DataTalks.Club

By DataTalks.ClubTechnology

DataTalks.Club episodes

November 05, 2021Becoming a Solopreneur in Data - Noah Gift
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
1 hr
October 29, 2021Building Business Acumen for Data Professionals - Thom Ives
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
1 hr 6 min
October 22, 2021Conquering the Last Mile in Data - Caitlin Moorman
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
1 hr 3 min
October 15, 2021Similarities and Differences between ML and Analytics - Rishabh Bhargava
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
1 hr
October 08, 2021Building and Leading Data Teams - Tammy Liang
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
1 hr
October 01, 2021What Researchers and Engineers Can Learn from Each Other - Mihail Eric
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
1 hr 2 min
September 24, 2021Introducing Data Science in Startups - Marianna Diachuk
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
59 min
September 17, 2021Defining Success: Metrics and KPIs - Adam Sroka
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
1 hr 3 min
September 11, 2021Making Sense of Data Engineering Acronyms and Buzzwords - Natalie Kwong
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
1 hr 1 min
September 03, 2021Mastering Algorithms and Data Structures - Marcello La Rocca
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
1 hr 3 min