Danny Ma

I love the transformation at the end! 😈❤️

Learn SQL for free at my profile - Danny Ma 🙂

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Take my free SQL Masterclass GitHub course with over 900 ⭐️’s - a cryptocurrency case study with 50+ questions with code solutions!

Join the 8 Week SQL Challenge and solve 8 realistic case studies using your SQL skills - some of these exercises are now used for actual hiring interviews across the world!

Sign up for a free 10 day trial on O’Reilly and take my SQL Simplified course and learn SQL fundamentals from scratch using O’Reilly’s interactive Katacoda platform!

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🔔 Follow me and ding the bell to get notified when I post!

I share fun data memes, sometimes insightful posts about the data world and every now and then, I share limited time free Udemy coupons!

These videos are kind of a new thing I’m trying - I hope you enjoy them 😂

#DataScience #data #analytics

Learn SQL with me for free! 🙂

1. Take my SQL Masterclass GitHub course - over 50+ questions with solutions and data included for an engaging cryptocurrency case study!

This repo now has almost 1,000 stars! ⭐️⭐️⭐️

https://lnkd.in/guSHn4VT

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2. Join my 8 Week SQL Challenge and solve my series of 8 realistic case studies - these challenges have actually been used for real interviews across the world!

All data and questions for all 8 case studies are included for free 🙂

https://lnkd.in/dyxvUK6s

P.S - I’m gonna turn these into their own projects on Github so you can use the data and integrate your analysis data visualisations with tools of your choice 🙂

You can use the bell icon on my profile Danny Ma to get notified when I post these new resources for the community!

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3. Checkout my SQL Simplified interactive online course on the O’Reilly platform - this will teach you the basics of SQL and get you proficient very quickly!

You can join the O’Reilly platform for a 10 day free trial - no credit card required for sign up!

https://lnkd.in/dZD87QJd

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If you want to skip to the good part - join me at Data With Danny for my Serious SQL course! 🙂

www.datawithdanny.com

There are absolutely no prerequisites and everyone is welcome to join us in the DWD family ❤️

For a one-time payment of $49 ($29 for students) - you get unlimited access to the following:

• All course content including tutorials, datasets and recorded live training videos

• Access to our members only Discord server where myself and my team of mentors are on hand to answer all of your data and career questions (not just related to Serious SQL!)

• Early sneek-peak access at new upcoming course content and private members only events!


To get access to the $29 student price - please send me a message to support@datawithdanny.com from your university email address!

#SQL #data #analytics

Live coding tests are not good indicators of performance for most people. The best developers I know need time to think and plan out their solutions, not regurgitate code they’ve memorised like some college exam 🙂

#data #analytics #datascience

I'm a self-taught #DataScientist and I don't have a background in programming, a Masters degree, a PhD or even a single certification to my name.

Want to know how I did it? ⬇ (Part 1)

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Back in 2013 when I first even heard of the term data scientist - and honestly, I fell in love with that title, I really thought that it was the job of my dreams.

Around that time - there was a Kaggle competition which required participants to optimise flight schedules to minimise layovers and delays.

And that really blew my mind - I just thought it was a crazily complex problem and I was amazed that you could use computer code to write a solution for it! 🤯

I still remember telling my best friend about how cool I thought the whole concept was - writing code to solve seriously complex problems at scale ❤️

At the time - I had just finished my bachelors of commerce. I had majored in actuarial science and business economics - but I also felt that I did little in my 4 years at university but memorise formulas and learn how to take exams.

All the financial maths and proofs were not really absorbed into my brain cells as much as I would have liked...

But I did have a good understanding of probability and risk - all probably thanks to one of my lecturers who framed every single probability concept as though we were in a casino 😎

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My first formal role outside of internships was as a data analyst - not just any type of data analyst, I was in campaign analytics.

And quite honestly - although there were lots of times where I wanted to quit my job in those first 2 years - they were also some of the most formative experiences in my working career.

I used a LOT (and I mean a lot) of SQL in my role, we were profiling how customers were purchasing products in supermarkets, then also designing marketing campaigns and experiments, as well as going in and building and executing email blasts with over 1M emails sent weekly.

