Data Scientist:

The Data Scientist is accountable for analyzing and compiling massive data sets — both unstructured and structured. These positions combine math and statistics with computing skills to understand extensive data and then apply the data to develop business solutions.

Data Scientists collect, process, and analyze data using everything from industry trends to technology to create practical strategies. Additionally, they ensure that the data is thoroughly cleansed, verified, accurate and complete.

Here are 8 skills every data scientist should have:

Math and Statistics:

Data Scientists need a solid understanding of both math and statistics. A Data Scientist will be an asset for any business, especially those that focus on data-driven decisions since they understand how to use maximum likelihood estimation methods, distribution models, and statistical tests to assist in recommendation and decision-making. Linear algebra and calculus are crucial to machine learning algorithms because they both relate to the process.  

Analytics and Modeling:

Since data is only as good as the people performing analytics and modeling on it, a skilled Data Scientist should have a high level of proficiency in this area. Data Scientists should utilize critical thinking and communication skills to analyze data, run tests, and build models to gather new insights and predict outcomes. 

Machine Learning Methods:

While expert knowledge in this field isn’t always necessary, some degree of familiarity is expected. Machine learning enables various capabilities, including decision trees, logistic regression, and more, and employers will look for these skills.

Programming:

For Data scientists to move from the theoretical realm to creating practical applications, they need strong programming skills. Almost all companies will expect you to know Python, R, as well as other programming languages. Among the subjects that fall under this umbrella are Object-Oriented Programming, basic syntax and functions, flow control statements, libraries, and documentation. 

Data Visualization:

Being a Data Scientist requires you to effectively communicate key messages and get buy-in for proposed solutions using visual data analysis. Having a solid grasp of breaking complex data into smaller, digestible pieces and using visual things (charts, graphs, and more) is a must-have skill for any Data Scientist hoping to advance professionally. 

Intellectual Curiosity:

As a data scientist, you must be passionate about solving problems and finding solutions – especially when it comes to ones requiring a bit of creativity. Data alone does not mean much, and a good Data Scientist is fueled by the desire to know more about what data can tell them and how it can be utilized to improve the quality of life on a broader scale.

Communication:

Data doesn’t communicate without someone manipulating it, so a Data Scientist needs to be an effective communicator. No matter what your project is, communication is key to its success, whether you are disseminating to your team the steps you want to take to get from A to B with the data or presenting to business management.

Entrepreneurial skills:

It takes business acumen for a Data Scientist to effectively use data in a way that’s meaningful to their employer. You must understand the key objectives and goals of the business and how they affect your work. Furthermore, you should be able to develop solutions that meet those goals in a way that’s cost-effective, easy to implement, and ensures broad adoption.

https://www.suryasys.com/product-teams-and-science-a-brief-primer/