Moving from Business Analyst to Data Analyst to Data Scientist…

51 Pages·2015·7.68 MB·English

Moving from Business Analyst to Data
Analyst to Data Scientist…Advancing
Your Career and Your Organization
Joanne Carswell
September 2015
• Definitions—BA, DA, DS
• Roles on Project Teams
• Types of Analysis
• Business Analyst Core Skills
• Data Analyst Required Skills
• Data Scientist Required Skills
• Evolving Your Business Analysts
• References
• Six Sigma Green Belt
• Certified Business Analyst Professional (CBAP)
• Certified Scrum Product Owner for Agile (CSPO)
• With Cox Automotive 6 years
• IT Manager for Data Services
• Over 15 years experience in IT SDLC
• Presented at IIBA BBC 2012 & 2013 Conferences,
Project World & World Congress for Business
Analysts 2013, & BA World 2013, 2014, & 2015
• Loves to travel
• Plays tennis
• Dislikes yard work
• Favorite food is steak
• Participates in 5Ks
• Bike enthusiast
• Completed a 9 class program
for data science
• Favorite hobby is
251 finds!
Definitions and Roles
• Serves as a liaison between the
business users and developers
• Key activities in SDLC
– Business Analysis Planning &
Monitoring
– Elicitation
– Requirements Analysis
– Requirements Management &
Communication
• Define business need and business case
• Modeling current state
• Modeling future state
• Gap analysis
• Functional requirements
• Assessing proposed solution
Others?
• Works in SDLC as an expert on the data
• May also have Business Analyst responsibilities
• Key activities
– Data definitions
– Data mapping
– Data quality
– Data governance
– Data modeling
– Data profiling
• Modeling current state of data
• Modeling future state of data
• Uncovering inconsistencies in data
• Data mapping for source and target
• Aligning to data governance standards
• Documenting data, metrics, and KPIs
• Determining reporting needs
• Investigation of large complex problems
• Employs techniques and theories from
mathematics, statistics, and information
technology
• Key activities
– Manage dataset(s)
– Analyze data
– Apply statistical techniques for discovery
– Create visualizations to aid in understanding