< 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. This is a work in progress.
< ### 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