Application of Data Science for Data Scientists | AIML TM

Partner: Udemy
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Description: 1. Introduction to Data ScienceOverview of what Data Science isImportance and applications in various industriesKey components: Data, Algorithms, and InterpretationTools and software commonly used in Data Science (e.g., Python, R)2. Data Science Session Part 2Deeper dive into fundamental conceptsKey algorithms and how they workExploratory Data Analysis (EDA) techniquesPractical exercises: Building first simple models3. Data Science Vs Traditional AnalysisDifferences between traditional statistical analysis and modern Data ScienceAdvantages of using Data Science approachesPractical examples comparing both approaches4. Data Scientist Part 1Role of a Data Scientist: Core skills and responsibilitiesKey techniques a Data Scientist uses (e.g., machine learning, data mining)Introduction to model building and validation5. Data Scientist Part 2Advanced techniques for Data ScientistsWorking with Big Data and cloud computingBuilding predictive models with real-world datasets6. Data Science Process OverviewSteps of the Data Science process: Problem definition, data collection, preprocessingBest practices in the initial phases of a Data Science projectExamples from industry: Setting up successful projects7. Data Science Process Overview Part 2Model building, evaluation, and interpretationDeployment of Data Science models into productionPost-deployment monitoring and iteration8. Data Science in Practice - Case StudyHands-on case study demonstrating the Data Science processProblem-solving with real-world data<p
Category: IT & Software > Other IT & Software > Data Science
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Price: 199.99
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Source: Impact
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