Tweet

This Thread may be Removed Anytime!

Twitter may remove this content at anytime! Save it as PDF for later use!

More from @thedataprof

Data Professor

@thedataprof

15 Sep

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

Read 10 tweets

Data Professor

@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

Data Professor

@thedataprof

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?

@plotlygraphs

3/ Perhaps model deployment is difficult?
- Django? Flask? API?
- We now have access to powerful libraries:
1. Dash (@plotlygraphs) plotly.com/dash/
2. @Gradio gradio.app
3. @streamlit streamlit.io
4. Shiny (@rstudio) shiny.rstudio.com

Read 9 tweets

Data Professor

@thedataprof

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

Data Professor

@thedataprof

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
developers.google.com/machine-learni…

3/ Free machine learning crash course from Google

Read 5 tweets

Data Professor

@thedataprof

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

Share this page!

Data Professor

Try unrolling a thread yourself!

More from @thedataprof

Data Professor

Data Professor

Data Professor

Data Professor

Data Professor

Data Professor

Did Thread Reader help you today?

Like this author's thread?