In order to stay competitive, be prepared for new ways of working with new tools! Here are the five skills necessary for the data science job market in 2020.
1.Agile development
Agile development is a method of organizing work that has been heavily used by development teams. More and more people are playing the role of data scientist. Their initial skills are pure software development, which has given rise to the role of machine learning engineer.
Post-its and agile development seem to go hand in hand
More and more data scientists / machine learning engineers are divided into developers: their job is to continually improve machine learning-related content in existing code bases.
For these roles, the data scientist must understand how to work agilely based on the Scrum approach. It defines different roles for different people, and this role definition guarantees the smooth implementation of work and continuous improvement.
2.Github
Git and Github are software for developers and can be very helpful when managing different versions of software. They track all changes made to the code base, and they really make collaboration easy when multiple developers make changes to the same project at the same time.
GitHub is a good choice
As the role of a data scientist becomes more important, being able to use these development tools proficiently is also one of the necessary skills. Git is becoming an essential skill when looking for a job, and it takes time to become proficient with Git. When you are alone or your colleague is a novice, it is easy to start researching Git, but when you join a Git expert team and only you are a novice, you may experience more than you think More effort can keep up.
Git is a skill you must master
3. Industrialization
In the field of data science, the way we think about projects is also changing. What hasn't changed is that data scientists still use machine learning to answer business questions. However, over time, data science projects have increasingly been developed for production systems, such as microservices in large software
AWS Cloud Vendor
At the same time, the CPU and RAM consumption of advanced models is also increasing, especially when using neural networks and deep learning.
As far as the work of data scientists is concerned, not only the accuracy of the model, but also the execution time of the project or other industrial aspects, the latter is becoming increasingly important.
4. Cloud and Big Data
In the industrialization of machine learning, the constraints on data scientists are becoming more and more serious. At the same time, it has become a serious constraint on data engineers and even the entire IT industry.
A famous cartoon (Source: https://www.cyberciti.biz/humor/dad-what-are-clouds-made-of-in-it/)
Where data scientists can work to reduce the time required for a model, it staff can contribute by changing computing services, which are typically obtained in one or both of the following ways:
Cloud: Moving computing resources to an external vendor, such as AWS, Microsoft Azure, or Google Cloud, makes it easy to build a machine learning environment that can be quickly and remotely accessed. This requires data scientists to have a basic understanding of cloud capabilities, such as using a remote server instead of their own computer, or using Linux instead of Windows / Mac.
PySpark is writing Python code for parallel (big data) systems
Big data: It uses Hadoop and Spark, two tools that allow tasks (work nodes) to be processed in parallel on many computers simultaneously. This requires data scientists to implement models in different ways, because the code must allow parallel execution.
5.NLP, neural networks and deep learning
Currently, data scientists still consider NLP and image recognition to be just data science expertise, not everyone has to master it.
You need to understand deep learning: machine learning based on human brain thinking
However, use cases for image classification and NLP are becoming more frequent, even in "regular" businesses. In the current situation, there is no way to adapt to the current technological environment without a basic understanding of such technologies.
Even if you don't have a direct application of such a model in your work, practical projects are easy to find. These items allow you to understand the basic steps of image and text items.