From Data Analyst to Data ScientistSource: Matterport mask RCNNHow does someone transition from being a Data Analyst to Data Scientist?Go to the LinkedIn website..Here we acknowledge the hard yards to build the skills to unlock insight from more-or-less any data.There are loads of great articles on starting out in data science (examples here and here) but less is said about the transition from data analyst.Before starting it’s worth me attempting a hand wavy definition of the two roles.A Data Analyst collects, processes and applies statistical algorithms to structured data in order to yield benefits and improve decision making.A Data Scientist has similar goals but also has robust skills for dealing with large quantities of unstructured data, potentially processing in near real time..Good data scientists are rare and in high demand; Let that be you and life should be good.Data science — loads to learn (left) , robots taking our jobs (middle) and good pay but remember that The Wolf a Wall Street ended in tears! (right)Am I already a data scientist?Most analysts will have a good foundation, but it takes years of effort to develop skills to comprehensively apply cutting edge approaches on awkward structures and/or large data sets..In the first couple of years of data science development one might expect to at least touch upon a reasonable chunk of the following:Data Science languages — Python/RRelational databases — MySQL, PostgressNon-relational databases — MongoDBMachine learning models — e.g..Regression, Boosted Trees SVM, NNsGraph — Neo4J, GraphXDistributed computing — Hadoop, SparkCloud — GCP/AWS/AzureAPI Interaction — OAuth, RestData Visualisation and Webapps — D3, RShinySpecialist fields — NLP, OCR and Computer VisionBoosted Trees models are popular in data science competitionsRShiny dashboards can be an effective way to develop an interactive means for others to explore data.Acquiring these skills takes a LOT of time (probably longer than your degree course)..Ten years ago it may have been acceptable to wait weeks to be sent on a data software course..On the numeric side, the Andrew Ng machine learning course and the Stanford neural network course are both fantastic and thoroughly enjoyable to study.Solve a problem — Preferably a real problem at your workplace, working alongside business experts and data engineers..Most medium to large organisations will have at least one small data science team..For example, spot a problem that could be automated and then buddy up with an expert, rather than commissioning them to do it.Champion appropriate tools and environments—Organisations aren’t always sure whether and how to invest in data science tools..Take opportunities to champion appropriate environments, tools and training.Develop a clear use case— Understand your business and how data science can be applied.. More details