DataTalks.Club

We talked about: Tammy’s background Being the chief of data First projects as the first data person in a company Initial resistance Expanding the team Role of business analyst Platanomelon’s stack Order for growing the data team Demand forecasting Should analysts know machine learning Qualifications for the first data person in a company Providing accurate results Receiving insights in a timely manner Providing useful insights Giving ownership to the team Starting as the first data person in a company Data For Future podcast Supporting team members that are stuck Finding Tammy online Links:  Tammy's podcast: https://dataforfuture.org/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Mihail’s background NLP and self-driving vehicles Transitioning from academia to the industry Machine learning researchers Finding open-ended problems Machine learning engineers Is data science more engineering or research? What can engineers and researchers learn from one another? Bridging the disconnect between researchers and engineers Breaking down silos Fluid roles Full-stack data scientists Advice to machine learning researchers Advice to machine learning engineers Reading papers Choosing between engineering or research if you’re just starting Confetti.ai Links: https://twitter.com/mihail_eric http://confetti.ai/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Marianna’s background Being the only data scientist What should already be in the company How much experience do you need Identifying problems Prioritization What should the company already know? First week First month First quarter Managing expectations Solving problems without ML Project timelines Finding the best solution Evaluating performance Getting stuck Communicating with analysts Transitioning from engineering to data science Growing the team Stopping projects Questions for the company From research to production Wrapping up Links: Marianna's LinkedIn: https://www.linkedin.com/in/marianna-diachuk-53ba60116/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Adam’s background Adam’s laser and data experience Metrics and why do we care about them Examples of metrics KPIs KPI examples Derived KPIs Creating metrics — grocery store example Metric efficiency North Star metrics Threshold metrics Health metrics Data team metrics Experiments: treatment and control groups Accelerate metrics and timeboxing Links: Domino's article about measuring value: http://blog.dominodatalab.com/measuring-data-science-business-value Adam's article about skills useful for data scientists: https://towardsdatascience.com/how-to-apply-your-hard-earned-data-science-skillset-812585e3cc06 Adam's article about standing out: https://towardsdatascience.com/how-to-stand-out-as-a-great-data-scientist-in-2021-3b7a732114a9 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Natalie’s background Airbyte What is ETL? Why ELT instead of ETL? Transformations How does ELT help analysts be more independent? Data marts and Data warehouses Ingestion DB ETL vs ELT Data lakes Data swamps Data governance Ingestion layer vs Data lake Do you need both a Data warehouse and a Data lake? Airbyte and ELT Modern data stack Reverse ETL Is drag-and-drop killing data engineering jobs? Who is responsible for managing unused data? CDC – Change Data Capture Slowly changing dimension Are there cases where ETL is preferable over ELT? Why is Airbyte open source? The case of Elasticsearch and AWS Links: Natalie's LinkedIn: https://www.linkedin.com/in/nataliekwong/ https://airbyte.io/blog/why-the-future-of-etl-is-not-elt-but-el Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Learning algorithms and data structures Resources for learning algorithms and data structures Most important data structures Learning the abstractions Learning algorithms if they aren’t needed at work Common mistakes when using wrong data structures Importance of data structures for data scientists Marcello’s book - Advanced Algorithms and Data Structures Bloom filters Where Bloom filters are useful Approximate nearest neighbours Searching for most similar vectors Knowing frameworks vs knowing internals of data structures Serializing Bloom filters Algorithmic problems in job interviews Important data structures for data scientists and data engineers Learning by doing Importance of compiled languages for data scientists Links: Marcello's book: Advanced Algorithms and Data Structures http://mng.bz/eP79 (promo code for 35% discount: poddatatalks21) MIT, Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/ Algorithms specialization by Tim Roughgarden: https://www.coursera.org/specializations/algorithms Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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

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

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

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