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-sβ¦
2. How to Master Python for Data Science
This article will have navigate you through the landscape of the Python language at their intersection with data science, which will help you get started in no time. towardsdatascience.com/how-to-master-β¦
3. How to Master Pandas for Data Science
This article explores the essential bare minimal knowledge that you need in order to master Pandas for getting started in data science. You can perform processing, wrangling and munging of data with pandas. towardsdatascience.com/how-to-master-β¦
4. How to Master Scikit-learn for Data Science
This article distills the essence of the scikit-learn library through the use of hand-drawn illustrations of key concepts as well as code snippets that you can use for your own projects. towardsdatascience.com/how-to-master-β¦
5. The Data Science Process
The data science process provides a systematic approach for tackling a data problem as well as recommended guidelines for approaching data science projects. towardsdatascience.com/the-data-scienβ¦
7. How to Build your First Machine Learning Model in Python
This article covers how to build your first machine learning model in Python. Particularly, you'll use traditional linear regression as well as other machine learning algorithms. towardsdatascience.com/how-to-build-yβ¦
8. How to Build a Simple Machine Learning Web App in Python
This article shows how to build a simple machine learning powered data science web app in Python using the streamlit library in less than 50 lines of code. towardsdatascience.com/how-to-build-aβ¦
10. How to Create and Deploy a Machine Learning App to Heroku
This article shows how to easily deploy a machine learning app created using @streamlit to the cloud using Heroku. towardsdatascience.com/how-to-create-β¦
11. How to Build a Simple Portfolio Website for FREE
Now that you have completed some projects, it is time to showcase them in your very own portfolio website that you built for free that is hosted on GitHub Pages. towardsdatascience.com/how-to-build-aβ¦
Thanks for reaching the end of this thread, but the learning does not stop here.
π Check out more of my tutorial blogs medium.com/@data-professor
π Check out more of my tutorial YouTube videos youtube.com/dataprofessor
β’ β’ β’
Missing some Tweet in this thread? You can try to
force a refresh
Getting started on the Open #Bioinformatics Research Project initiative
ππ§΅π 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
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? DataβModelβ Deployment
Hereβs a cartoon illustration Iβve drawn a while back:
The #machinelearning learning curve
ππ§΅π 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 thatβs where accountability steps in.
Hi friends, hereβs my new hand-drawn cartoon illustration βοΈ
Quickly deploy #machinelearning models
ππ§΅π 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/ Topics include:
- Overview of Data science
- Probability and Statistics
- Data cleaning
- Feature engineering
- Modeling
- Classical Machine learning
- Deep learning
- SQL
- Python data structures
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.