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Ever feel lost in the world of data titles? Don't worry, we've got you covered! This presentation dives into the key roles that make up the data dream team: Data Scientist: The architect of the information age, building models to unlock hidden knowledge. Data Analyst: The business detective, transforming data into actionable insights. Data Engineer: The plumber of the data flow, ensuring clean and accessible information. ️ Business Analyst: The bridge builder, connecting business needs with data-driven solutions. (Bonus) Machine Learning Engineer: The builder of the AI revolution, turning models into real-world applications. Join us to understand how these data dream team players work together to unlock the power of information! @DataScience #Careers #DemystifyingData

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We're hiring data scientist & data analyst! 👉 https://lnkd.in/gup7Zx-a In semiconductor device engineering, there frequently being an "Issue Zero" distracts us from the real root-cause finding, which is caused by the biased problem statement and insufficient methodology. Seduce people taking the comfortable answer quickly and consequently trapping into the trade-off loop. Consume tremendous effects. The best way to escape this loop is equipping with a quality mindset to collect all necessary background information, then bypass the stereotype bias forged by the limited experience & knowledge. What we need is a neutral methodology to dig out the valuable datasets for storytelling, not blind guessing. Based on the initial data to collect more relevant information and filter the non-POR to define good/bad, and subsequently run the data analytic matrix to identify key factors. Let's work smart with Data Analytics × Matrix Thinking to avoid the trade-off loop at first. Join us to work on the interesting topics. #Mindset #DataAnalytics #DataScience #DataAnalyst #DataScientist #BigData #AI #ArtificialIntelligence #Python #Programming #MatrixThinking #Methodology #DataTalk #Semiconductor #GaN #Power #IC #Loop #EscapeLoop #EDA #ABTesting #Technology #Strategy #Discipline #SmartDesign #TradeOff #Issue #SixSigma #RootCause #Foundry #Database

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There is a pressing need to clearly outline job responsibilities for data scientists, as employers frequently confuse them with data engineers, software engineers, business analysts, or ML Ops engineers. #DataScience #JobRoles #DataScientist #DataEngineer #BusinessAnalyst #MLEngineer #TechJobs #CareerDevelopment #TechRoles #DataAnalytics

Fun fact: I had the title “data scientist” before I knew data science. The year was 2015. The month was December. TensorFlow had just been open-sourced the month prior. Theano was the top dog in tensor processing. I had mainly been solving business problems with Excel, VBA, and SQL up to this point. I was just starting to learn R. I was on the road traveling with a VP of Sales doing “pre-sales engineer stuff”. I was selling a product to insurance companies that I knew I could build but hadn’t been built yet. My official job title was “Senior Business Data Analyst”. But this was a mouthful to introduce myself as in meetings with insurance company executives. One day the VP of Sales turns to me and says, “We need to come up with a better job title for you. You have a PhD so you’re a scientist. And your job is to make money from our data. So I’m going to call you a Data Scientist”. I didn’t think much of it at the time. But a few months later I needed to solve a #computervision problem. I taught myself #python and #machinelearning in order to solve this problem and the rest, as they say, is history. #cv #ml #datascience #data #engineering #science #llms #chatgpt #datascience #datascientist #hiring #leadership #career #dataengineering

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Have you ever wondered who keeps order in the digital world? It's a team effort! Let's break down the fascinating roles of Data Engineers, Analysts, Scientists, and ML Engineers. Data engineers are the plumbers of the data world, building the infrastructure that allows data to flow smoothly. They focus on data collection, storage, and processing. Data analysts are the detectives, use data to uncover trends and answer business questions. They are skilled at data visualization and communication. Data scientists are the scientists who build and test models to extract knowledge from data. They have a strong foundation in statistics and machine learning. Machine learning engineers are the builders, taking the models created by data scientists and turning them into production-ready applications. They focus on scalability and efficiency. Programming knowledge is necessary for all of these roles; however, the specific languages and tools used will differ. While data analysts can focus on SQL and data visualization tools, data engineers must be familiar with big data frameworks. Machine learning libraries are used by both machine learning engineers and data scientists; however, data scientists may also utilize statistical programs like R. The world of data is always changing. Which amazing new roles could we see in the future? Let's talk about it! #innovation #dataskills #futureofwork #mlengineer #dataanalyst #dataengineer #datascientist

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Hey LinkedIn algorithm, I am looking to connect with people: •Data Scientists • Data Analysts •Data Engineers • Data Consultants •Machine Learning Engineers • AI specialists •Database Administrators #CONNECT #JessicaFeto #datascientist #dataanalyst #AI #ML #data