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Entrevista de Manager, Data Science

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naviHealth

A working problem to see how I would approach a data science project including project management, MVP, data wrangling, EDA, model choices, and interpretation goals.

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Project Management for Data Science can vary depending on your project, but the DS standard project Phases are outlined below: Data Analytics/Data Science project phases • Business Understanding or Problem definition • Data Interpretation • Data Preparation • Modeling • Evaluation • Deployment Minimum viable product (MVP) – is a version of a product with just enough features to satisfy early customers adoption and provide feedback for future product enhancements or development efforts. Gathering insights from an MVP is often less expensive than developing a product with more features, which increases costs and risk if the product fails, for example, due to incorrect assumptions. It may also involve carrying out market analysis beforehand. When using Agile Following these steps will help you identify and prioritize features, as well as help you confidently outline what you need to get your MVP to market. Step 1: Identify and Understand your Business and Market Needs, goals and objectives/KPIs Step 2: Map Out User Journey(s) ... Step 3: Create a pain and gain map. ... Step 4: Decide What Features to Build. Exploratory data analysis (EDA) – In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey by to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analytis (IDA) which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA. Data Wrangling (Data Preparation Steps) • Raw Data – Define, Locate, Extract • Structure Data – Data transformation, Cleaning and pull into a cube, database, excel spreadsheet or some other storage format. • Data Preprocessing - Data aggregation, data preprocessing prepares data for EDA. • Exploration Data Analysis (EDA) – Modeling, training data • Insight, Reports, Dashboards, Visual Graphics – pulling data into a visual

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