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We talked about:

Angelica’s background Angelica’s books Data journalism How Angelica got into data journalism The field of digital humanities and Angelica’s data journalism course Technical articles vs data journalism articles Transforming reports into data storytelling Are reports to stakeholders considered technical writing? Data visualization in articles Article length The process of writing an article Finding writing topics How Angelica got into writing a book (communication with publishers) The process for writing a book Brainstorming Reviews and revisions Conclusion

Links:

Data Journalism examples (FENCED OUT): https://www.washingtonpost.com/graphics/world/border-barriers/europe-refugee-crisis-border-control/??noredirect=on Data Journalism examples (La tierra esclava): https://latierraesclava.eldiario.es/ Small medium publication aiming at being Stack Overflow of Medium: https://medium.com/syntaxerrorpub Example of a self-published book on Data Visualization: https://www.amazon.com/Introduction-Data-Visualization-Storytelling-Scientist-ebook/dp/B07VYCR3Z6/ref=sr_1_4?crid=4JRJ48O7K8TK&keywords=joses+berengueres&qid=1668270728&sprefix=joses+beremguere%2Caps%2C273&sr=8-4 My novels (in Italian) La bambina e il Clown: https://www.amazon.it/Bambina-Clown-Angelica-Lo-Duca/dp/1500984515/ref=sr_1_9?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=2KGK9GMN0FAHI&keywords=la+bambina+e+il+clown&qid=1668270769&sprefix=la+bambina+e+il+clown%2Caps%2C88&sr=8-9 My novels (in Italian) Il Violinista: https://www.amazon.it/Violinista-1-Angelica-Lo-Duca/dp/1501009672/ref=sr_1_1?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=12KTF9EF5UKIG&keywords=il+violinista+lo+duca&qid=1668270791&sprefix=il+violinista+lo+duca%2Caps%2C81&sr=8-1 Course on Data Journalism: https://www.coursera.org/learn/visualization-for-data-journalism

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Nikola’s background Making the first steps towards a transition to BI and Analytics Engineering Learning the skills necessary to transition to Analytics Engineering The in-between period – from Marketing to Analytics Engineering Nikola’s current responsibilities Understanding what a Data Model is Tools needed to work as an Analytics Engineer The Analytics Engineering role over time The importance of DBT for Analytics Engineers Where can one learn about data modeling theory? Going from Ancient Greek and Latin to understanding Data (Just-In-Time Learning) The importance of having domain knowledge to analytics engineering Suggestion for those wishing to transition into analytics engineering The importance of having a mentor when transitioning Finding a mentor Helpful newsletters and blogs Finding Nikola online

Links:

Nikola's LinkedIn account: https://www.linkedin.com/in/nikola-maksimovic-40188183/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

Creating Business Applications with Microsoft 365: Techniques in Power Apps, Power BI, SharePoint, and Power Automate

Learn how to automate processes, visualize your data, and improve productivity using Power Apps, Power Automate, Power BI, SharePoint, Forms, Teams, and more. This book will help you build complete solutions that often involve storing data in SharePoint, creating a front-end application in Power Apps or Forms, adding additional functionality with Power Automate, and effective reports and dashboards in Power BI. This new edition greatly expands the focus on Power Apps, Power BI, Power Automate, and Teams, along with SharePoint and Microsoft Forms. It starts with the basics of programming and shows how to build a simple email application in .NET, HTML/JavaScript, Power Apps on its own, and Power Apps and Power Automate in combination. It then covers how to connect Power Apps to SharePoint, create an approval process in Power Automate, visualize surveys in Power BI, and create your own survey solution with the combination of a number of Microsoft 365 tools. You’ll work with anextended example that shows how to use Power Apps and SharePoint together to create your own help ticketing system. This book offers a deep dive into Power BI, including working with JSON, XML, and Yes/No data, as well as visualizing learning data and using it to detect inconsistencies between Excel files. You’ll also see how to connect to Remedy and to the help system you will have created. Under author Jeffrey Rhodes’s guidance, you’ll delve into the Power Apps collection to learn how to avoid dreaded "delegation" issues with larger data sets. Back on applications, you will create a training class sign-up solution to only allow users to choose classes with available seats. Digging deeper into Teams, you’ll learn how to send chats, posts, and "adaptive cards" from Power Automate. Rounding things out, you’ll save Forms attachments to SharePoint with Power Automate, create your own "Employee Recognition" app with all of the Power Platform and Teams, add or edit weekly status reports, and learn how to create reservation and scoring applications. After reading the book, you will be able to build powerful applications using Power Apps, Power Automate, Power BI, SharePoint, Forms, and Teams. What You Will Learn Create productivity-enhancing applications with Power Apps, Power Automate, SharePoint, Forms, and/or Teams Transform and visualize data with Power BI to include custom columns, measures, and pivots Avoid delegation issues and tackle complicated Power Apps issues like complex columns, filtering, and ForAll loops Build scheduled or triggered Power Automate flows to schedule Teams Meetings, send emails, launch approvals, and much more Who This Book Is For Business and application developers.

