It‘s no secret that data scientists are one of the most in-demand professions in the world. In fact, Glassdoor ranked it as one of the best jobs in the world in 2020. What’s more, the median base salary for data scientists is $107,801.
If you are looking for a lucrative career that offers a lot of growth opportunities, data science should be on your radar. From government organizations to the biggest companies in the world, everyone is looking to leverage Big Data.
In this post, we’ll talk about what you need to become successful as a data scientist in 2022.
A Quick Guide: Which Skills Do Data Scientists Need?
Data science is a field that combines statistics, mathematics, computer science, and Machine Learning to derive patterns and comprehensible results from sample data. As a result, you need to have a good background in technical skills.
But that’s not all.
You need some non-technical skills too if you want to excel in the profession and stay ahead of the competition. In this section, we’ll discuss both the technical and non-technical skills you need to become a data scientist.
Technical Skills
Whether you pursue a master’s degree in Data Science or opt for online learning, these are the technical skills you must learn to get an entry-level job as a data scientist.
1. Programming Languages
As a data scientist, you need to be able to make sense of unstructured data. For this, you need to learn programming languages. Here are the ones that you should consider learning to become a more skilled data scientist:
- R: This popular programming language has a steep learning curve. However, it has great capabilities for statistical computing and data visualization.
- Python: Phenomenal scalability and shorter learning curve compared to other programming languages makes Python a must-have skill for data scientists.
- SQL: Structured Query Language or SQL deals with relational databases. It can help you with data manipulation and data mining.
- Scala: From web programming to Machine Learning, Scala has many applications. It is particularly effective in handling Big Data.
- Julia: This programming language is specifically developed for speedy numerical analysis and computational science.
- JavaScript: This programming language can be easily scaled for larger applications. It can help you in handling real-time data, asynchronous tasks, and in improving visualizations.
2. Tools and Frameworks
Along with learning programming languages, you also need to learn some special tools and frameworks to become a data scientist. Understanding them gives you a better idea of how you can extract important information from any complex data set.
You can also acquire certifications in some of these frameworks to establish your credibility and expertise. If you want to become a data scientist, here are some tools and frameworks you should learn:
- MATLAB: MATLAB is helpful to organize and clean complex data sets. It is also great for matrix calculations.
- Apache Airflow: Businesses adopt this open-source tool for workflow management and simplifying ML pipelines and ETL processes. Google, Slack, and other big companies in the tech industry use this tool in their day-to-day operations.
- SAS: This statistical software program is used for data management, business intelligence, multivariate analysis, predictive analytics, and more.
- Hadoop: This is an open-source software framework that can help you in processing large data sets. Because of its scalability and fault tolerance, this framework is used widely in data analysis.
- Apache Spark: This open-source processing system is used by data scientists to tackle large data workloads. The main benefits of Apache Spark that make it attractive for data scientists include, scalability, speed, and user-friendliness.
- Apache Hive: Built on Apache Hadoop, this data warehouse software program is great for query, analysis, and data summarization.
- Apache Pig: This platform is useful for making programs that can run via Apache Hadoop. As a data scientist, it can enable you to write complex data transformations even if you don’t know Java.
Non-Technical Skills
Now that we’ve discussed the main technical skills that you need to become a data scientist, let’s take a look at the non-technical aspects.
1. Strong Communication Skills
As a data scientist, you will have access to complex data sets. Your job is to simplify them, analyze them, and make sense of the patterns from it. Whatever your technical findings are, you’ll be required to share them with the sales and marketing teams of your organization. For this, you will need strong communication skills.
You will communicate your findings in a way that they easily understand the key points without the need to get into the technical aspects.
In addition to this, you may also be required to create a strong storyline around the data you find. That’s where it also helps to have a background of data storytelling and visualization.
2. Collaborative Skills
Data scientists can’t work on their own. The nature of the job is such that you may have to work in groups with product managers, strategists, and even C-level executives to create data-driven strategies.
In some roles, you may even have to directly work with the customers. That’s why it is important that you have the skills to work with others efficiently and carry on the teamwork.
3. Business Acumen
If you want to succeed as a data scientist, you also need a strong business acumen. It will help you discern important problems that need to be solved in an organization. This skill can also give an edge over your competitors and find better business opportunities.
4. Curiosity
Data science is a dynamic field that is constantly evolving. To stay on top of your game, you need to be curious about the latest tools and technologies in your field.
Darshan Somahekar, who runs classic puzzle gaming site Im-a-puzzle, explains that non-technical skills are just as important as technical ones. We have over 100,000 solitaire games played daily on our platform where we track every metric possible, and you can slice and dice our data in many ways.
One of our best data scientists was able to shed light on what types of users most return and why, which was game changing for our business.
Technical skills can be learned, but having the ability to ask the right questions, run the analysis, and properly communicate insights is what distinguishes great data scientists.
Conclusion
Becoming a data scientist can open up a world of opportunities. If you aspire to be one, make sure you invest time in acquiring the required technical and non-technical skills.
Along with learning programming languages and the latest tools, it’s also important to have strong communication and collaborative skills. Strong business acumen and intellectual curiosity set the best data scientists apart from the rest of the lot.
Do you have any questions about becoming a data scientist? Please feel free to mention them in the comments section.