. This lets them grasp insights quickly and act on business opportunities to gain a competitive advantage.

10- Business acumen

A robust understanding of the particular industry you’re working in is crucial to become a top-notch data scientist. It’s also important to be able to discern the problems critical for the business and identify new ways the company should be leveraging the captured data. To perform this task efficiently, data scientists need to understand how the problems they solve can impact the business.

11- Communication skills

When it comes to hiring an elite data scientist, companies look for someone who can fluently and clearly communicate their findings to non-technical teams like Sales or Marketing, apart from having the above skills. A data scientist has to enable the business to make useful decisions by arming it with quantified insights. He/she also needs to understand the requirements of the non-technical teams in order to appropriately wrangle the data. Effective data storytelling is another key requirement to become a top-notch data scientist. A data scientist must know how to develop a storyline around their findings to make it simple for everyone to understand. For example, presenting a table of data isn’t as sharing the findings from that data in a storytelling format.

Final Takeaway

These days, there’re lots of events, coding seminars, data science meets, hackathons etc organized by leading organizations to groom talents and scout for the best as well. Participation in those events not only helps you to broaden your knowledge horizon to encounter real-world challenges but also to network easily. Workshops and data science bootcamps greatly help you in taking your skills to the next level and give you a competitive edge as well. You need to have a solid understanding of the majority of the above skills to become a top-notch data scientist. And to learn and sharpen those skills, you need to pick a premier institute which offers best courses on data science topics. These days, the marketplace is flooded with lots of data science courses. A significant number of training academies offer lucrative discounts on those courses. However, it’s much more than pretty packages or hefty discounts to choose the right course for yourself. In order to succeed in your journey to become a top-notch data scientist, you should have a basic knowledge of the courses you’re planning to undergo, their individual offerings to be able to compare them and take your pick, and a clearly chalked out career plan.

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To learn more about data science, click here and read our another article.

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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.

.  .  .

To learn more about data science, click here and read our another article.

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