How to get started in #datascience?

👀🧵👇 See thread below
2/ 1. Craft your own personal learning plan
Earlier this year I made a video that details the steps you can take to craft your own personal learning plan for your data journey. Everyone's plan is different, make your own! Here's how...
3/ 2. Work on data projects using datasets that is interesting to you
When starting out, I found that working on datasets that's interesting to you will help you engage in the process. Be persistent and work on the project to completion (end-to-end).
How? Data→Model→ Deployment
4/ 3. Over time, build a portfolio of projects and share on GitHub. Be creative on this part in order to set yourself apart from others. Iris or Titanic datasets are good starting points but don't showcase this, seek out new and interesting data, e.g. collect your own data
5/ 4. Learning and Build in the public
Put yourself out there by tweeting about your learning progress. This helps to build public accountability and in doing so helps you to be consistent in your learning journey. Do the #66daysofdata challenge by @KenJee_DS
6/ Resources
There are ample resources from which you can learn data science
- Bootcamps
- Mentors
- Books
- Blogs
- YouTube
- Podcast
- Udemy, Data Camp, Data Quest, 365 Data Science
7/ Speaking of learning resources, I make #free #datascience contents for:
- YouTube → Data Professor
- Medium →
8/ My friends also make awesome tutorials, definitely check them out:
- @KenJee_DS
- @Krishnaik06…
- @codebasicshub
- @hellotinah…
Too many to list here...(more in my Notion template)
9/ My free @NotionHQ template…
Also watch the video
10/ That's a lot of tweet already in this thread. Finally, get inspired by these #datascience #quotes that I've compiled here 👇…
Here's mine:
"The best way to learn data science is to do data science. And please enjoy the journey."
Thanks for reading!

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More from @thedataprof

31 Aug
Here’s a cartoon illustration I’ve drawn a while back:
The #machinelearning learning curve

👀🧵👇 See thread below
2/ Starting the learning journey
The hardest part of learning data science is taking that first step to actually start the journey.
3/ Consistency and Accountability
After taking that first step, it may be challenging to maintain the consistency needed to push through with the learning process. And that’s where accountability steps in.
Read 9 tweets
27 Aug
Hi friends, here’s my new hand-drawn cartoon illustration ✏️
Quickly deploy #machinelearning models
👀🧵👇 See thread below
2/ Deployment of machine learning models is often overlooked especially in academia
- We spend countless hours compiling the dataset, processing the data, fine tuning the model and perhaps interpreting and making sense of the model
- Many times we stop at that
- Why not deploy?
3/ Perhaps model deployment is difficult?
- Django? Flask? API?
- We now have access to powerful libraries:
1. Dash (@plotlygraphs)
2. @Gradio
3. @streamlit
4. Shiny (@rstudio)
Read 9 tweets
17 Aug
Cheat sheet that summarizes #DataScience in 10 pages
(Links in the comments below 👇)
2/ Link to the cheatsheet by Maverick Lin…
3/ Topics include:
- Overview of Data science
- Probability and Statistics
- Data cleaning
- Feature engineering
- Modeling
- Classical Machine learning
- Deep learning
- Python data structures
Read 4 tweets
26 Jul
1/ #Pandas is the go-to library that you need for #datawrangling for your #datascience projects when coding in #Python.
👀🧵👇 See thread below
2/ Why Do We Need Pandas?
The Pandas library has a large set of features that will allow you to perform tasks from the first intake of raw data, its cleaning and transformation to the final curated form in order to validate hypothesis testing and machine learning model building.
3/ Basics of Pandas - 1. Pandas Objects
Pandas allows us to work with tabular datasets. The basic data structures of Pandas that consists of 3 types: Series, DataFrame and DataFrameIndex. The first 2 are data structures while the latter serves as a point of reference.
Read 10 tweets
25 Jul
1/ #MachineLearning Crash Course by Google
- Free course
- Learn and apply fundamental machine learning concepts
- 30+ exercises
- 25 lessons
- 15 hours to complete
- Real-world case studies
- Explainers of ML algorithms

👀🧵👇 See thread below
2/ Machine Learning Crash Course by Google…
3/ Free machine learning crash course from Google
Read 5 tweets
25 Jul
1/ Interested in how Deep Learning and AI is impacting a 50-year old grand challenge in biology (protein structure folding)?
See this thread 👀🧵👇
#deeplearning #AI #biology #bioinformatics
2/ Deepmind's Alphafold2 Solves Protein Structures (Part 1) #shorts
3/ Deepmind's Alphafold2 Solves Protein Structures (Part 2) #shorts
Read 5 tweets

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