How to become a more sought-after specialist in the field of Data Science in 2019
The title of this article may seem a little strange. Indeed: if you work in the field of Data Science in 2019, you are already in demand. Demand for specialists in this field is growing steadily: at the time of this writing, 144.527 vacancies with the keyword “Data Science” were posted on LinkedIn.
Nevertheless, to follow the latest news and trends in the industry is definitely worth it. To help you do this, the CV Compiler team and I analyzed several hundred Data Science jobs in June 2019 and determined what skills employers expect most from candidates.
This graph shows the skills that employers most often mention in Data Science jobs in 2019:
We analyzed approximately 300 jobs with StackOverflow, AngelList, and related resources. Some terms could be repeated more than once within the same vacancy.
Important: This rating demonstrates the preferences of employers rather than specialists in the field of Data Science.
Obviously, Data Science is not primarily frameworks and libraries, but fundamental knowledge. However, some trends and technologies are still worth mentioning.
According to market research of Big Data in 2018 , the use of Big Data in enterprises increased from 17% in 2015 to 59% in 2018. Accordingly, the popularity of tools for working with big data has increased. If you disregard Apache Spark and Hadoop (we'll talk about the latter in more detail), the most popular tools are MapReduce (36) and Redshift (29).
Despite the popularity of Spark and cloud storage, the “era” of Hadoop is not over yet. Therefore, some companies expect candidates to know Apache Pig (30), HBase (32), and similar technologies. HDFS (20) is also found in some jobs.
Given the ubiquitous use of various sensors and mobile devices, as well as the popularity of IoT (18), companies are trying to learn how to process data in real time. Therefore, threading platforms such as Apache Flink (21) are popular with employers.
The preparation of data and the selection of model parameters is an important part of the work of any specialist in the field of Data Science. Therefore, the term Data Mining (128) is quite popular among employers. Some companies also pay attention to Hyperparameter Tuning (21) (a term like Feature Engineering should not be forgotten either ). The selection of optimal parameters for the model is important, because the overall performance of the model depends on the success of this operation.
The ability to correctly process data and display the necessary patterns is important. However, data visualization (55) is an equally important skill. You must be able to present the results of your work in a format that is understandable to any team member or client. In terms of data visualization tools, employers prefer Tableau (54).
In vacancies, we also came across such terms as AWS (86), Docker (36), as well as Kubernetes (24). We can conclude that the general trends from the field of software development have slowly migrated to the field of Data Science.
Carla Gentry
Data Scientist, owner of Analytical Solution
LinkedIn | Twitter
Andrey Burkov,
Director of Machine Learning at Gartner,
author of the Hundred-Page Machine Learning Book .
LinkedIn
Ganapati Pulipaka,
Chief Data Scientist at Accenture,
Top 50 Tech Leader Award Winner.
LinkedIn | Twitter
Lon Riesberg
Founder / Curator of Data Elixir ,
ex-nasa.
Twitter | LinkedIn
Data Science is a rapidly developing and complex field in which fundamental knowledge is as important as experience with certain tools. We hope this article helps you determine what skills are needed to become a more sought-after specialist in the field of Data Science in 2019. Good luck!
This article was written by the CV Compiler team, a tool for improving resumes for data science and other IT professionals.
Nevertheless, to follow the latest news and trends in the industry is definitely worth it. To help you do this, the CV Compiler team and I analyzed several hundred Data Science jobs in June 2019 and determined what skills employers expect most from candidates.
Most sought after Data Science skills in 2019
This graph shows the skills that employers most often mention in Data Science jobs in 2019:
We analyzed approximately 300 jobs with StackOverflow, AngelList, and related resources. Some terms could be repeated more than once within the same vacancy.
Important: This rating demonstrates the preferences of employers rather than specialists in the field of Data Science.
Key Trends in Data Science
Obviously, Data Science is not primarily frameworks and libraries, but fundamental knowledge. However, some trends and technologies are still worth mentioning.
Big data
According to market research of Big Data in 2018 , the use of Big Data in enterprises increased from 17% in 2015 to 59% in 2018. Accordingly, the popularity of tools for working with big data has increased. If you disregard Apache Spark and Hadoop (we'll talk about the latter in more detail), the most popular tools are MapReduce (36) and Redshift (29).