We talked about:

About Anna and METRO Anna’s background The importance of a technical background for data product owners What are product owners? Product owners vs product managers Anna’s work on recommender systems at METRO Expanding the data team Types of algorithms used for recommender systems What kind of knowledge and skills data product owners need to have Problems and ideas should come from the business How Anna handles all her responsibilities The process for starting work on new domains Product portfolio management ProductTank and Anna’s role in it Anna’s resource recommendations

Links:

Data Science for Business Book: https://www.amazon.de/-/en/Foster-Provost/dp/1449361323/ref=sr_1_1?keywords=data+science+for+business&qid=1666404807&qu=eyJxc2MiOiIxLjg3IiwicXNhIjoiMS41MiIsInFzcCI6IjEuNDYifQ%3D%3D&sr=8-1 Article on Data Science Products: https://www.linkedin.com/pulse/way-create-data-science-products-lessons-learnt-anna-hannemann-phd/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Audience Poll Andrey’s background What data science practice is Best DS practice in a traditional company vs IT-centric companies Getting started with building data science practice (finding out who you report to) Who the initiative comes from Finding out what kind of problems you will be solving (Centralized approach) Moving to a semi-decentralized approach Resources to learn about data science practice Pivoting from the role of a software engineer to data scientist The most impactful realization from data science practice Advice for individual growth Finding Andrey online

Links: 

Data Teams book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused/dp/1484262271/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Sonal’s background How the idea for Zingg came about What Zingg is The difference between entity resolution and identity resolution How duplicate detection relates to entity resolution How Sonal decided to start working on Zingg How Zingg works What Zingg runs on Switching from consultancy to working on a new open source solution Why Zingg is open source Open source licensing Working on Zingg initially vs now Zingg’s current and future team Sonal’s biggest current challenge Avoiding problems with entity/identity resolution through database design Identity resolution vs basic joins, data fusions, and fuzzy joins Deterministic matching vs probabilistic machine learning Identity and entity resolution applications for fraud detection Graph algorithms vs classic ML in entity resolution Identity resolution success stories What Sonal would do differently given the chance to start over with Zingg Advice for those seeking to realize their own solution to a data problem Reading suggestion from Sonal Conclusion

Links:

Open-Source Spotlight demo "Zingg":https://www.youtube.com/watch?v=zOabyZxN9b0 Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs book: https://www.amazon.com/Creative-Selection-Inside-Apples-Process/dp/1250194466

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Tomasz’s background What Tomasz did before DataOps (Data Science) Why Tomasz made the transition from Data science to DataOps What is DataOps? How is DataOps related to infrastructure? How Tomasz learned the skills necessary to become DataOps Becoming comfortable with terminal The overlap between DataOps and Data Engineering Suitable/useful skills for DataOps Minimal operational skills for DataOps Similarities between DataOps and Data Science Managers Tomasz’s interesting projects Confidence in results and avoiding going too deep with edge cases Conclusion

Links:

Terminal setup video, 19 minutes long: https://www.youtube.com/watch?v=D2PSsnqgBiw Command line videos, one and a half hour to become somewhat comfy with the terminal: https://www.youtube.com/playlist?list=PLIhvC56v63IKioClkSNDjW7iz-6TFvLwS Course from MIT talking about just that (command line, git, storing secrets): https://missing.csail.mit.edu/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

Today I’m chatting with Iván Herrero Bartolomé, Chief Data Officer at Grupo Intercorp. Iván describes how he was prompted to write his new article in CDO Magazine, “CDOs, Let’s Get Out of Our Comfort Zone” as he recognized the importance of driving cultural change within organizations in order to optimize the use of data. Listen in to find out how Iván is leveraging the role of the analytics translator to drive this cultural shift, as well as the challenges and benefits he sees data leaders encounter as they move from tactical to strategic objectives. Iván also reveals the number one piece of advice he’d give CDOs who are struggling with adoption. 

