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Data Science Methodology From Understanding to Preparation Data Understanding Case Study : Understanding the Data Descriptive Statistics: - unvariate statistics (unvariate : 분포가 변량이 하나인... 일도량의) - pairwise correlations (pairwise : 쌍으로) - histogram Histograms are a good way to understand: - how values or variables are distributed - what data preparation might be needed to make the variable more u..

Data Science Methodology From Requirements to Collection Data Requirements From Requirements to Collection - Data Requirements : What are data requirements? - Data Collection : What occurs during data collection? Case Study : Selecting the cohort Define and select cohort: (cohot : 집단) - inpatient within health insurance provider's service area - primary diagnosis of CHF (Congestive Heart Failure..

Data Science Methodology From Problem to Approach Data Science Methodology Overview Addressing data science challenges - data science combines statistics, techonology, and domain expertise to extract insights from vast data - adopting a methodology can help address these issues Challenges: - resolve the problems of misunderstanding of the business questions - not knowing how to apply the data to..

Tools for Data Science RStudio IDE Introduction to R and RStudio What is R? - Statistical Programming Language - used for data processing and manipulation - statistical, data analysis, and machine learning - R is used most my academics, healthcare and the government - R supports importing of data from different sources : flat files, databases, web, statistical software (eg. SPSS) R Capabilities ..

Tools for Data Science Jupyter Notebooks and Jupyter Lab Introduction to Jupyter Notebooks Origin of Jupyter Notebook - Jupyter Notbeooks : Originated as "iPython", stands for JUlia PYThon R What is a Jupyter Notebook? - browser-based application - allows to create and share documents containing code, equations, visualizations, narrative text links, ... - records data science experiments - allow..

Tools for Data Science Libraries, APIs, Datasets and Models Libraries for Data Science Introduction - Libraries are a collcection of functions and methods that allow you to perform many actions without writing the code Python Libraries: - Scientific Computing Libraries in Python - Visaulization Libraries in Python - High-Level Machine Learning and Deep Learning - Deep Learning Libraries in Pytho..

Tools for Data Science Languages of Data Science Languages of Data Science Which language should I learn? - wide range of available technical options - different programming languages have their own strengths and weaknesses - language depends on your needs, the problems to solve, and who you're solving them for What problems do you need to solve? - can be related to the company, role, age of exi..

Tools for Data Science Data Science Tools Categories of Data Science Tools Data Science Categories - raw data must pass through Data Science categories : such as Data Management, Data Integration and Transformation, Data Visualization, Model Building, Model Deployment, Model Monitoring and Assessment - to do these tasks, data asset management and code asset management, execution environments, an..