DataTalks.Club

DataTalks.Club

By DataTalks.ClubTechnology
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DataTalks.Club episodes

  • Chief Data Officer - Marco De Sa

    We talked about:

    • Marco’s background
    • Role of CDO
    • Keeping track of many things
    • Becoming a CDO
    • Strategy vs tactics
    • VP of Data vs CDO
    • How many VPs of Data could be there?
    • Splitting the work between VP and CDO
    • Difference between CTO, CPO, and CDO
    • Breaking down the goals and working backwards from them
    • Assessing if we’re moving in the right direction
    • Dealing with many meetings
    • Being more effective
    • Building the data-driven culture
    • Challenges of working remotely
    • Does CDO need deep technical skills?
    • Importance of MBA
    • The key skills for becoming a CDO
    • Biggest challenges within OLX so far
    • Demonstrating the CDO skills on a job interview
    • Overcoming resistance

    • Join DataTalks.Club: https://datatalks.club/slack.html

      Our events: https://datatalks.club/events.html

      1 hr 2 min
    • Freelancing in Machine Learning - Mikio Braun

      We talked about:

      • Mikio’s background
      • What Mikio helps with
      • Moving from a full-time job to freelancing
      • Finding clients and importance of a strong network
      • Building a network
      • Initial meetings with clients
      • Understanding what clients need
      • Template for the offer (Million dollar consulting)
      • Deciding on rate type: hourly, daily, per project
      • Taking vacations (and paying twice for them)
      • Avoiding overworking
      • Specializing: consulting as a product
      • Working full-time as a principal vs being a consultant
      • Is the overhead worth it?
      • Getting a new client when you already have a project
      • After freelancing: what’s next?
      • Output of Mikio’s work
      • Learning new things
      • Lessons learned after finding clients
      • Registering as a freelancer in Germany
      • Personal liability of a freelancer
      • Effect of globalization and remote work on consulting
      • Advice for people who want to start freelancing
      • Woking full-time and freelancing at the same time

      • Books: 

        • Million Dollar Consulting  by Alan Weiss
        • Built to Sell by John Warrillow

        • Links:

          • Mikio's Twitter: https://twitter.com/mikiobraun
          • Mikio's LinkedIn: https://www.linkedin.com/in/mikiobraun/

          • Join DataTalks.Club: https://datatalks.club/slack.html

            Our events: https://datatalks.club/events.html

            1 hr 3 min
          • Launching a Startup: From Idea to First Hire - Carmine Paolino

            We talked about:

            • Carmine’s background
            • Carmine’s startup FreshFlow
            • Doing user research
            • Design thinking
            • Entrepreneur first
            • Finding co-founders: the “expertise edges” framework
            • The structure of the EF program
            • Coming up with the idea
            • How important is going through a startup accelerator?
            • Finding your first client
            • Finding investors
            • Consequences of having a bad investor
            • Splitting responsibilities between co-founders
            • Hiring
            • The importance of delegating
            • Making work attractive to hires
            • Plans for the future
            • Just-in-time supply chain
            • What would you have done differently?
            • Advice for people starting a startup
            • Don’t focus on skills only
            • Getting motivation
            • Am I ready for a startup?
            • Importance of a business school
            • Advice on finding a co-founder
            • Do I need EF if I already have an idea?
            • Having a prototype before the pitch

            • Books:

              • The Mom Test by Rob Fitzpatrick
              • Design Thinking by Robert Curedale
              • Links:

                • FreshFlow: https://freshflow.ai/
                • Carmine's LinkedIn: https://www.linkedin.com/in/carminepaolino
                • Carmine's Twitter: https://twitter.com/paolino

                • Join DataTalks.Club: https://datatalks.club/slack.html

                  Our events: https://datatalks.club/events.html

                  1 hr 8 min
                • Approach Learning as ML Project - Vladimir Finkelshtein [mini]

                  We don't have an episode lined up for this week, but we recorded a small chat with Vladimir some time ago. Enjoy it! 

                  We talked about:

                  • Vladimir's background
                  • Learning by answering questions
                  • Don't be afraid of being wrong
                  • Winnings books
                  • Learning random things
                  • Approach learning as a machine learning project

                  • Links:

                    • Vladimir on LinkedIn: https://www.linkedin.com/in/vladimir-finkelshtein/

                    • Join DataTalks.Club: https://datatalks.club/slack.html

                      Our events: https://datatalks.club/events.html


                      14 min
                    • Humans in the Loop - Lina Weichbrodt

                      We talked about:

                      • Lina’s background
                      • What we need to remember when starting a project (checklists)
                      • Make sure the problem is formalized and close to the core business
                      • Get the buy-in with stakeholders
                      • Building trust with stakeholders
                      • Don’t just focus on upsides – ask about concerns
                      • Turning a concert into a metric
                      • What happens when something goes wrong?
                      • Post mortem reporting
                      • Apply the 5 why’s
                      • If a lot of users say it’s a bug – it’s worth investigating
                      • Post mortem format
                      • Action points
                      • Debugging vs explaining the model
                      • Are there online versions of checklists?
                      • Make sure to log your inputs
                      • Talking to end-users and using your own service
                      • Your ideas vs Stakeholder ideas
                      • Should data practitioners educate the team about data?
                      • People skills and ‘dirty’ hacks
                      • Where to find Lina

                      • Join DataTalks.Club: https://datatalks.club/slack.html

                        Our events: https://datatalks.club/events.html

                        58 min
                      • Running from Complexity - Ben Wilson

                        We talked about:

                        • Ben’s Background
                        • Building solutions for customers
                        • Why projects don’t make it to production
                        • Why do people choose overcomplicated solutions?
                        • The dangers of isolating data science from the business unit
                        • The importance of being able to explain things
                        • Maximizing chances of making into production
                        • The IKEA effect
                        • Risks of implementing novel algorithms
                        • If it can be done simply – do that first
                        • Don’t become the guinea pig for someone’s white paper
                        • The importance of stat skills and coding skills
                        • Structuring an agile team for ML work
                        • Timeboxing research
                        • Mentoring
                        • Ben’s book
                        • ‘Uncool techniques’ at AI-First companies
                        • Should managers learn data science?
                        • Do data scientists need to specialize to be successful?

