The basic job description of a Data Scientist has for a while featured skillsets in R, Python, SQL, and Machine Learning. With the field rapidly developing, these core abilities are not, at this point enough to stay relevant in the data science industry.
The Data Science industry is very competitive, and data science experts are rapidly learning and improving their skillsets and experience.
This has given great importance to the fast-growing job description of the Machine Learning Engineer, and hence, my guidance for 2020 is that each and every Data Scientist should be developers also.
To stay relevant and competitive, make sure to get yourself prepared for new approaches and strategies of working that accompany new data science technologies and tools.
Agile is a technique for oganizing work that is much utilized by development teams. Data Science jobs are filled increasingly more by individuals who’s unique range of abilities is pure software development, and this enables increased importance to the role of a Machine Learning Engineer.
To an ever-increasing extent, Data Scientists/Machine Learning Engineers are overseen as developers: steady creating enhancements to Machine Learning tools in an existing codebase.
For this kind of job, Data Scientists need to know the Agile method of working based on the Scrum method. It characterizes a few roles for various individuals, and this job definition ensures that consistent improvement can be actualized easily.
Git and Github are programmes for programmers that are of extraordinary assistance while managing various versions of software. They track all progressions that are made to a code base, and also, they include genuine straightforwardness in joint effort when various developers make changes to the same project simultaneously.
With the role of Data Scientist turning out to be more dev-hefty, it is vital to be able to make use of those dev tools. Git is fast becoming a very key job requirement, and it takes some time to get used to the best practices for utilizin Git. Git is easier to use when you are working solely or when your team are new. but you might struggle more than you anticipate if you are a begginner and you are joining a team of Git experts.
Something that is also evolving in data science is the way data scientists think about projects. The Data Scientist is no doubt still the one who uses machine learning to answer business questions, as it has always been. But then, Data Science projects are increasingly being created for production systems, for instance, as a micro-service in a bigger software.
Simultaneously, the more advanced models are becoming increasingly CPU and RAM intensive to run, particularly when working with Neural Networks and Deep Learning.
As far as Data Scientist job description is concerned, it is getting more critical to not just consider the precision of your model, but additionlly consider the speed of execution or other industrialization components of your project.
4: Cloud and Big Data
While industrialization of Machine Learning is turning into a more genuine imperative for Data Scientists, it has additionally become a genuine limitation for Data Engineers and IT by and large.
Where the Data Scientist can take a shot at cutting down the time required by a model, the IT team can contribute by changing to quicker compute services that are by and large acquired in either of the following:
Cloud: moving compute assets to 3rd party vendors like AWS, Microsoft Azure, or Google Cloud makes it simple to set up a fast Machine Learning ecosystem that can be accessed remotely. This requires a Data Scientists to have an essential understanding of Cloud technology, for instance: working with remote servers instead of a local computer or working on Linux rather than Windows or Mac.
Big Data: a second part of faster IT is utilizing Hadoop and Spark, which are tools that makes it possible for the synchronization of processes on numerous computers simultaneously. This requires the data scientist to be able to utilize a different method to run models since their codes must allow parallel execution.
5: NLP, Neural Networks, and Deep Learning
As of late, it has still been acknowledged for a Data Scientist to consider that NLP and image recognition as simple specializations of Data Science that not all need to be good at.
Be that as it may, the use cases for image classification and NLP get increasingly more successive even in ‘ordinary’ business. Today, it has become a requirement that a data scientist must hve at least a basic knowledge of these kinds of models.
Regardless of whether you don’t have direct applications of such models in your job, a hands-on project is anything but difficult to obtain and will enable you to understand the means required in image and text projects.