Today, data scientists are in high demand, thanks to large amounts of data constantly being produced by consumers worldwide. Every time we interact with a mobile app or a website, the data is stored in a company’s database. They convey information such as a user’s preferences, location, and others.

From a business standpoint, these data are valuable because it could help them formulate customized marketing strategies that cater to specific clients. However, without professionals to collect, process, analyze, and visualize these data, they will remain abstract information.

Data scientists essentially allow companies to make smart, data-driven decisions. This is how most businesses improve their products and services and remain competitive. Data science is not only used in commercial businesses but across all fields including medicine, education, and tourism.

A career in data science is not impossible to land but it does require an extensive technical skillset. You’d need to sharpen your communication and problem-solving skills, among others, as the job requires you to present your findings to the relevant parties in a way that is easy to understand. More importantly, you’d need to demonstrate strong abilities in the technical skills listed below.

Machine Learning

Machine learning is a fundamental skill a data scientist needs. It is the application of Artificial Intelligence (AI) that gives a system the ability to learn from data, identify patterns, and make decisions. Systems are fed a certain amount of data and they will automatically learn to detect predictable patterns in data sets, making it possible for them to perform certain tasks without any human intervention. The knowledge of machine learning enables data scientists to perform tasks like analyzing millions of data in a quick and efficient manner, with little room for error.

If you are interested to learn more about machine learning, you can opt for Flatiron School’s Data Science bootcamp. This immersive program—available in-person and online—covers everything you need to know to pursue a career in data science. It should be noted that Flatiron prepares its students to be job-ready by offering weekly one-on-one career coaching sessions. It also guarantees your money back if you don’t get a job within six months upon graduation. 

Python

Learning Python is a must if you want to become a data scientist. It is a versatile language that is used to interpret, analyze, and visualize data. As a result, data scientists all over the world use Python to make their workflow more efficient. Today, Python has an active community with a huge amount of resources available to help beginners get better. Due to its popularity, there are many libraries that data scientists can take advantage of such as Numpy and SciPy. 

If you want a foolproof way of learning to code in Python, we recommend enrolling in Thinkful. It also offers a Data Science Flex course that can equip students with the fundamentals such as Python, SQL, Spark, and more, in just six months. The course is also flexible, requiring only 20 weeks of your time per week. The school does, however, require its students to demonstrate basic math and data skills when applying but it is nothing to worry about. It also provides one-on-one mentors and career coaches for its students.

SQL 

When handling a big amount of data, they need to be organized and managed in an efficient way so you can easily retrieve and work with them when needed. For this reason, data scientists must have a good grasp of SQL knowledge. It is a database language that is used to maintain, create, and retrieve relational databases. 

For this specific skill, Kenzie Academy is one of the most recommended schools. Not only it offers a high-level coding bootcamp, but its courses also cover many other complementary skills that are needed to pursue a data science career. The school has trained many successful coders who now are earning an average of $48,000 annually. 

Data Visualization

Data visualization is a way to represent visually gathered information. In this case, data scientists must have the ability to interpret graphically the information that’s under consideration. It is because when they do it, they are able to create better insights that will lead your company to make smarter data-driven decisions. Also, data visualization is not only about showing results, but it is also about learning and understanding data. In effect, data scientists are able to recognize data’s behavior. As a result, they learn and understand data’s vulnerability.

Basic Statistics

For data scientists, it is essential to have a good comprehension of statistics. It is because making predictions and finding structure in the collected data, are the most critical tasks that a data scientist has to accomplish. For this reason, they should be familiar with maximum likelihood estimators, distributions, statistical tests, and other analytical methods. In effect, knowing when different procedures are a valid approach will be one of the main aspects of data scientists’ statistics knowledge.

Cloud Computing

Cloud computing and data sciences are going hand in hand. It is because, generally, data scientists have to deal with data stored in a cloud. Nowadays, many companies, in order to not have a physical database structure, are now using cloud computing services. As a result, data scientists have to deal with platforms such as Microsoft Azure, Google Cloud, and AWS. These services provide access to frameworks, programming languages, databases, and many other operational tools that data scientists should be familiar with.

Also, having acquired knowledge in cloud computing is useful for data scientists because they can analyze, parse and sanitize data more efficiently as several platforms like the ones shown above, count with features or services that fit perfectly for those kinds of tasks.

Conclusion

As discussed, a data scientist’s job is not easy. Collecting, analyzing, interpreting, and visualizing data are all tasks that require a lot of effort and time. In some cases, data scientists hold the future of companies in their hands so, they cannot afford to be making mistakes. Although this may be true, the job satisfaction level is also high considering how much it pays. Take the first step toward becoming a great data scientist by sharpening those skills today.