DataTalks.Club

We talked about: Summary of “Getting a Data Engineering Job” webinar Python and engineering skills  Interview process Behavioral interviews Technical interviews Learning Python and SQL from scratch Is having non-coding experience a disadvantage? Analyst or engineer? Do you need certificates? Do I need a master’s degree? Fully remote data engineering jobs Should I include teaching on my resume? Object-oriented programming for data engineering Python vs Java/Scala SQL and Python technical interview questions GCP certificates Is commercial experience really necessary? From sales to engineering Solution engineers Wrapping up Links: Getting a Data Engineering Job (webinar): https://www.youtube.com/watch?v=yvEWG-S1F_M The Flask Mega-Tutorial Part I - Hello, World! blog: https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world Mode SQL Tutorial: https://mode.com/sql-tutorial/ MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Daynan’s background Astronomy vs cosmology Applications of data science and machine learning in astronomy Determining signal vs noise What the data looks like in astronomy Determining the features of an object in space Ground truth for space objects Why water is an important resource in the space economy Other useful resources that can be found in asteroids Sources of asteroids The data team at an asteroid mining company Open datasets for hobbyists Mission and hardware design for asteroid mining Partnerships and hires Links:  LinkedIn: https://www.linkedin.com/in/daynan/ We're looking for a Sr Data Engineer: https://boards.eu.greenhouse.io/karmanplus/jobs/4027128101?gh_jid=4027128101 Minor Planet Center: https://minorplanetcenter.net/- JPL Horizons has a nice set of APIs for accessing data related to small bodies (including asteroids): https://ssd.jpl.nasa.gov/api.html ESA has NEODyS: https://newton.spacedys.com/neodys   IRSA catalog that contains image and catalog data related to the WISE/NEOWISE data (and other infrared platforms): https://irsa.ipac.caltech.edu/frontpage/ NASA also has an archive of data collected from their various missions, including a node related to small bodies: https://pds-smallbodies.astro.umd.edu/ Sub-node directly related to asteroids: https://sbn.psi.edu/pds/ Size, Mass, and Density of Asteroids (SiMDA) is a nice catalog of observed asteroid attributes (and an indication of how small our sample size is!): https://astro.kretlow.de/?SiMDA The source survey data, several are useful for asteroids: Pan-STARRS (https://outerspace.stsci.edu/display/PANSTARRS) MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Juan’s background Typical problems in marketing that are solved with ML Attribution model Media Mix Model – detecting uplift and channel saturation Changes to privacy regulations and its effect on user tracking User retention and churn prevention A/B testing to detect uplift Statistical approach vs machine learning (setting a benchmark) Does retraining MMM models often improve efficiency? Attribution model baselines Choosing a decay rate for channels (Bayesian linear regression) Learning resource suggestions Bayesian approach vs Frequentist approach Suggestions for creating a marketing department Most challenging problems in marketing The importance of knowing marketing domain knowledge for data scientists Juan’s blog and other learning resources Finding Juan online Links:  Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w Juan's website: https://juanitorduz.github.io Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:  Gloria’s background Working with MATLAB, R, C, Python, and SQL Working at ICE Job hunting after the bootcamp Data engineering vs Data science Using Docker Keeping track of job applications, employers and questions Challenges during the job search and transition Concerns over data privacy Challenges with salary negotiation The importance of career coaching and support Skills learned at Spiced Retrospective on Gloria’s transition to data and advice Top skills that helped Gloria get the job Thoughts on cloud platforms Thoughts on bootcamps and courses Spiced graduation project Standing out in a sea of applicants The cohorts at Spiced Conclusion Links: LinkedIn: https://www.linkedin.com/in/gloria-quiceno/ Github: https://github.com/gdq12 MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Jeff’s background Getting feedback to become a better teacher Going from engineering to teaching Jeff on becoming a curriculum writer Creating a curriculum that reinforces learning Jeff on starting his own data engineering bootcamp Shifting from teaching ML and data science to teaching data engineering Making sure that students get hired Screening bootcamp applicants Knowing when it’s time to apply for jobs The curriculum of JigsawLabs.io The market demand of Spark, Kafka, and Kubernetes (or lack thereof) Advice for data analysts that want to move into data engineering The market demand of ETL/ELT and DBT (or lack thereof) The importance of Python, SQL, and data modeling for data engineering roles Interview expectations How to get started in teaching The challenges of being a one-person company Teaching fundamentals vs the “shiny new stuff” JigsawLabs.io Finding Jeff online Links:  Jigsaw Labs: https://www.jigsawlabs.io/free Teaching my mom to code: https://www.youtube.com/watch?v=OfWwfTXGjBM Getting a Data Engineering Job Webinar with Jeff Katz: https://www.eventbrite.de/e/getting-a-data-engineering-job-tickets-310270877547 MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:  Jessica’s