Highlights / Skip to:

Iván explains what prompted him to write his new article, “CDOs, Let’s Get Out of Our Comfort Zone” (01:08) What Iván feels is necessary for data leaders to close the gap between data and the rest of the business and why (03:44) Iván dives into who he feels really owns delivery of value when taking on new data science and analytics projects (09:50) How Iván’s team went from managing technical projects that often didn’t make it to production to working on strategic projects that almost always make it to production (13:06) The framework Iván has developed to upskill technical and business roles to be effective data / analytics translators (16:32) The challenge Iván sees data leaders face as they move from setting and measuring tactical goals to moving towards strategic goals and initiatives (24:12) Iván explains how the C-Suite’s attitude impacts the cross-functional role of data & analytics leadership (28:55) The number one piece of advice Iván would give new CDO’s struggling with low adoption of their data products and solutions (31:45)

Quotes from Today’s Episode “We’re going to do all our best to ensure that [...] everything that is expected from us is done in the best possible way. But that’s not going to be enough. We need a sponsorship and we need someone accountable for the project and someone who will be pushing and enabling the use of the solution once we are gone. Because we cannot stay forever in every company.” – Iván Herrero Bartolomé (10:52)

“We are trying to upskill people from the business to become data translators, but that’s going to take time. Especially what we try to do is to take product owners and give them a high-level immersion on the state-of-the-art and the possibilities that data analytics bring to the table. But as we can’t rely on our companies having this kind of talent and these data translators, they are one of the profiles that we bring in for every project that we work on.” – Iván Herrero Bartolomé (13:51)

“There’s a lot to do, not just between data and analytics and the other areas of the company, but aligning the incentives of all the organization towards the same goals in a way that there’s no friction between the goals of the different areas, the people, [...]  and the final goals of the organization. – Iván Herrero Bartolomé (23:13) “Deciding which goals are you going to be co-responsible for, I think that is a sophisticated process that it’s not mastered by many companies nowadays. That probably is one of the main blockers keeping data analytics areas working far from their business counterparts” – Iván Herrero Bartolomé (26:05)

“When the C-suite looks at data and analytics, if they think these are just technical skills, then the data analytics team are just going to behave as technical people. And many, many data analytics teams are set up as part of the IT organization. So, I think it all begins somehow with how the C-suite of our companies look at us.” – Iván Herrero Bartolomé (28:55) “For me, [digital] means much more than the technical development of solutions; it should also be part of the transformation of the company, both in how companies develop relationships with their customers, but also inside how every process in the companies becomes more nimble and can react faster to the changes in the market.” – Iván Herrero Bartolomé (30:49) “When you feel that everyone else not doing what you think they should be doing, think twice about whether it is they who are not doing what they should be doing or if it’s something that you are not doing properly.” – Iván Herrero Bartolomé (31:45)

Links “CDOs, Let’s Get Out of Our Comfort Zone”: https://www.cdomagazine.tech/cdo_magazine/topics/opinion/cdos-lets-get-out-of-our-comfort-zone/article_dce87fce-2479-11ed-a0f4-03b95765b4dc.html LinkedIn: https://www.linkedin.com/in/ivan-herrero-bartolome/

We talked about:

Katie’s background What is a data scientist? What is a data science manager? Quality of the craft How data leaders promote career growth Supporting senior data professionals Choosing the IC route vs the management route Managing junior data professionals Talking to senior stakeholders and PMs as a junior The importance of hiring juniors What skills do data scientist managers need to get hired? How juniors that are just starting out can set themselves apart from the competition Asking senior colleagues for help and the rubber duck channel The challenges of the head of data Conclusion

Links:

