DataTalks.Club

We talked about: Andreas’s background Why data engineering is becoming more popular Who to hire first – a data engineer or a data scientist? How can I, as a data scientist, learn to build pipelines? Don’t use too many tools What is a data pipeline and why do we need it? What is ingestion? Can just one person build a data pipeline? Approaches to building data pipelines for data scientists Processing frameworks Common setup for data pipelines — car price prediction Productionizing the model with the help of a data pipeline Scheduling Orchestration Start simple Learning DevOps to implement data pipelines How to choose the right tool Are Hadoop, Docker, Cloud necessary for a first job/internship? Is Hadoop still relevant or necessary? Data engineering academy How to pick up Cloud skills Avoid huge datasets when learning Convincing your employer to do data science How to find Andreas Links: LinkedIn: https://www.linkedin.com/in/andreas-kretz Data engieering cookbook: https://cookbook.learndataengineering.com/ Course: https://learndataengineering.com/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Santiago’s background “Transitioning to ML” vs “Adding ML as a skill” Getting over the fear of math for software developers Learning by explaining Seven lessons I learned about starting a career in machine learning Lesson 1 – Take the first step Lesson 2 – Learning is a marathon, not a sprint Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together. Lesson 4 – Do something with the knowledge you gain Lesson 5 – ML is not just math. Math is not scary. Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary. Lesson 7 – You don’t need to know every detail Tools and frameworks needed to transition to machine learning Problem-based learning vs Top-down learning Learning resources Santiago’s favorite books Santiago’s course on transitioning to machine learning Improving coding skills Building solutions without machine learning Becoming a better engineer What is the difference between machine learning and data science? Getting into machine learning - Reiteration Getting past the math Links: Santiago's Twitter: https://twitter.com/svpino Santiago's course: https://gumroad.com/svpino#kBjbC Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Links: https://www.notion.so/Analytics-Engineer-New-Role-in-a-Data-Team-9decbf33825c4580967cf3173eb77177 https://www.linkedin.com/in/victoriaperezmola/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html Conference: https://datatalks.club/conferences/2021-summer-marathon.html

We talked about: Jessi’s background Uri’s background Data governance Implementing data governance: policies and processes Reasons not to have data governance Start with “why” Cataloging and classifying our data Let data work for you The human component Data quality Defining policies Implementing policies Shopping-card experience for requesting data Proving the value of data catalog Using data catalog Data governance = data catalog? Links: Book: https://www.oreilly.com/library/view/data-governance-the/9781492063483/ Jessi’s LinkedIn: https://www.linkedin.com/in/jashdown/ Uri’s LinkedIn: https://linkedin.com/in/ugilad Uri’s Twitter: https://twitter.com/ugilad Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html Conference: https://datatalks.club/conferences/2021-summer-marathon.html

We talked about: Yury’s background Failing fast: Grammarly for science Not failing fast: Keyword recommender Four steps to epiphany Lesson learned when bringing XGBoost into production When data scientists try to be engineers Joining a fintech startup: Doing NLP with thousands of GPUs Working at a Telco company Having too much freedom The importance of digital presence Work-life balance Quantifying impact of failing projects on our CVs Business trips to Perm: don’t work on the weekend What doesn’t kill you makes you stronger Links: Yury's course: https://mlcourse.ai/ Yury's Twitter: https://twitter.com/ykashnitsky Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Data-led academy Arpit’s background Growth marketing Being data-led Data-led vs data-driven Documenting your data: creating a tracking plan Understanding your data Tools for creating a tracking plan Data flow stages Tracking events — examples Collecting the data Storing and analyzing the data Data activation Tools for data collection Data warehouses Reverse ETL tools Customer data platforms Modern data stack for growth Buy vs build People we need to in the data flow Data democratization Motivating people to document data Product-led vs data-led Links: https://dataled.academy/ Join our Slack: https://datatalks.club/slack.html

