Tools for quickly performing Exploratory Data Analysis

A Thread 🧵

Here’s a curated list of YouTube videos that I’ve made on the topic.

#datascience #eda 1. AutoPlotter

How to add math equations to your @streamlit apps

Perhaps you wanted to describe that new algorithm that you have developed for your #machinelearning project.
You can describe them using the st.latex() method.

Code and demo inside 🧵 @streamlit 2/ Code

Here's how you can use st.latex() to write #math equations to describe your new #machinelearning algorithms inside a @streamlit app

Resources for getting started in #DataScience

Where to start? What to read? What to learn? I got you covered.

🧵 See thread below 👇 2/ Roadmap to #datascience

Here's my 4 step process on becoming a data scientist
1. Plan
2. Learn
3. Build
4. Explain

👉 Video
👉 Blog towardsdatascience.com/the-art-of-lea…

1/ Book overview and review
Practical Data Science with Python (By Nathan George)
🧵 See thread below
#datascience #python 2/ 👤 Author
- Nathan George
- Data scientist at a fintech company
- Taught at Regis University, DataCamp and Manning LiveProject
- Mentor students at Udacity AI and Machine Learning NanoDegree

How to get started in #datascience

👀🧵👇 See thread below 1. Data Science Starter Kit
This starter kit provides a framework that will help pinpoint you in the right direction and help you take your first steps
towardsdatascience.com/data-science-s…

Getting started on the Open #Bioinformatics Research Project initiative
👀🧵👇 See thread below 1. Watch the introductory video on the Open Bioinformatics Research Project initiative for:
- Intro to the initiative
- High-level overview of the dataset
- Ideas for which types of analysis to perform

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...

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.

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?

Cheat sheet that summarizes #DataScience in 10 pages
(Links in the comments below 👇) 2/ Link to the cheatsheet by Maverick Lin
github.com/ml874/Data-Sci…

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.

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…

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