From machine learning and statistics to data visualization, deep learning, and computer science, to say that data scientists are expected to know a lot is putting it mildly. With dozens of frameworks, technologies, and languages within these areas, it can be difficult for those looking to get into a career in data science to know exactly where to focus their attention.
Which skills are in-demand by employers and what should aspiring data scientists be focusing on?
- Training and Education – Good data scientists are incredibly educated individuals. A whopping 88-percent have a Master’s degree while 46-percent have a PhD. These degrees are not just for looks either: a strong data scientist must develop the quite a substantial depth of knowledge in order to do their jobs correctly. In addition to degrees, they must also take additional training courses to learn special skills within the subset of data science.
- Data Visualization – The enormous amount of data produced by businesses must be translated into a simplified, easily comprehendible format. Data scientists must be able to visualize this data by using tools like Tableau, ggplot, and many others to help others understand the information.
- Python Coding – Python is the most common coding language required by data science roles. Its versatility allows the language to be used for nearly every step involved in the data science process, can take on various formats of data, and allow for the easy importing of SQL tables into code among many other benefits.
- SQL – Although other technologies have also become a big part of data science, it is still a common requirement for data scientists to be able to write and execute complicated queries within this database. SQL databases not only help with accessing and working on data, but also save time with concise commands while assisting with relational databases.
- Unstructured Data – Unstructured data is content that does not fit into traditional database tables, such as blog posts, customer reviews, video feed, social posts, and so on. Sorting of these types of data can be rather challenging as they are not the most streamlined formats. Data scientists must have the skills to understand and manipulate this data no matter the platform.
- Artificial Intelligence and Machine Learning – Although most data scientists do not yet have a lot of experience in the worlds of AI and machine learning, it has become an important and growing requirement for professionals in the field. These skills can help data scientists solve problems based on the predictions put out by these technologies.
- Apache Spark – As the most popular big data technology, Apache Spark is a big data computation framework that caches its computations in memory to help make it faster than other solutions (such as Hadoop). This tech was designed for the field of data science to help algorithms run faster and handle large, complex unstructured data sets. Apache Spark also allows data scientists to prevent data loss.
The world of data science is constantly expanding and new technologies are continually being released, so this list of must-have skills will always be evolving along with the times. However, these seven skills are must-haves for the profession as it stands now.
Are you a data scientist? Which skills have you found to be the most in-demand or helpful in the profession? Let us know in the comments below.