Data Scientist Vs Machine Learning Engineer – Which is Better!

1. DataScientistVsMachineLearningEngineer • WhichisBetter! • The year 2021 has seen an upward surge in the two most popular roles of Data Scientist and Machine Learning engineer in the IT industry. Whether there are somedistinctionsoroverlapping ofthe roles,dependslargely onthe organizations people choose to work with. Each organization defines the roles in theiruniquewayandindividualsneedtoprepare accordingly. • SkillsPreparationforMLEngineerandDataScientist • Considering there is an overlapping of roles and skills for both data scientist and an ML engineer, it is not surprising to read that certain skillsets are common to both. The difference lies in how they apply in those skills in their day-to-day workings. • Implying – You should not only master the skills but should be proficient in applyingthemfordifferentroles. • But before that, understand the major differences in how both data scientists and ML engineersapproachtheirwork. • A data scientist works more on the data models, while an ML engineer’s focusismoreonthedeploymentof thosedatamodels. • A data scientist’s focus is more on understanding the algorithms, whereas an ML engineer will be more concerned on shipping those models into a productionenvironment,whichinteractswiththeusers. • Now that we are aware of the differences in the working styles, let’s have a look attheindividualskillsrequiredforbothdatascientistandML engineer. • Data Scientist–SkillsRequired • The year 2021 has seen an emergence of various new tools and skills for data scientists, however, there are top three tools and skills that are used by most of thedatascientistsinsolvingeverydayqueries.Theyare– • Python/R:Needwesaymoreontheuseofthesetwopopularprogramming languages by data scientists. Most of the practicing data scientists use Python, while some of them use R. R is used more for statistical data and Pythonisquiteuser-friendlyandcompatiblewithother languagesaswell. • JupyterNotebookoranyotherpopularIDE:Mostofthedatascientistsyou willmeetintheinitialstagesofyourcareer,useJupyterNotebook.Reason: It is the central place for coding, writing text, and viewing various outputs includingresultsandvisualizations.WhilethereareotherpopularIDEs

2. like PyCharm, and Atom, Jupyter Notebook has been considered as a go- toIDEfordatascientistsandindustryexpertsfeelthatisunlikelytochange sometimesoon. 3.SQL:OrStructuredQueryLanguageasitisknownpopularly isan essentialtool.Reason:dataisatthehelmofamachinelearningalgorithm, which will finally become a part of the data science model’s data. Data scientistsuseSQLfortheinitialpartoftheirdatascienceprocesssuchas querying the first data along with creating new features. But that’s not all whereSQLisused.SQLisalsousedattheendofthedatascienceprocess when the model is run and deployed, and the results are saved in the organizationaldatabase, whichinturnalso usesSQL. With these three skills and tools mastered, you will be good to go as a successful data scientist. No doubt there are other skills that you will learn as you earn. However,thebaseisbuiltof thesethreeskills– Aprogramminglanguage Avisualizationplatform oranIDE Lastbutnottheleasta queryinglanguage Machine Learning –SkillstoAce! As discussed earlier, the role of an ML engineer comes into play when a data scientist has built the model. Reason: The main purpose of a machine learning engineer is to take a deeper dive into the code and shipping and the process is known as deployment. As an ML engineer you may not have to know the workings of random forest, however, you will be expected to know how to save andloadafile,whichcanbethenpredictedinaproductionenvironment.Tosum it up a machine learning engineer is more focused on software engineering. So some oftheskillsand tools thatyouneedtomasterinclude Python: Yes, it is common for both data scientists and machine learning engineers. However, the similarity ends here. While a machine learning engineer ismoreintoobject-orientedprogramming(OOP)inPython, whereas a data scientist is not concerned with the OOP, as data scientist’s primary job is to build models and concentrate on the statistics involved along with analytics. Machine learning engineers need to be more trained in Python, however, if you are well-versed in Python then you can work bothas a machinelearningengineeranddatascientist. GitHub/Git: This is one of the common tools to store code repositories. Usually, machine learning engineers use Git and GitHub, as it is code managementtoolandplatformthat isessential formachinelearning engineerstomakecodechangesaswellaspullrequests.Occasionally,both

3. datascientistsandmachinelearningengineersarewell-equippedingitand GitHub. 3.Deployment tools: One of the skills where machine learning engineers and data scientists differ is deploying a model. While there are some data scientists who may know how to deploy a model, it is a core function of ML engineers. There are organizations that prefer data scientists to be proficient with both data science and machine learning skills. So, if you knowalltheskills, youhaveplentyof opportunitiesatyourfeet. So, whether it is the role of a data scientist or a machine learning engineer, the above-mentioned skills will help you gain the footage. Yes, don’t forget to upgradeyourlearningskillbygoingforcertificationsfromthereputedinstitutes.