Jobs at Gloss Genius: https://boards.greenhouse.io/glossgenius

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Alvaro’s background Working as a QA (Quality Assurance) engineer Transitioning from QA to Machine Learning Gathering knowledge about ML field Searching for an ML job (improving soft skills and CV) Data science interview skills Zoomcamp projects Zoomcamp project deployment How to not undersell yourself during interviews Alvaro’s experience with interviews during his transition Alvaro’s Zoomcamp notes Alvaro’s coach The importance of mathematical knowledge to a transition into ML Preparing for technical interviews Alvaro’s typical workday Alvaro’s team’s tech stack The importance of a technical background to transitioning into ML

Links:

Alvaro's CV: https://www.dropbox.com/s/89hkt3ug0toqa2n/CV%20nou%20-%20angl%C3%A8s.pdf?dl=0 Github profile: https://github.com/ziritrion LinkedIn profile: https://www.linkedin.com/in/alvaronavas/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcampJoin 

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

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

Join hosts Shane Safir and Alcine Mumby as they dig deep with Dr. Christopher Emdin around how to be a good ancestor, biomimicry as a guide to school transformation, burning the pedagogical sage, and so much more. This episode will change you. A must-listen for all new administrators and teachers finding their way in complex times.

For Further Learning:

Order Chris’s book Rathedemic at http://www.beacon.org/Ratchetdemic-P1703.aspx Read Chris’s foreword in Street Data to make connections to the pod conversation Order adrienne marie brown’s Emergent Strategy at https://www.akpress.org/emergentstrategy.html

We talked about:

Supreet’s background Responsible AI Example of explainable AI Responsible AI vs explainable AI Explainable AI tools and frameworks (glass box approach) Checking for bias in data and handling personal data Understanding whether your company needs certain type of data Data quality checks and automation Responsibility vs profitability The human touch in AI The trade-off between model complexity and explainability Is completely automated AI out of the question? Detecting model drift and overfitting How Supreet became interested in explainable AI Trustworthy AI Reliability vs fairness Bias indicators The future of explainable AI About DataBuzz The diversity of data science roles Ethics in data science Conclusion

Links:

LinkedIn: https://www.linkedin.com/in/supreet-kaur1995/ Databuzz page: https://www.linkedin.com/company/databuzz-club/ Medium Blog Page: https://medium.com/@supreetkaur_66831

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Audience Poll Andrey’s background What data science practice is Best DS practice in a traditional company vs IT-centric companies Getting started with building data science practice (finding out who you report to) Who the initiative comes from Finding out what kind of problems you will be solving (Centralized approach) Moving to a semi-decentralized approach Resources to learn about data science practice Pivoting from the role of a software engineer to data scientist The most impactful realization from data science practice Advice for individual growth Finding Andrey online

Links:

Data Teams book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused/dp/1484262271/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

podcast_episode
by David A. Bader (New Jersey Institute of Technology (NJIT))

We talked about:

David’s background A day in the life of a professor David’s current projects Starting a school The different types of professors David’s recent papers Similarities and differences between research labs and startups Finding (or creating) good datasets David’s lab Balancing research and teaching as a professor David’s most rewarding research project David’s most underrated research project David’s virtual data science seminars on YouTube Teaching at universities without doing research Staying up-to-date in research David’s favorite conferences Selecting topics for research Convincing students to stay in academia and competing with industry Finding David online

Links: 

David A. Bader: https://davidbader.net/ NJIT Institute for Data Science: https://datascience.njit.edu/ Arkouda: https://github.com/Bears-R-Us/arkouda NJIT Data Science YouTube Channel: https://www.youtube.com/c/NJITInstituteforDataScience

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Christiaan’s background Usual ways of collecting and curating data Getting the buy-in from experts and executives Starting an annotation booklet Pre-labeling Dataset collection Human level baseline and feedback Using the annotation booklet to boost annotation productivity Putting yourself in the shoes of annotators (and measuring performance) Active learning Distance supervision Weak labeling Dataset collection in career positioning and project portfolios IPython widgets GDPR compliance and non-English NLP Finding Christiaan online

Links:

My personal blog: https://useml.net/ Comtura, my company: https://comtura.ai/ LI: https://www.linkedin.com/in/christiaan-swart-51a68967/ Twitter: https://twitter.com/swartchris8/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Zhamak’s background What is Data Mesh? Domain ownership Determining what to optimize for with Data Mesh Decentralization Data as a product Self-serve data platforms Data governance Understanding Data Mesh Adopting Data Mesh Resources on implementing Data Mesh