                        • Links:

                          • Ben's book: https://www.manning.com/books/machine-learning-engineering-in-action (get 35% off with code "ctwsummer21")

                          • Join DataTalks.Club: https://datatalks.club/slack.html

                            Our events: https://datatalks.club/events.html


                            1 hr 12 min
                          • I Want to Build a Machine Learning Startup! - Elena Samuylova

                            We talked about:

                            • Elena’s background
                            • Why do a startup instead of being an employee?
                            • Where to get ideas for your startup
                            • Finding a co-founder
                            • What should you consider before starting a startup?
                            • Vertical startup vs infrastructure startup
                            • ‘AI First’ startups
                            • Building tools for engineers
                            • What skills do you need to start a startup?
                            • Startup risks
                            • How to be prepared to fail
                            • Work-life balance
                            • The part-time startup approach
                            • Startup investment models
                            • No resources and no technical expertise – what to do?
                            • Productionizing your services
                            • When to hire an expert
                            • Talking to people with a problem before solving the problem
                            • Starting Elena’s startup, Evidently
                            • Elena’s role at Evidently
                            • Why is Evidently open source?
                            • “People will just copy my open source code. Should I be concerned?”
                            • Bottom-up adoption
                            • Creating value so that clients engage with your product
                            • Is there a difference between countries when creating a startup?
                            • Does open source mean the data is safer?
                            • When should you hire engineers?
                            • Following the market
                            • Startups out of genuine interest vs Just for money and for fun

                            • Links:

                              • EvidentlyAI: https://evidentlyai.com/
                              • Elena's LinkedIn: https://www.linkedin.com/in/elenasamuylova/
                              • Elena's Twitter: https://twitter.com/elenasamuylova/

                              • Join DataTalks.Club: https://datatalks.club/slack.html

                                Our events: https://datatalks.club/events.html


                                59 min
                              • Build Your Own Data Pipeline - Andreas Kretz

                                We talked about:

                                • Andreas’s background
                                • Why data engineering is becoming more popular
                                • Who to hire first – a data engineer or a data scientist?
                                • How can I, as a data scientist, learn to build pipelines?
                                • Don’t use too many tools
                                • What is a data pipeline and why do we need it?
                                • What is ingestion?
                                • Can just one person build a data pipeline?
                                • Approaches to building data pipelines for data scientists
                                • Processing frameworks
                                • Common setup for data pipelines — car price prediction
                                • Productionizing the model with the help of a data pipeline
                                • Scheduling
                                • Orchestration
                                • Start simple
                                • Learning DevOps to implement data pipelines
                                • How to choose the right tool
                                • Are Hadoop, Docker, Cloud necessary for a first job/internship?
                                • Is Hadoop still relevant or necessary?
                                • Data engineering academy
                                • How to pick up Cloud skills
                                • Avoid huge datasets when learning
                                • Convincing your employer to do data science
                                • How to find Andreas

                                • Links:

                                  • LinkedIn: https://www.linkedin.com/in/andreas-kretz
                                  • Data engieering cookbook: https://cookbook.learndataengineering.com/
                                  • Course: https://learndataengineering.com/

                                  • Join DataTalks.Club: https://datatalks.club/slack.html

                                    Our events: https://datatalks.club/events.html

                                    1 hr 2 min
                                  • From Software Engineering to Machine Learning - Santiago Valdarrama

                                    We talked about:

                                    • Santiago’s background
                                    • “Transitioning to ML” vs “Adding ML as a skill”
                                    • Getting over the fear of math for software developers
                                    • Learning by explaining
                                    • Seven lessons I learned about starting a career in machine learning
                                    • Lesson 1 – Take the first step
                                    • Lesson 2 – Learning is a marathon, not a sprint
                                    • Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together.
                                    • Lesson 4 – Do something with the knowledge you gain
                                    • Lesson 5 – ML is not just math. Math is not scary.
                                    • Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary.
                                    • Lesson 7 – You don’t need to know every detail
                                    • Tools and frameworks needed to transition to machine learning
                                    • Problem-based learning vs Top-down learning
                                    • Learning resources
                                    • Santiago’s favorite books
                                    • Santiago’s course on transitioning to machine learning
                                    • Improving coding skills
                                    • Building solutions without machine learning
                                    • Becoming a better engineer
                                    • What is the difference between machine learning and data science?
                                    • Getting into machine learning - Reiteration
                                    • Getting past the math

                                    • Links:

                                      • Santiago's Twitter: https://twitter.com/svpino
                                      • Santiago's course: https://gumroad.com/svpino#kBjbC
                                      • Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230

                                      • Join DataTalks.Club: https://datatalks.club/slack.html

                                        Our events: https://datatalks.club/events.html

                                        1 hr

                                      About DataTalks.Club

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                                      DataTalks.Club - the place to talk about data!

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