Can I switch my career to data science?

1. GuidelineschangetoDataSciencefor “Areyouatastageinyourlifewhereyou arecontemplatingaswitchtodatascience ornot? Severaldataanalysts/scientistscometothis lineandget stuck,often overthinking about a mid-career change to data scientist. We are to tell our audienceaboutthesmoothtransitiontothebrightersideofDataScience. Sincethe explosionof data and the ever-growing need for troubleless management and utilization of data has massively become imperative. The data industry has seen a steadyincrease in the needfor moreand more dataanalysts/scientistsadaptedtothehassle-freestorageandmanagement oflargedata.Foratransitionfromasoftwareengineertoadatascientist, we are certain that our audience must have severalquestions aboutthe switch over to the side of data science and how can this be achieved with the least possibletroubleorglitches. With this article, we hope to solve all the troubles and questions the operator mayhaveabouttheimplicationofdatascienceinone’sprofessionalcareer andthemid-careerchangetodatascientist. 1. What does it take to become a Data Scientist? Data Scienceis not atrademasteredby all. It is anamalgamationof multiple skills, reasoning, and preferences. The big question How to become a data scientist?’has haunted thousands of skilled professionals for many years whichentailsapathoflearning,planning&consistency.So,thereal

2. question to which a beginner should find the answer is “Should I become a Data Scientist?”ornot. So to find an answer to this dilemma, sit down and ask yourselfsomeofthesebasicrevealingquestions:–  ‘DoIenjoybignumbercrunching&rationalproblem-solving scenarios?’   ‘DoIrelishworkingwithhandlingunstructuredissues?’   ‘ Do Iappreciate deep researchand spend hours operating on data?’   ‘Do I like constructing and representing evidence-based stories?’   ‘Do I consistently seize to questioning, people’s assumptions andam alwaysinquisitiveto know‘Why”?’   ‘DoIlovetheproblem-solvingaspectofajobandflourish onintellectual challenges?’  Thein-depthpenetrationintothesequestionswouldhelptheuser to understand the need to change over to Data Science as a professional calling orNOT.

3. 2. ProspectsofDataScienceinthefieldof thedataIndustry • Alotofprofessionalsenteringthedataindustryoftentendtogetconfused by the choices of job profiles that the data industry has to offer and often confusea certain job with the definitivejobdescription of a data scientist. The data industry is an amalgamationof severaldifferent kinds of job profiles, each with their defined job descriptions, because of which it becomes easyforbeginnersenteringthisvastdomainofthedataindustrytogetlost inthecrowdandmissoutontheonce-in-a-lifetimeopportunity. • Thefollowingtopicprovidesabriefonthejobdescriptionof adatascientistinthedataindustry:- • Chart out thelearningmodule: • Planningoutthe learningjourneyisthe nextstepforevolvingand transforming into a Data Scientist and sticking to the path is all the more important. Often many aspiring individuals tend to getstuck in this phase andoftenareunabletomoveaheadordeviatefromthepathaltogether. • Do youneedaPh.D.tobecomeaDataScientist? • To get a better understanding of this concept, let us segregate the major job rolesofa DataScientist:– • Applied datasciencerole • Researchrole • The Applied Data Science role is fundamentally about working with pre- existing algorithms and understanding their implications in the normal world. In simpler words, it is the art of employing these distinct algorithms in your workprofile.Whichwefirmlybelieveno onewouldrequirea Ph.D. • Themajorityofthedatascienceprofessionalsthatyouwouldmeetinthe dataindustryfieldwouldfitintheapplieddata sciencecategory.

4. But If the user wishes to move ahead in the research role then Ph.D. would become a requirement. Fabricating research-based algorithms from the base up, writing a scientific thesis, etcare a perfect fitfor thejobrole of a research data scientist. It also helps if the Ph.D. is in a similar field as the job profile that the user is pursuing. E.g., a Ph.D. course in linguistics will be extremelyhelpfulforasuccessfulcareerinNaturallanguageprocessing. IsData Sciencecertificationanecessity? Thereareseveraldistinctmeansofreachingthedestinationthattheuser seeks to reach. But Yes, a certification in Data Science goes a long way the skills that the user may have acquired are because of the certification course andconstantpracticeandnotbecauseofthecertificateitself. Overthedecademultiplecourseshavebeenputuppromisingacertification intheartofDataScience,buthavingacertificatedoesnotguaranteeajob as aDataScientist. Recruiters look into a lot more than just the certificate which can be earned bysometimesjustglidingthroughthecourse.Recruiterswhileinterviewing pay special attention to the projects worked on by the user and the skillsets employed whilst completing the projects. In the end, the showdown happens at the time of the interview where the recruiter can come at the user from anyanglepossibleandquerytheuseronhow aparticulartaskwas accomplished while working on the project and how a certain skillset was utilized to acquire the desired result. So, make certain to practice multiple topic-related projects while going through the course to capture and gain clarityontheconceptsbeingemployedintheproject. MasterProgramming: Several languages may be utilized or the user might have to use them while workinginthejobprofileofaDataScientist,thusitishighlyrecommended to master at least one programming language which is the most popular and maybe employedindifferentscenariosofdevelopment. The most versatile language extremely popular with developers is the coding languageofPython.Itisanextremelybeginner-friendlylanguagethatshould

