Data science is a career that you can continuously pursue irrespective of your previous skill or experience. Adding some data science skills to your skillset makes you more desirable as a job prospect. Even as an employee, you are more valuable to your organization. However, there are several data science skills, and you must know the best ones that you can learn and improve on to make you more valuable in your organization.
There are some core data science skills that you need to be successful in the industry today. This article discusses eight of these skills.
1. Machine learning skills
Machine learning is essential for people working at a large organization with access to massive amounts of data and people working at companies where their product is data-driven, such as Uber, Google Maps, and Netflix. Getting yourself acclimatized with ML methods means understanding things like ensemble methods, random forests, k-nearest neighbors, etc. To implement most of these techniques, you need to understand Python libraries. This is why you must know machine learning to understand how algorithms work. You also have to understand broad strokes, as well as the most appropriate time for each technique.
2. Programming skills
If you’re vying for a role at a company, it is expected that you understand the use of many of the tools necessary in your industry. Basic programming skills that you need to understand are database querying languages such as SQL and statistical programming languages such as R or Python.
3. Statistics
Every data scientist must understand statistics very well. So it would be best if you familiarize yourself with maximum likelihood estimators, distributions, statistical tests, etc. Of course, you also have to do the same with machine learning. However, one aspect that will stand out in your understanding of statistics knowledge is knowing when specific techniques are the best approach and when they are not. All companies need statistics, but it’s more crucial for data-driven companies in which stakeholders depend on you to design/evaluate experiments and also make decisions.
4. Linear algebra and multivariable calculus
According to an essay writer, these two concepts are crucial for companies where data defines their product. Every improvement in algorithm optimization or predictive performance may mean a big win for the company. Therefore, it is essential to learn these skills.
Employers may ask for some of the statistics or machine learning results that you use elsewhere when interviewing for data science roles. They may also ask questions in linear algebra or calculus as they are the basics of these techniques. This is why data scientists must understand these skills, even though there are numerous implementations in R or Python. The fact is that you may need your data science teams to develop their in-house deployments.
5. Data visualization and communication
Data visualization and communication are other crucial skills in data science that you should learn, primarily if you work at a young company that is not used to making data-driven decisions. However, it is also crucial if you work at a company where the data scientists are seen as those that help other people arrive at data-driven decisions.
Communicating is how you describe your findings and how techniques work for both the non-technical and technical audiences. It is also beneficial to familiarize yourself with data visualization tools such as d3.js, ggplot, and matplotlib. However, you must ensure that you are not only familiar with the tools used to visualize data alone. Get to understand the principles behind information communication and visually encoding data.
6. Data wrangling
When you visualize data, it is most likely going to appear messy and impossible to work with. This is why you must understand how to handle the imperfections in data. Some of these data imperfections could be inconsistent string formatting (such as New York vs. NY vs. new york), inconsistent date formatting (such as 01-01-17 vs. 2017-01-01), and missing values.
This is even more important if you work at a small company and you are one of the first data scientists hired, or you work at a data-driven company where the product does not relate to data (mainly because the company grew quickly without paying much for data cleanliness). This skill is an important one for all data scientists to possess.
7. Data intuition
Every company wants to ensure that you are a problem solver that’s data-driven before they employ you. During your interview, it is likely that your interviewer asks you about high-level issues. For instance, they may ask you about a data-driven product that they want to develop or a test they are planning to run. You must think about the essential things and those that are not important. As a data scientist, how would you interact with product managers and engineers? What are the methods that you would use? When does it make sense to make approximations? These are things that you must know.
8. Software engineering
A background in software engineering may not always be necessary, especially if you are interviewing or working at a large company. However, if you are job-seeking or work at a small company (especially if you are one of their first hires in data science), it would be essential for you and the company to have a solid background in software engineering. You are going to be responsible for plenty of data logging and may also be in charge of developing data-driven products. So, you need this skill.
Conclusion
Analytics provides data insights that companies need to make the best decisions and create high-quality products. However, if the team is unable to understand the data, then it is all a waste. Having some basic knowledge in data science for all employees is therefore essential. However, as a data scientist, there are specific skills that you must learn to put yourself in a better position within an organization. These skills may also come in for non-data science employees. This article discusses eight of these essential data science skills for all employees.
Author Bio
Jessica Chapman is a writing editor at Aussiessays. She’s from Chicago and is into sports, politics, and traveling.
