DataTalks.Club

By DataTalks.ClubTechnology

DataTalks.Club episodes

January 13, 2023Doing Software Engineering in Academia - Johanna Bayer
We talked about:
Johanna’s background

Open science course and reproducible papers

Research software engineering

Convincing a professor to work on software instead of papers

The importance of reproducible analysis

Why academia is behind on software engineering

The problems with open science publishing in academia

The importance of standard coding practices

How Johanna got into research software engineering

Effective ways of learning software engineering skills

Providing data and analysis for your project

Johanna’s initial experience with software engineering in a project

Working with sensitive data and the nuances of publishing it

How often Johanna does hackathons, open source, and freelancing

Social media as a source of repos and Johanna’s favorite communities

Contributing to Git repos

Publishing in the open in academia vs industry

Johanna’s book and resource recommendations

Conclusion

Links:
The Society of Research Software Engineering, plus regional chapters: https://society-rse.org/

The RSE Association of Australia and New Zealand: https://rse-aunz.github.io/

Research Software Engineers (RSEs) The people behind research software: https://de-rse.org/en/index.html

The software sustainability institute: https://www.software.ac.uk/

The Carpentries (beginner git and programming courses): https://carpentries.org/

The Turing Way Book of Reproducible Research: https://the-turing-way.netlify.app/welcome

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
50 min
January 06, 2023Data-Centric AI - Marysia Winkels
We talked about:
Marysia’s background

What data-centric AI is

Data-centric Kaggle competitions

The mindset shift to data-centric AI

Data-centric does not mean you should not iterate on models

How to implement the data-centric approach

Focusing on the data vs focusing on the model

Resources to help implement the data-centric approach

Data-centric AI vs standard data cleaning

Making sure your data is representative

Knowing when your data is good enough

The importance of user feedback

“Shadow Mode” deployment

What to do if you have a lot of bad data or incomplete data

Marysia’s role at PyData

How Marysia joined PyData

The difference between PyData and PyCon

Finding Marysia online

Links:
Embetter & Bulk Demo: https://www.youtube.com/watch?v=L---nvDw9KU

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
54 min
December 16, 2022Business Skills for Data Professionals - Loris Marini
We talked about:
Loris’ background

Transitioning from physics to data

Aligning people on concepts

Lead indicators and stickiness

Context, semantics, and meaning

Communication and being memorable

Making data digestible for business and building trust

The importance of understanding the language of business

Stakeholder mapping

Attending business meetings as a data professional

Organizing your stakeholder map

Prioritizing

How to support the business strategy

Learning to speak online

Resource recommendations from Loris

Links:
Discovering Data Discord server: https://bit.ly/discovering-data-discord

Loris' LinkedIn: https://www.linkedin.com/in/lorismarini/

Loris' Twitter: https://twitter.com/LorisMarini
55 min
December 09, 2022From Software Engineer to Data Science Manager - Sadat Anwar
We talked about:
Sadat’s background

Sadat’s backend engineering experience

Sadat’s pivot point as a backend engineer

Sadat’s exposure to ML and Data Science

Sadat’s Act Before you Think approach (with safety nets)

Sadat’s street cred and transition into management

The hiring process as an internal candidate

The importance of people management skills

The Brag List

The most difficult part of transitioning to management

Focusing on projects and setting milestones

Sadat’s transition from EM to data science management

How much domain knowledge is needed for management?

The main difference between engineering and management

How being an EM helped Sadat transition no DS management

53:32 Transitioning to DS management from other roles

How to feel accomplished as a manager

Sadat’s book recommendations

Sadat’s meetups

Links:
Sadat's Meetup page: https://www.meetup.com/berlin-search-technology-meetup/

Meetup event "Bias in AI: how to measure it and how to fix it event": https://www.meetup.com/data-driven-ai-berlin-meetup/events/289927565/

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
53 min
December 02, 2022Teaching and Mentoring in Data Analytics - Irina Brudaru
We talked about:
Irina’s background

Irina as a mentor

Designing curriculum and program management at AI Guild

Other things Irina taught at AI Guild

Why Irina likes teaching

Students’ reluctance to learn cloud

Irina as a manager

Cohort analysis in a nutshell

How Irina started teaching formally

Irina’s diversity project in the works

How DataTalks.Club can attract more female students to the Zoomcamps

How to get technical feedback at work

Antipatterns and overrated/overhyped topics in data analytics

Advice for young women who want to get into data science/engineering

Finding Irina online

Fundamentals for data analysts

Suggestions for DataTalks.club collaborations

Conclusions

Links:
LinkedIn Account: https://www.linkedin.com/in/irinabrudaru/

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
54 min
November 25, 2022Technical Writing and Data Journalism - Angelica Lo Duca
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
51 min
November 18, 2022From Digital Marketing to Analytics Engineering - Nikola Maksimovic
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
47 min
November 11, 2022Product Owners in Data Science - Anna Hannemann
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
55 min
November 04, 2022Building Data Science Practice - Andrey Shtylenko
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
50 min
October 28, 2022Large-Scale Entity Resolution - Sonal Goyal
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
54 min