The document outlines the importance of building a data analytics portfolio to demonstrate skills that add value by extracting insights from data and presenting them effectively. It includes guidance on data projects, networking opportunities, certifications, and essential soft skills needed for success in analytics. Additionally, it highlights the significance of peer-led training and self-assessment in identifying skill gaps for continuous improvement.

1. Building a Data
Analytics Portfolio
Jamie Renehan
Group Advanced Analytics @ BOI

2. Agenda
• Data Analytics Life Cycle
• Why build a Data Analytics Portfolio
• Building a Data Analytics Portfolio
• Data Projects
• Events and Networking
• Sharing Knowledge
• Data Analytics Certifications
• Soft Skills
• Peer-led Training

3. Data Analytics Life Cycle

4. Why build a Portfolio
• Demonstrate skills that add value
How to add value
• Extracting insights from raw data, and presenting those insights to others
• Building systems that offer direct value to the customer
• Building systems that offer direct value to others in the organization
• Sharing your expertise with others in the organization
Build a Portfolio demonstrate these Data Analytics skills
• Ability to communicate
• Ability to collaborate with others
• Technical competence
• Ability to reason about data
• Motivation and ability to take initiative

6. Building a Data Analytics Portfolio
• Data Projects (Kaggle Challenge, LinkedIn Economic Graph Research)
• Hackathons (Microsoft OpenHack Dublin, Hackerday, Girls in Tech Dublin,
Ulster Bank)
• ,
• Data Charity Initiatives (Data Kind, Viz for Social Good)
• Data Visualization Portfolio (Tableau Public, Makeover Monday)
• Social Media (LinkedIn Accomplishments such as Projects, Courses & Certs,
Twitter, Acclaim)

11. Data Projects
• Data sets (Messy vs Clean, demonstrate data cleansing, transformation or
Visualization)
• Avoid projects that tackle common problems
• Picking an angle
• Create a well-structured project (high-performance , modular code,
documentation)
• GitHub Pages (turn repositories into websites to showcase portfolio,
projects and documentation)

12. Events and Networking
• Data Events
• Meetups (Meetup.com, Eventbrite, MLDublin)
• Conferences (Analytics Institute)
• Workshops (Data Science and Engineering Club)
• Webinars

14. Share Knowledge
• Establish User Groups (Internal, meetup.com)
• Teach and deliver presentations (SlideShare)
• Distribute relevant content (Social Media, Blogging, publications, Github)

15. Data Analytics Certifications
• Web Analytics (Google Analytics IQ)
• Data Engineering (Microsoft, Oracle, Teradata)
• Data Visualization (Tableau Certified Professional, Power BI, Qlik)
• Advanced Analytics (R, Python)
• Cloud (Azure, AWS, Google)
• Data Science Platforms (Cloudera Data Science Workbench, SAS, Azure ML,
Dataiku)
• Graph Analytics (Neo4j)

16. Soft Skills
• Problem Solving (find the root cause of a problem)
• Critical Thinking (approach problem or task from multiple directions)
• Storytelling (build a compelling and digestible story)
• Communicating (present the results to engineers and senior executives)
• Curiosity (never stop asking “Why?”, explore, investigate, gain knowledge)

17. Peer-led Training
• Teradata Learning & Certification Group
• Tableau Masterclass Series
• Kaggle Santander Customer Transaction Prediction Challenge
• Machine Learning Algorithm Masterclass Series
• Analytics Institute Certification
• Soft skills development for Graduate Programme & Analytics teams

18. Data & Analytics COP Self-Assessment Survey
• Categorising Survey Respondents into 6 Analytics Personas
•
• Identify Skill Gaps and develop training approach to address
(Machine Learning / AI, Scrum, Predictive Modelling, Design Thinking)
• Finding Optimal Mentor-Mentee Matches

20. Questions
Jamie.Renehan@boi.com
@JamieRenehan
www.linkedin.com/in/JamieRenehan/
public.tableau.com/profile/Jamie.Renehan/