To understand the impact of data science and data scientists, let us evaluate the situation of the 90s when Java was introduced and every business wanted to hire experts who were proficient in Java. However, the existing Java professionals had to upgrade their skills to keep pace with changing business needs.
Similarly, data science is one such emerging technology that will redefine the global culture and economy. Data scientists are considered to be data wranglers having a high skill set. If you are an aspiring data scientist or working as a data science professional you should be able to perform multiple tasks based on big data and analytics that require technical and non-technical skill sets. Let us discuss these skills in detail.
Top technical and non-technical data scientist skills
Math
As a data scientist, you will require a strong foundation in the following math concepts.
- Statistics: As a data science professional you are required to know about terms like mean, median, mode, and standard deviations.
- Probability: Topics like standard errors, probability distribution functions, and central limit theorem will come in handy as a data scientist.
Data visualization
This refers to skills for visual presentation of data that helps to:
- Explain your tools and sell your models as a data scientist.
- It is helpful while communicating with non-technical savvies.
Programming
This is the most vital programming language that is used to achieve different business goals.
- Python: is considered to be a golden standard data science skill set that will be widely used in 2022. In 2018, over 66 percent of data scientists reported using this programming language. Python is easy to deploy in websites or applications, and appears with an active community of data science.
- R: This is an open-source language used for statistical evaluation that has tools for communicating and presenting data-driven results. It is designed to work with large data at all stages of the data evaluation process.
Analytical tools
The following tools are used to extract meaningful insights from data and offer frameworks for processing big data.
- SQL: is an important data scientist skill that allows querying, store and manipulate data in relational database management systems.
- Hadoop: is an open-source software library created by Apache that distributes the processing of big data across a troupe of computing devices. This tool uses its own distributed file system to store and stream large data sets to user-based applications like MapReduce, to take care of data analytics.
Machine learning
When an organization handles large data sets, it is pretty obvious that machine learning will be an important part of its operational tasks for the future. In-depth knowledge of ML, however, is not mandatory as a data scientist skill, it is recommended to get acquainted with terms like ensemble methods, random forests, and k-nearest neighbors especially while working on big data.
How do you learn these skills?
You can find data science courses and certifications such as Coursera’s and Senior Data Scientist (SDSTM ) certification program teaching these skillsets. In addition, there are several online books and university courses available that teach these skillsets.
Non-technical data science skill sets hold equal importance and complement technical skills. Let us read through the top soft skills of a data scientist.
Communication
Data science is a jargon-heavy field, as a data scientist you should understand these concepts well enough to communicate them further to the audience, peers, and superiors either in written or verbally.
Tip: Beginners in this field need to engage in work conversations with their team members as much as possible to pick up on communication skills.
Problem-solving
Organizations these days look for people who understand the concept of problem-solving. This is a useful skill for data scientists that showcases how comfortable and capable the candidate is overcoming business obstacles. Data science covers a bunch of problems and data scientists are the ones who demonstrate creativity and flexibility in solving issues.
Tip: It is not always possible to think out of the box, this requires data scientists to invest time to master and acclimatize in data science. As mentioned in the above point, discussion with peers will offer an interesting perspective and edge here.
Risk analysis
You need to analyze risks at the beginning of a project, avoid them proactively, and must be prepared to mitigate the same. Avoiding risks helps to actualize the calculated project results.