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Tags: [[tag:CompositeJD]] These are a composite set of skills extracted from various data analyst job descriptions. The idea is to generate a generic list of basic skills that I have and also ones that I can target. ### Skills/ Experience I have aligned to JD's - MS Excel basics and advanced skills, including knowledge of using VBA - Education: Engineering degree, i.e quantitative or technical background - Engaging with customers and clients, and providing analytical support to solve problems and extracting business requirements + formulating strategies. - Coordinating with technical support services as a customer for various softwares and services. - Experience in dealing with multiple stake holders. - Experience in dealing with cross-functional and multi-national teams. - Experience dealing with multiple products. - [ ] Basic knowledge of building dashboards (Shiny, Tableau, Excel) : examples need to be demonstrated. - Experience with Manual Data collection - Experience with ERP systems (CRM, Sales, Purchase, Manufacturing modules). - [X] Basic Extract Load Transform (ETL) knowledge, especially via ERP. - SQL : including connecting with remote db's (MySQL, Postgres, Mongo) ### Other skills - Knowledge of the Agile/Waterfall process - Comfortable with Linux and CLI knowledge. Ability to handle a VPS. - [ ] Docker : sample project displaying skills. - Docker is not asked very commonly in data scientist profiles. However, it is certainly a strong skill to have as a data scientist. - Numerical Analysis (Simulation driven design approaches like CFD/CAE) - General scripting in python/bash/R/perl etc. ### Skills at various stages of completion The details will be fleshed out soon. - TODO Better grip on fundamental statistics - TODO Improve familiarity with Excel - TODO Tableau : data viz/BI software (Examples of expertise to be formulated) - TODO Intermediate SQL (Fluency + projects) [0/2] - TODO Fluency in Data Cleaning with Python [/] - TODO Fluency in ML with Python [0/1] - TODO Unit testing approach in code - TODO End to End project example with R (Data cleaning to ML) - TODO End to end project in Python (Data cleaning to ML) - TODO Gain knowledge in dealing with larger datasets. - TODO Better expertise in 'Data Wrangling' and Exploratory Data Analysis (EDA) - TODO Gain fundamental expertise in Cloud Platforms - TODO Improving knowledge of how ML algorithms work - TODO Big data - TODO AWS fundamentals - TODO Demonstrate an automation pipeline process
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