Learn and follow employer-specific administrative policies, procedures, safety protocols.
Demonstrate familiarity with data science nomenclature and explain Data Analyst’s operational role.
Articulate and practice employer-specific “work culture” models (if applicable).
Complete tutorials (e.g., DataCamp, codecademy, Lynda.com) to attain basic knowledge and skills in common data science software and techniques (e.g., SQL, R, Python, Java, visualization, statistics).
Project Intake and Project Management
Demonstrate understanding of work intake process.
Acquire project management knowledge: e.g., SixSigma, or applicable internal program.
Analyze prospective projects: “Understand the ask up front.”
Communicate with coworkers, clients, users, and management throughout process.
Determine documentation needs at process inception.
Perform cost benefit analysis.
Develop process map.
Manage workflow with the aid of project management platforms, such as: SharePoint, Huddle, Confluence, Workfront.
Data/Extract, Transform, and Load (ETL)
Learn various data and statistical analysis concepts, such as: population, sample, data sets, variables, types of data, i.e. numerical, categorical.
Collect (extract) data from multiple sources, and ready for submission to workflow.
Utilize data manipulation tools, such as Structured Query Language (SQL), Alteryx, and Pentaho to transform data into operable format: address and resolve naming conflicts, duplicate records, and different value representations.
Load data into appropriate tables, where further manipulation may take Place, or final product displayed.
Conceptualize data presentation formats for various audiences (clients, customers, internal/external, etc.).
Use data visualization software (such as Tableau, Cognos, Qlikview) to capture and re-present data.
Develop ability to manipulate and present data in complicated ways.