Because it is an assemblage of both the worlds!Skills involving a typical Data Science Role:As much as the domain offers a good pay, it also demands an individual to have a spectrum of skill set.
The major essentials are listed below:Mathematics ( Statistics in particular but not just that!)Algorithm and Data StructuresCritical Thinking and Problem SolvingSoftware engineeringSoft SkillsCreativityMajor data Science Roles:The major Data Science roles are as follows:Data EngineerData AnalystMachine Learning EngineerData ScientistLet us understand these roles one at a time.
Data EngineerThe workflow in any typical data driven company starts with collection of the raw data from various sources.
That’s the basic role of an Data Engineer.
They are software engineers who design, build and integrate data from various resources.
These engineers write complex queries on Big data to make sure it is easily accessible and it works smoothly.
They have to ensure that there is uninterrupted flow of data from the resources.
A Data Engineer works on the early part of the complete pipeline of a project.
Core Skills and Resources:Solid Database Knowledge (SQL & NoSQL)Data Warehousing (Hadoop, HIVE, Apache Spark)Basic Machine Learning KnowledgeWithout big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway — Geoffrey Moore, author and consultantData AnalystBoth the data engineer and data analyst work hand in hand at the early stage of the pipeline.
A data analyst interprets the data and turns it into information that is best suited for the requirement of the project.
The main responsibilities of a data analyst are as follows:Collection and interpreting dataAnalysing the resultsIdentify patterns and trendsVisualise the insightsCore Skills and Resources:Statistical Analysis (SAS)Visualization (Tableau)Big Data (Hadoop, Hive)Machine Learning (TensorFlow,Torch)Apart from these technical skills, a data analyst requires a strong business and interpersonal skills since they work closely with the clients for arranging the data.
“The goal is to turn the data into information, and information into insight” — Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
Machine Learning EngineerA machine learning engineer works in the middle phase of a pipeline.
These engineers will work on the big data arranged during the early stages of the project.
These engineers are responsible for creating programs and algorithms that trains a computer (model in specific) automatically.
Big Data as the name suggests, is a huge amount of data.
As the number of dimensions in the data set keeps increasing, it is very difficult for a human to perceive the data and in turn manipulate it.
Hence the rise of machine learning algorithms.
Core Skills and Resources:Machine Learning Algorithms and Libraries (Scikit-Learn,TensorFlow)Data Modelling and EvaluationSoftware Engineering (REST API calls, modularity, version control, testing)Data ScientistData scientist are part mathematician, part computer scientist and part trend-spotter.
Their day to day responsibilities involve the following:Collecting raw data and transforming it into a usable formatObtain insights and patterns from the dataTrain models to predict unseen instancesApply Machine Learning, Deep LearningCommunicate and collaborate with both Technical and Business team“ A Data Scientist is the alchemist of the 21st century”Core Skills and Resources:Communication skillsBusiness AcumenDatabaseData warehousingData visualizationML and AIData Science is an art.
It is fascinating as to what one can do with Big data that’s constantly changing the world.
Although, Data Scientist is the highest paid job, in my opinion, all the roles are equally important at every stage of a project since Data Science projects are usually not a ‘one man show’.