There was no margin for error - I had the tedious job of creating a QA template - then teaching the rest of the team to follow it to reduce operational risk.

But after I did this for a few months - I wondered if it was going to get me any closer to my #datascience goal - this lead me to feel super frustrated and doing everything that I could to improve my skills outside of work.

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I did everything - Kaggle, CodeAcademy, LeetCode, Hackerrank, Coursera, books, reading ML research papers, reading Python package source code, reading blog posts etc

Pretty much everything that aspiring data scientists are doing now - I was doing 7 years ago - I would be up at 4:30am and learn Python until 9am then head off to work and arrive late to the office.

I kept this up for at least 6 months, relentlessly focused on making it - I had a clear goal in mind - become a data scientist!

EDIT: part 2 is continued here!
https://lnkd.in/gxe_jGR

#DataWithDanny

Data Scientist A: Attempts to learn everything as fast as possible
Data Scientist B: Creates a learning plan and prioritises key skills
↓ I've been both A & B throughout my career - here are my thoughts:

When I first started learning about data science - I was definitely type A.

I started googling and researching everything I could about data science - trying to formulate a plan but also struggling under the immense pressure.

“There is just so much stuff you need to know!“ was my constant thought as I was in this phase.

I made a list of 20+ things I needed to know - and I was in a super fast rush to learn everything.

I mean - how can anyone take me seriously as a #DataScientist if I don't know everything a data scientist should know?!?!?

I remember at the peak of my madness - I was waking up at 5am to learn Python, reading statistics textbooks and machine learning papers at the office when I had some breaks for lunch and on public transport.

I was obsessed about learning data science, but I was definitely not taking a very sustainable approach to my learning process!

To say I was overwhelmed is understating how I actually felt - I was drowning under the workload and I'm impressed I managed to go so long without burning out or having a mental breakdown!

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These days looking back on my experience - I don't think I would personally change a single thing because it has taken me down my particular path to wherever I am now.

However - there are many thing which I would change purely because of how inefficient I was with my time and also how poorly the skills I was learning mapped to what I would actually do as a data scientist in the future!

Taking a more methodical approach, rather than a shotgun approach would have helped me immensely on my data journey and this is what drives me to do what I do today - running the Data With Danny program and teaching data science skills in a particular order!

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If you're after a solid and proven learning path for #DataScience - here's what I recommend:

1. Learn SQL and use it to solve data problems (really well)

2. Learn Python at the same time as SQL if you can! (as a pure programming language)

3. Start a GitHub project to track all of your progress and learnings - you can also focus on this as you starting SQL and Python - but be sure to not overwhelm yourself!

4. Try some ML and analytics challenges and turn them into blog posts, tutorials or other knowledge sharing initiatives like a YouTube video - this will help you get feedback from others and also improve your personal brand

Consult maths textbooks or Google when you run into any technical parts you don't know - there is no shame in learning it just in time, just make sure you learn it thoroughly!

5. Help others as much as possible as you are also learning - teaching something is one of the best ways to learn it thoroughly!

6. Try to apply all of your new skills in your current role - practice, get feedback, repeat!

#DataWithDanny

Hey #Data and #Analytics Community - here are 9 FREE Udemy courses worth $1,000 🤑 EDIT: Latest post with 8 new courses here: https://lnkd.in/gRuGW6f

Get in QUICK - please COMMENT and SHARE with your friends so others can see this too!!! 🚀🚀🚀 #DataWithDanny

Basic Python
https://lnkd.in/gzSDg3F

Automate boring stuff with Python
https://lnkd.in/geSethr

Practical approach to Python
https://lnkd.in/gsF8RYj

Regex in Python
https://lnkd.in/gwPDz6S

Absolute Beginners Python
https://lnkd.in/g6RKuTU

Marketing Analytics with R
https://lnkd.in/g2uTD6z

Basics of ML in R
https://lnkd.in/gN_vent

Financial Analytics in R
EDIT: EXPIRED!!!

Tableau 4 Data Science
https://lnkd.in/gCekz-W

#DataScience