We talked about: Shawn’s background and his book Marketing ourselves Components of personal marketing Personal brand for an average developer Picking a domain: what to write about? Being too niche Finding a good niche Learning in public Borrowed platforms vs own platform Starting on social media: Picking what they put down Career transitioning: mutual exchange of value Personal marketing for getting a new job Getting hired through the back door Finding content ideas Marketing yourself in public — summary Open-source knowledge Internal marketing: promoting ourselves at work Signature initiative Public speaking Wrapping up Discount for the coding career book 75% of the engineering ladder criteria are not technical Links: Shawn's personal page: https://www.swyx.io/ Twitter: https://twitter.com/swyx Book of the week page: https://datatalks.club/books/20210510-the-coding-career-handbook.html (with a discount for DTC members!) Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Tatiana’s background 12 career hacks and changing career Hack #1: Change your social circle Hack #2: Forget your fears and stereotypes Hack #3: Forget distractions Hack #4: Don’t overestimate others and don’t underestimate yourself Hack #5: Attention genius Hack #6: Make a team Hack #7: Less is more. Forget about perfectionism Hack #8: Initial creation Hack #9: Find mentors Hack #10: Say “no” Hack #11: Look for failures Hack #12: Take care of yourself Kaggle vs internships and pet projects Resources for learning machine learning Starting with Kaggle Improving focus Astroinformatics How background in Physics is helpful for transitioning Leaving academia Preparing for interviews Links: Mock interviews: https://www.pramp.com/ Learning ML: https://www.coursera.org/learn/machine-learning and https://www.coursera.org/specializations/deep-learning Python: https://www.coursera.org/learn/machine-learning-with-python  SQL: https://www.sqlhabit.com/  Practice: https://www.kaggle.com/ MIT 6.006: https://courses.csail.mit.edu/6.006/fall11/notes.shtml Coding: https://leetcode.com/ System design: https://www.educative.io/courses/grokking-the-system-design-interview Ukrainian telegram groups for interview preparation: https://t.me/FaangInterviewChannel,  https://t.me/FaangTechInterview, https://t.me/FloodInterview Join DataTalks.Club: https://datatalks.club/slack.html

We talked about: Oleg’s background Standing out in recruitment process NextRound — a service for free mock interviews Why rejections are generic Starting NextRount — preparing a list of situations Steps in the interview process Read the job description! CV is your landing page Take-home assignments Questions about your past experience Hypothetical case questions Technical rounds Handling rejections What to do after receiving an offer? Do recruiters pay attention to age? Getting a job with a PhD — it’s a cold start problem Should I answer rejection emails? Negotiating when my salary is low Should I apply for jobs that require 5 years of experience? Tricking applicant tracking systems What else Oleg learned after interviewing 300 data scientists How a horse's ass determined the design of a space shuttle Links: Oleg's service for interviews: https://nextround.cc/ LinkedIn: https://www.linkedin.com/in/olegnovikov/ Join DataTalks.Club: https://datatalks.club/slack.html

We talked about: DataTalks.Club intro Lior’s background Who is a data strategist? Improving communication between business and tech Building trust Putting data and business people together Dealing with pushbacks Building things in the lean way (and growing tomatoes) Starting with ugly code Convincing others to take our code MVP vs development and Hummus Talking to people who can’t code Break down the silos Hummus Hummus places in Berlin Lior’s book: Data is Like a Plate of Hummus Data chaos Links: Book: https://www.amazon.com/-/en/Sarah-Mayor/dp/B086L277LZ (can be found on any amazon store) Company: https://www.taleaboutdata.com/ Podcast: https://podcast.whatthedatapodcast.com/ Linkedin: https://www.linkedin.com/in/liorbarak/ Twitter: https://twitter.com/liorb Hummus places in Berlin: Azzam: https://goo.gl/maps/uCkb3ATc5CVKapDa6 Akkawy: https://g.page/akkawy The Eatery Berlin: https://g.page/theeateryberlin Join DataTalks.Club: https://datatalks.club/slack.html