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Building a Data Analytics Portfolio
- 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
Editor's Notes
- 1. Discovery: The team learns the business domain, assesses the resources available, framing the business problem as an analytics challenge. 2. Data preparation: Data transformation and Analysis. 3. Model planning: determines the methods, techniques, and workflow. Explore relationships between variables, selects key variables and the most suitable models. 4. Model building: Develops datasets for testing, training and production purposes. 5. Communicate results: Collaboration with major stakeholders, identify key findings, quantify the business value, and develop a narrative to summarize and convey findings to stakeholders. 6. Operationalize: delivers final reports, briefings, code, and technical documents, implement the models in a production environment.
- Kaggle https://www.kaggle.com/ LinkedIn Economic Graph Research https://engineering.linkedin.com/teams/data/projects/economic-graph-research/economic-graph-details DataDriven https://www.datadriven.org/ Microsoft OpenHack Dublin for AI/Machine Learning Tuesday 3rd - Thursday 5th July, 2018, 8:30am – 5:00pm Hackerday https://www.dezyre.com/hackerday Outlay 2018 banking hackathon, 7th and 8th of April https://www.outlayhackathon.com/ DataKind Dublin https://www.meetup.com/DataKind-DUB/ Viz for Social Good https://www.vizforsocialgood.com/ Tableau Public https://public.tableau.com/ Makeover Monday (weekly social data project) www.makeovermonday.co.uk/ Meetup https://www.meetup.com/ Eventbrite https://www.eventbrite.ie/ Analytics Institute https://analyticsinstitute.org/ Data Science and Engineering Club https://www.meetup.com/Data-Science-and-Engineering-Club/ GitHub https://github.com/ Data.gov https://www.data.gov/ Acclaim (enterprise-class Open Badge platform) https://www.youracclaim.com/ Neo4j Certification (Graph Database Platform) https://neo4j.com/graphacademy/neo4j-certification/ Google Analytics Individual Qualification (IQ) https://analytics.google.com/analytics/academy/ Cognitive Class - Free Data Science and Cognitive Computing Courses https://cognitiveclass.ai/ Coursera https://www.coursera.org/ EdX https://www.edx.org/ data.world (host and share your data, collaborate with your team) https://data.world/
- GitHub Pages is a static site hosting service designed to host your personal, organization, or project pages directly from a GitHub repository. showcase your work on GitHub Add your projects to your LinkedIn profile Twitter/Social Media: one of the most popular social media sites for Data Scientists is Twitter. Demonstrate communication and Knowledge sharing Content Blogs SlideShare List any papers or publications Online Presence: Kaggle, GitHub & LinkedIn profiles (try to fill out as much sections as possible) An end to end project Finding good datasets +Find a messy dataset => demonstrate data cleansing and transformation +Data sets for Data Visualization Projects shouldn't be messy, because you don't want to spend a lot of time cleaning data. Try to avoid projects that tackle common problems, pick something topical and relevant Clean up and document your code Split Code Into Modules Modular code is code which is separated into independent modules. Modular programming is a software design technique that emphasizes separating the functionality of a programme into independent, interchangeable modules, such that each contains everything necessary to execute only one aspect of the desired functionality. Creating a well-structured project, so its easy to integrate into operational flows Writing high-performance code that runs quickly and uses minimal system resources Documenting the installation and usage of your code well, so others can use it Picking an angle The important thing is to stick to a single angle. Trying to focus on too many things at once will make it hard to make an effective project. It’s also important to pick an angle that has sufficient nuance.
- Data Science and Engineering Club Saturday, July 7, 2018 Applied Math, Probability and Statistics for Data Science
- Frank Friedman Oppenheimer (August 14, 1912 – February 3, 1985) was an American particle physicist, cattle rancher, professor of physics at the University of Colorado, and the founder of the Exploratorium in San Francisco. Docendo discimus, (Latin "by teaching, we learn") is a Latin proverb.
- Content Blogs SlideShare List any papers or publications Online Presence: Kaggle, GitHub & LinkedIn profiles (try to fill out as much sections as possible)
- Problem Solving https://www.lynda.com/Business-Skills-tutorials/Problem-Solving-Techniques/553700-2.html Critical Thinking https://www.lynda.com/Business-Skills-tutorials/Critical-Thinking/424116-2.html Storytelling https://www.lynda.com/Excel-tutorials/Data-Visualization-Storytelling-Essentials/435230-2.html Communicating https://www.lynda.com/Leadership-Management-tutorials/Communicating-Confidence/359601-2.html Cultivating curiosity https://www.lynda.com/Business-Skills-tutorials/Cultivating-curiosity/185320/414081-4.html
- Analytics Capability Self-Assessment Survey Tableau Demo (Overall Dashboard to highlight Gaps, Role Personas with aspiring candidates/gaps, Mentor/Mentee Matching) Peer-led Training initiatives 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