background Giving a talk at a tech conference about coffee Jessica’s transition into tech (How to get started) Going from learning to actually making money Landing your first job in tech Does your age matter when you’re trying to get a job? Challenges that Jessica faced in the beginning of her career Jessica’s role at PyLadies Fighting the Imposter Syndrome Generational differences in digital literacy and how to improve it Events organized by PyLadies Jessica’s beginnings at PyLadies (organizing events) Jessica’s experience with public speaking The impact of public speaking on your career Tips for public speaking Jessica’s work at Ecosia Discrimination in the tech industry (and in general) Finding Jessica online Links: Ecosia's website: https://www.ecosia.org/ Ecosia's blog: https://blog.ecosia.org/ecosia-financial-reports-tree-planting-receipts/ PyLadies Berlin: https://berlin.pyladies.com/ PyLadies' Meetup: https://meetup.com/PyLadies-Berlin Code Academy: https://www.codecademy.com/ Freecodecamp: https://www.freecodecamp.org/ Coursera Machine Learning: https://www.coursera.org/learn/machine-learning ML Bookcamp code: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp Google Summer code: https://summerofcode.withgoogle.com/ Outreachy website: https://www.outreachy.org/ Alumni Interview: https://railsgirlssummerofcode.org/blog/2020-03-17-alumni-interview-jessica Python pizza: https://python.pizza/ Pycon: https://pycon.it/en Pycon 2022: https://2022.pycon.de/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about:  Nicolas’ background The tech talent market in different countries Hiring data scientists vs data engineers A spike in interest for data engineering roles The importance of recruiters having  technical knowledge The main challenges of hiring data engineers The difference in hiring junior, mid, and senior level data engineers Things recruiters look for in people who switch to a data engineering role The importance of knowing cloud tools The importance of knowing infrastructure tools Preparing for the interview The importance of a formal education The importance having a project portfolio How your current domain influence the interview Conclusion Links:  Nicolas' Twitter: https://twitter.com/n_rassam  Nicolas' LinkedIn: https://www.linkedin.com/in/nicolasrassam/  Onfido is hiring: https://onfido.com/engineering-technology/  Interview with Alicja about recruiting data scientists: https://datatalks.club/podcast/s07e02-recruiting-data-professionals.html Webinar "Getting a Data Engineering Job" with Jeff Katz: https://eventbrite.com/e/310270877547 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Christopher’s background The essence of DataOps Also known as Agile Analytics Operations or DevOps for Data Science Defining processes and automating them (defining “done” and “good”) The balance between heroism and fear (avoiding deferred value) The Lean approach Avoiding silos The 7 steps to DataOps Wanting to become replaceable DataOps is doable Testing tools DataOps vs MLOps The Head Chef at Data Kitchen What’s grilling at Data Kitchen? The DataOps Cookbook Links: DataOps Manifesto website: https://dataopsmanifesto.org/en/ DataOps Cookbook: https://dataops.datakitchen.io/pf-cookbook Recipes for DataOps Success: https://dataops.datakitchen.io/pf-recipes-for-dataops-success DataOps Certification Course: https://info.datakitchen.io/training-certification-dataops-fundamentals DataOps Blog: https://datakitchen.io/blog/ DataOps Maturity Model: https://datakitchen.io/dataops-maturity-model/ DataOps Webinars: https://datakitchen.io/webinars/ Join DataTalks.Club: https://datatalks.club/slack.html   Our events: https://datatalks.club/events.html

We talked about: Stefan’s background Applications of machine learning in healthcare Sidekick Health – gamified therapeutics How is working for King different from Sidekick Health? The rewards systems in gamified apps The importance of building a strong foundation for a data science team The challenges of building an app in the healthcare industry Dealing with ethics issues Sidekick Health’s personalized recommendations and content The importance of having the right approach in A/B tests (strong analytics and good data) The importance of having domain knowledge to work as a data professional in the healthcare industry Making a data-driven company Risks for Sidekick Health Sidekick Health growth strategy Using AI to help people live better lives Links: LinkedIn: https://www.linkedin.com/in/stefanfreyrgudmundsson/  Job listings: https://sidekickhealth.bamboohr.com/jobs/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Liesbeth’s background What is design? The importance of interaction in design Design as a process (Double Diamond technique) How long does it take to go from an idea to finishing the second diamond? Design thinking (Google’s PAIR) What is a Design Sprint and who should participate in it? Why should data specialists care about design? Challenging your task-giver (asking “why”) How to avoid the “Chinese whisper game” (reiterating the problem) Defining the roadmap for data science teams What is innovation? Bringing innovation to your management Task force-team approach to solving problems Innovation, resource management issues, and using data to back your ideas Words of advice for those interested in design and innovation Links: LinkedIn: https://www.linkedin.com/in/liesbeth-dingemans/ Medium posts on design, innovation, art and AI: https://medium.com/@liesbethmd Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html