5. bemasteredbytheuser toemployitsoperationsalongwithitsbasic machine-learning libraries like Pandas, NumPy, and SciKit Learn. The user should be well-versed and confident in writing custom functions, generators, etc. Even though the user might not be able to optimize the fabricated code, the user however should be able to transform well-thought operations into coding. • Masterthegrammar of DataScience: • Statistics are by many profound data scientists and analysts is considered to be the grammar of data science. Statistics is the basics of acing the interview foradata sciencejob. • The user though may not require to have a statistical background but should be well versed in statistics topics related to data science as it is one of the primerequirementsofbeinga datascientist.Someofthetopicsinclude:– • DescriptiveAnalytics(median, mode,variance) • InferentialAnalytics(z-test,t-test,hypothesistesting) • Statistical Analytics(forecasting,logisticregression) • These aresome of the basic statistical tools that the user might have to masterandshouldnottakemuchtimetomasterregardingtheusercan findtheappropriate resources. • HacktheHackathons: • Data Scienceisallaboutpracticalinstinctsratherthantheoretical understanding. The user needs to have a knack for being able to choose the best algorithm or the best data cleaning methods. One look at the data and the user should be able to figure out the way to manage the data irrespective ofthe factwhethertheuser possessesan in-depthknowledgeofthe algorithms implied or not and the only place where the user might be able to honetheseskillsisHackathon. • Data Science Hackathons are the best stepping stone in the user’s path to perfection in the field of data science. The user may practice the skills on a dataset and win prizes whilst showcasing their skills to the world. These hackathoneventsandcompetitionshavegainedmorepopularityinthelast

6. few years as several aspiring professionals wish to take a bite of the data sciencecake.Takingpartintheseeventsmayalsobecomeapartofthe user’s portfolio and in turn, increase the weightage of one’s curriculum vitae. This can be achieved via online platforms like Make Kaggle, HackerEarth, Dare2Compete, etc. Polishsoftskills: Itwouldbecompletelyinappropriateto assumethatjustfabricatinga module to analyze and predict the future of the business would be enough to become a world-class Data Scientist. Several other soft skills surround and go a longway inthis domainthatneedsto bepolishedforthe effective fabrication of an analysis model and its implication and employment across multipledepartments. Let’s takealookatsomeof thesesoftskills: –  Communicationskills  Itcannotbestressedenoughtheimportanceofthisskillinthejobprofileof a data scientist.Effective communicationof theinsights being fabricated from the user’s model to the stakeholders is of utmost imperative. No matter howgood amodelitwillneverbeabletocommunicatetheinsights generatedbyittothenon-technicalmanagementwhoaredirectlyinvolved in thefirm’sdecision-makingprocedure.  StorytellingSkills  Thewaytheuser communicatesthe insightsgeneratedbythe model determines the user’s ability as a Data Scientist. One such example is the way the user can show the rise in the sale of box office cinema hall tickets on weekends over weekdays which is the business’s highest revenue-generating night.

7.  Systematicthinking  Systematic brainstorming of the issues that might arise from several possible outcomes and looking for a rundown of the imaginable solutions is the most valuable possession of a data scientist. It allows the data scientist to factor in the different factors influencing the data and look into it objectively from severalpointsofview.  BeingCurious  As a Data Scientist, the user needs to be curious at all times. Curious about which algorithm, which issue, the final objective from a certain point of view, etc. This curiosity will help the user to understand the matter at hand in a muchmoredetailedwayandfabricatethemodelaccordingly. A successful transition to Data Science is an upcoming trend in the data industry and does not seem to be going anywhere anytime soon. The mastery of this skill is certain of creating a very vast and global impact on the data managementindustry.Theskilledprofessionalswillbehandsomely compensatedfortheirmasteryofthisskillbasedon experienceand knowledge. Already beingtermedas the“21stcentury’s sexiest job” this profile has gained accolades across several industries and the demand for professionals who are good at this is increasing more than ever before. So, we firmly believe that in the coming 2 -3 decades the job profile of a data scientistisheretostay. Someusefullinks are Below: To know moreabout our DataScience certification Course visit – Data sciencecertification Toknowmoreabout our DataSciencevisit-DataScienceGuide mustvisitour officialyoutubechanneltoknow more aboutdataanalytics& data scienceand manymorevisit-AnalyticsTraining hub