Once you have learned the skills and gotten a job as a data scientist, how do you ensure that you stay on an upward trajectory to becoming an elite data scientist? Everyone’s thoughts are different, but a common consensus is that there are certain skills you need to acquire before you can be referred to as an elite data scientist. One thing to note is that it is impossible to have all the skills listed below, but having a vast majority of them will separate you from other data scientists. Now, let us look at the skills you need!
Ability to Find the Right Data
The results and insights a data scientist gets from data analysis depends on the amount and quality of the data they collect. If a data scientist does not have access to enough or relevant data, their research, insights, and conclusions will suffer.
Data scientists who work for small businesses realize that there are lots of data collection opportunities they have not taken advantage of, especially if they are the first data scientist hired by the business. Once they realize this, they can help businesses find ways to collect new types of data that the business owner might not even have thought about before.
All the above reasons point to why data scientists should not only know where to find data but also which type of data would best serve the purpose for which they need it.
One of the most important skills for an elite data scientist is the ability to understand different businesses and industries, especially the one they are working for. Understanding the business or industry puts them in a better position to understand what problems they are trying to solve which helps them come up with the best decisions and solutions possible.
Data scientists need to understand which problems are critical and would therefore have the biggest positive impact on a business if they are solved. Lastly, data scientists can identify ways a business can make better use of the data it collects for improved outcomes.
To become an elite data scientist, you need to have in-depth knowledge of at least one analytical tool. Many data scientists prefer R because it is built for data science. R can be used to solve many of the analytical problems and challenges data scientists face in data science.
The one challenge with R is that it is very difficult to learn. However, there are a lot of learning resources that can help data scientists master R. And, because it remains in great demand, learning R will be worth the time and effort.
Python remains one of the easiest programming languages to learn or data science roles. Also, it is often required in almost all data science job applications. Python is a great programming language for data science due to its versatility.
Python can be used in many of the steps involved in data analysis and science processes. Numerous Python libraries can help make the work of a data scientist easier while helping them get better results from their analysis.
Python also accepts numerous data types and can import SQL databases directly into your code which makes it easier to work with this data. It also allows data scientists to create different types of datasets.
Continuous Skill Development
Data science changes very fast. If you want to stay on the cutting edge of data science, you need to keep developing and improving your skills. You should stay within the data science community as this is a great way of learning about new tools, techniques, and research, all of which can help you become a better data scientist.
Many data scientists choose to undertake online training to learn the latest skills, innovative thinking, and trends that would make them better at their jobs. A popular option is an online MS in computer science program that focuses on data science. In addition to imparting new knowledge and skills, these online MS in computer science programs teach skills that can help data scientists transition into other fields like machine learning and artificial intelligence.
Machine Learning and Artificial Intelligence
Although the numbers are changing, a lot of data scientists have not yet mastered machine learning and artificial intelligence. If you want to stand out as an elite data scientist, these are two areas that you cannot ignore. Techniques such as reinforcement learning, decision trees, adversarial learning, logistic regression, and more will help you solve problems that you might otherwise not be able to or that would take too long to solve.
Since you cannot escape working with massive amounts of data, these are two skills that you need to stand out.
While a data scientist might understand the results of their data analysis, other people might not. A majority of people need data to be translated into formats they can easily understand. Charts, graphs, and other visual data representation options are better understood than raw data and this is why data visualization is critical.
Data scientists should be able to use the different data visualization tools that are available to present data in formats that everyone can understand. These tools can turn complex data into formats that different stakeholders can find useful.
Also, data visualization is important in establishing the relationship between different sets of data. Visualization can make data that might otherwise seem not useful into useful data that is understood by everyone.
Unstructured data is any type of data that cannot be held in database tables. These include data types like customer reviews, comments, videos, audio, and more. Sorting and making sense of these types of data is very difficult because they are not organized.
There are very few data scientists who work with unstructured data and if you want to join their realm, you will need to learn this skill.
While the number of skills required to become one of the best data scientists can seem long, many of these skills are not strictly required unless a job description asks for them. However, making use of them when working with data can help you stand out and this can have a positive effect on your career and prospects.