Links:

Free 30-day code from O'Reilly: https://learning.oreilly.com/get-learning/?code=DATATALKS22 Data Mesh book: https://learning.oreilly.com/library/view/data-mesh/9781492092384/ LinkedIn: https://www.linkedin.com/in/zhamak-dehghani

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Mehdi’s background The difference between startup, scale-up and enterprise Hypergrowth Data platform engineers in a scale-up environment What a data platform is and who builds it Managing the fast pace of a scale-up while ensuring personal growth Should a senior data person consider a scale-up or an enterprise? Should a junior data person consider a scale-up or an enterprise? Sourcing talent for hyper-growth companies and developing a community culture Generating content and getting feedback Generalization vs specialization for data engineers in a scale-up The ratio of work between platform building and use case pipelines Being proactive in order to progress to mid or senior level Caps and bass guitars MehdiO DataTV and DataCreators.Club (Mehdi’s YouTube Channel and podcast)

Links:

Mehdi's YouTube channel: https://www.youtube.com/channel/UCiZxJB0xWfPBE2omVZeWPpQ Mehdi's Linkedin:  https://linkedin.com/in/mehd-io/ Mehdi's Medium Blog: https://medium.com/@mehdio Mehdi's data creators club: https://datacreators.club/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Alexander’s background The role of Partner at Königsweg Being part of the data and AI community How Alexander became chair at PyData Alexander’s many talks and advice on giving them Explaining AI to managers Why being able to explain machine learning to managers is important The experimentational nature of AI and why it’s not a cure-all Innovation requires patience Convincing managers not to use AI or ML when there are better (simpler) solutions The role of MLOps in enterprises Thinking about the mid- and long-term when considering solutions Finding Alexander online

Links: 

Alexander's Twitter: https://twitter.com/hendorf Alexander's LinkedIn: https://www.linkedin.com/in/hendorf/ Königsweg: https://www.koenigsweg.com PyData Südwest: https://www.meetup.com/pydata-suedwest/ PyData Frankfurt: https://www.meetup.com/pydata-frankfurt/ PyConDE & PyData Berlin: https://pycon.de

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

Danny’s background What an MLOps Architect does The popularity of MLOps Architect as a role Convincing an employer that you can wear many different hats Interviewing for the role of an MLOps Architect How Danny prioritizes work with data scientists Coming to WhyLabs when you’ve already got something in production vs nothing in production Market awareness regarding the importance of model monitoring How Danny (WhyLabs) chooses tools ONNX Common trends in tooling setups The most rewarding thing for Danny in ML and data science Danny’s secret for staying sane while wearing so many different hats T-shaped specialist, E-shaped specialist, and the horizontal line The importance of background for the role of an MLOps Architect Key differences for WhyLogs free vs paid Conclusion and where to find Danny online

Links:

Matt Turck: https://mattturck.com/data2021/ AI Observability Platform: https://whylabs.ai/observability Danny's LinkedIn: https://www.linkedin.com/in/dleybz/ Whylabs' website: https://whylabs.ai/ AI Infrastructure Alliance: https://ai-infrastructure.org/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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

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

We talked about:

DataTalks.Club intro Tereza’s background Working as a coach Identifying the mismatches between your needs and that of a company How to avoid misalignments Considering what’s mentioned in the job description, what isn’t, and why Diversity and culture of a company Lack of a salary in the job description Way of doing research about the company where you will potentially work How to avoid a mismatch with a company other than learning from your mistakes Before data, during data, after data (a company’s data maturity level) The company’s tech stack Finding Tereza online

Links: 

Decoding Data Science Job Descriptions (talk): https://www.youtube.com/watch?v=WAs9vSNTza8 Talk at ConnectForward: https://www.youtube.com/watch?v=WAs9vSNTza8 Slides: https://www.slideshare.net/terezaif/decoding-data-science-job-descriptions-250687704 Talk at DataLift: https://www.youtube.com/watch?v=pCtQ0szJiLA Slides: https://www.slideshare.net/terezaif/lessons-learned-from-hiring-and-retaining-data-practitioners

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

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

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