DataTalks.Club

We talked about: Barbara’s background Do you need a manager or an expert? Technical and non-technical requirements for managers Importance of technical skills for managers Responsibilities and skills of a manager Importance of technical background for managers Getting involved in business development and sales Developing the team Checking team’s work Data science expert Hiring experts Who should we hire first? Can an expert build a team? Data science managers in startups Project management Ensuring that projects provide value Questions before starting a project Women in data science Finding Barbara online General advice Link: Barbara's LinkedIn: https://www.linkedin.com/in/barbara-sobkowiak-1a4a9568 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Nick’s background Being a career coach Overview of the hiring process Behavioral interviews for data scientists Preparing for behavioral interviews Handling "tricky" questions Project deep dive Business context Pacing, rambling, and honesty “What’s your favorite model?” What if I haven’t worked on a project that brought $1 mln? Different questions for different levels Product-sense interviews Identifying key metrics in unfamiliar domains Tech blogs Cold emailing Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Noah’s background Solopreneurship A day of a solopreneur Exponential vs linear work Escaping the office work - digging the tunnel Structuring goals Staying motivated Publishing books Planning out books Writing a book is like preparing to run a marathon Distributed income Getting started as a solopreneur Lowering expenses and adding time The right time to quit full-time Building a network Teaching at universities Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Links: https://join.slack.com/t/integratedmlai/shared_invite/zt-r3hpj44k-gfhf1pzIt3jixrATyXCWnQ https://www.linkedin.com/in/thomives/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Caitlin’s background The last mile in data The Pareto Principle Failing to use data Making sure data is used Communicating with decision-makers Working backwards from the last mile Understanding how data drives decisions Sketching and prototyping Showing the benefits of power data Measurability Driving change in data Asking high-leverage questions Resistance from users Understanding domain experts Linear projects vs circular projects Recommendations for data analyst students Finding Caitlin online Links: Emelie's talk https://locallyoptimistic.com/post/linear-and-circular-projects-part-1/ https://locallyoptimistic.com/post/linear-and-circular-projects-part-2/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Rishabh's background Rishabh’s experience  as a sales engineer Prescriptive analytics vs predictive analytics The problem with the term ‘data science’ Is machine learning a part of analytics? Day-to-day of people that work with ML Rule-based systems to machine learning The role of analysts in rule-based systems and in data teams Do data analysts know data better than data scientists? Data analysts’ documentation and recommendations Iterative work - data scientists/ML vs data analysts Analyzing results of experiments Overlaps between machine learning and analytics Using tools to bridge the gap between ML and analytics Do companies overinvest in ML and underinvest in analystics? Do companies hire data scientists while forgetting to hire data analysts? The difficulty of finding senior data analysts Is data science sexier than data analytics? Should ML and data analytics teams work together or independently? Building data teams Rishabh’s newsletter – MLOpsRoundup Links: https://mlopsroundup.substack.com/ https://twitter.com/rish_bhargava Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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