Hadoop
Despite the popularity of Spark and cloud storage, the “era” of Hadoop is not over yet. Therefore, some companies expect candidates to know Apache Pig (30), HBase (32), and similar technologies. HDFS (20) is also found in some jobs.
Real-time data processing
Given the ubiquitous use of various sensors and mobile devices, as well as the popularity of IoT (18), companies are trying to learn how to process data in real time. Therefore, threading platforms such as Apache Flink (21) are popular with employers.
Feature Engineering and Hyperparameter Tuning
The preparation of data and the selection of model parameters is an important part of the work of any specialist in the field of Data Science. Therefore, the term Data Mining (128) is quite popular among employers. Some companies also pay attention to Hyperparameter Tuning (21) (a term like Feature Engineering should not be forgotten either ). The selection of optimal parameters for the model is important, because the overall performance of the model depends on the success of this operation.
Data visualization
The ability to correctly process data and display the necessary patterns is important. However, data visualization (55) is an equally important skill. You must be able to present the results of your work in a format that is understandable to any team member or client. In terms of data visualization tools, employers prefer Tableau (54).
General trends
In vacancies, we also came across such terms as AWS (86), Docker (36), as well as Kubernetes (24). We can conclude that the general trends from the field of software development have slowly migrated to the field of Data Science.
Expert opinion
This list of technologies really reflects the real state of things in the world of Data Science. However, there are no less important things than writing code. This is the ability to correctly interpret the results of their work, as well as visualize and present them in an understandable form. It all depends on the audience - if you talk about your achievements to candidates of science, speak their language, but if you present the results to the customer, he will not care about the code - only the result that you have achieved.
Carla Gentry
Data Scientist, owner of Analytical Solution
LinkedIn | Twitter
This graph shows current trends in the field of Data Science, but it is rather difficult to predict the future based on it. I am inclined to believe that the popularity of R will decline (like the popularity of MATLAB), while the popularity of Python will only grow. Hadoop and Big Data also appeared on the list by inertia: Hadoop will disappear soon (no one is seriously investing in this technology anymore), and Big Data has ceased to be an increasing trend. The future of Scala is not entirely clear: Google officially supports Kotlin, which is much easier to learn. I am also skeptical about the future of TensorFlow: the scientific community prefers PyTorch, and the influence of the scientific community in the field of Data Science is much higher than in all other areas. (This is my personal opinion, which may not coincide with the opinion of Gartner).
Andrey Burkov,
Director of Machine Learning at Gartner,
author of the Hundred-Page Machine Learning Book .
PyTorch is the driving force behind reinforced learning, as well as a strong framework for parallel code execution on multiple GPUs (which cannot be said about TensorFlow). PyTorch also helps build dynamic graphs that are effective when working with recurrent neural networks. TensorFlow operates with static graphs and is more difficult to learn, but it is used by more developers and researchers. However, PyTorch is closer to Python in terms of debugging code and libraries for data visualization (matplotlib, seaborn). Most Python code debugging tools can be used to debug PyTorch code. TensorFlow also has its own debugging tool - tfdbg.
Ganapati Pulipaka,
Chief Data Scientist at Accenture,
Top 50 Tech Leader Award Winner.
LinkedIn | Twitter
In my opinion, work and career in Data Science are not the same thing. To work, you will need the above set of skills, but to build a successful career in Data Science, the most important skill is the ability to learn. Data Science is a fickle field, and you will have to learn to master new technologies, tools and approaches in order to keep pace with the times. Constantly set yourself new challenges and try not to “be content with little.”
Lon Riesberg
Founder / Curator of Data Elixir ,
ex-nasa.
Twitter | LinkedIn
Data Science is a rapidly developing and complex field in which fundamental knowledge is as important as experience with certain tools. We hope this article helps you determine what skills are needed to become a more sought-after specialist in the field of Data Science in 2019. Good luck!
This article was written by the CV Compiler team, a tool for improving resumes for data science and other IT professionals.
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