Discover and read the best of Twitter Threads about #66DaysOfData
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🤯 Say goodbye to lifeless textbooks and hello to an exciting way to learn statistics! 💪
I have a masters degree in statistics, but these 11 books taught me more about how statistics in the real world than any course I've taken.
Have you read any of them?👇🏽🧵
#66DaysOfData
I have a masters degree in statistics, but these 11 books taught me more about how statistics in the real world than any course I've taken.
Have you read any of them?👇🏽🧵
#66DaysOfData
📚🧠Who says learning statistics has to be boring?!
🤓 The Manga Guide to Statistics makes it fun and easy to learn all the basic concepts, with entertaining examples and applications.
Get it here:nostarch.com/mg_statistics.…
🤓 The Manga Guide to Statistics makes it fun and easy to learn all the basic concepts, with entertaining examples and applications.
Get it here:nostarch.com/mg_statistics.…
Learn to calculate regression equations and perform hypothesis tests with The Manga Guide to Regression Analysis.
You also learn: simple, multiple, and logistic regression to predict iced tea orders and bakery revenues, and calculate confidence intervals and odds ratios.
You also learn: simple, multiple, and logistic regression to predict iced tea orders and bakery revenues, and calculate confidence intervals and odds ratios.
If I had to learn data analysis with Python in 2023, here's how I would do it.
Get ready to transform your data analysis skills with these highly recommended resources.
#66DaysOfData 👇🏽🧵
Get ready to transform your data analysis skills with these highly recommended resources.
#66DaysOfData 👇🏽🧵
1/ Cognitive Class: Data Analysis with Python
You'll learn how to prepare data for analysis, perform statistical analyses, create data visualizations, predict trends from data, and more!
Spend 30 minutes a day, everyday, and you'll be done in 3 weeks.
cognitiveclass.ai/courses/data-a…
You'll learn how to prepare data for analysis, perform statistical analyses, create data visualizations, predict trends from data, and more!
Spend 30 minutes a day, everyday, and you'll be done in 3 weeks.
cognitiveclass.ai/courses/data-a…
2/ Udacity's A/B Testing Course
This course will cover the design and analysis of A/B tests.
Spend 1 hour a day, everyday, and you'll be done in 4 weeks.
udacity.com/course/ab-test…
This course will cover the design and analysis of A/B tests.
Spend 1 hour a day, everyday, and you'll be done in 4 weeks.
udacity.com/course/ab-test…
Machine learning and Python go hand in hand.
Ready to take the first step towards a rewarding career in machine learning?
These 4 resources will help you learn Python and get started 👇🏽🧵
#100DaysOfCode #66DaysOfData #DeepLearning
Ready to take the first step towards a rewarding career in machine learning?
These 4 resources will help you learn Python and get started 👇🏽🧵
#100DaysOfCode #66DaysOfData #DeepLearning
1/ Python Principles
I've never seen anything like this course.
This is a text based course with an interactive coding environment that will teach you all the basics of Python.
There's lots of challenges and exercises, too.
This should take 2 weeks.
pythonprinciples.com/lessons/
I've never seen anything like this course.
This is a text based course with an interactive coding environment that will teach you all the basics of Python.
There's lots of challenges and exercises, too.
This should take 2 weeks.
pythonprinciples.com/lessons/
2/ CognitiveClass' Python for Data Science
Spend 1 hour a day and you'll be done in a week.
cognitiveclass.ai/courses/python…
Spend 1 hour a day and you'll be done in a week.
cognitiveclass.ai/courses/python…
Here’s a cartoon illustration I’ve drawn a while back.
The #MachineLearning Learning Curve
👀🧵👇 See thread below
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.
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.
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.
12 data science communities you can join to enrich your data learning journey & collaborate with like-minded people📊📈
A🧵↓
A🧵↓
List of 12 communities↓
1) #/66DaysOfData
2) Data leaders
3) Data literacy project
4) IBM Datasci community
5) Kaggle
6) Makeover Monday
↓ (Credit courtesy: @DataKwery)
1) #/66DaysOfData
2) Data leaders
3) Data literacy project
4) IBM Datasci community
5) Kaggle
6) Makeover Monday
↓ (Credit courtesy: @DataKwery)
7) ODSC
8) R-ladies
9) R-studio community
10) Tableau community
11) TidyTuesday community
12) WomanWhoCode
Click the link below to access all community links at once compiled by @DataKwery
🔗 datakwery.com/communities/?u…
8) R-ladies
9) R-studio community
10) Tableau community
11) TidyTuesday community
12) WomanWhoCode
Click the link below to access all community links at once compiled by @DataKwery
🔗 datakwery.com/communities/?u…
Let's talk about the 8 most useful @Docker commands for working with Docker containers.
#MLOps #100DaysOfCode #MachineLearning #66DaysOfData
🧵 👇🏽
#MLOps #100DaysOfCode #MachineLearning #66DaysOfData
🧵 👇🏽
1/ docker container run
You use this command to start a new @Docker container.
It accepts an image and a command as its arguments. The image is used to create the container. The command is the application the container will run when it starts.
You use this command to start a new @Docker container.
It accepts an image and a command as its arguments. The image is used to create the container. The command is the application the container will run when it starts.
For example, running the following command will start an ubuntu container and run the bash shell
docker container run -it ubuntu /bin/bash
Oh yea, the -it runs an interactive terminal. It attaches the containers terminal to your terminal.
docker container run -it ubuntu /bin/bash
Oh yea, the -it runs an interactive terminal. It attaches the containers terminal to your terminal.
Do data scientists need to know @Docker?
Most definitely. But you don't need to know everything. When it comes to Docker images, here are the five commands you absolutely must know.
#100DaysOfCode #66DaysOfData
🧵👇🏽
Most definitely. But you don't need to know everything. When it comes to Docker images, here are the five commands you absolutely must know.
#100DaysOfCode #66DaysOfData
🧵👇🏽
1/ docker image pull
This command downloads images. You can pull images from repositories inside of remote registries. By default, images are pulled from @Docker Hub.
This command will pull images tagged as `latest` by default.
This command downloads images. You can pull images from repositories inside of remote registries. By default, images are pulled from @Docker Hub.
This command will pull images tagged as `latest` by default.
2/ docker image ls
This command lists all of the images stored in your @Docker hosts local image cache. You can use the `--digest` option to get the SHA256 hash code of the image as well.
This command lists all of the images stored in your @Docker hosts local image cache. You can use the `--digest` option to get the SHA256 hash code of the image as well.
Three facts you need to realize about data science:
1. Learning is not enough
2. You need experience to get a job
3. You need principles for working
#datascience #66daysofdata
1. Learning is not enough
2. You need experience to get a job
3. You need principles for working
#datascience #66daysofdata
1/ Learning is not enough
A hiring manager isn’t going to be able to open your head, peek inside your brain, and verify if you know how to do this thing or that, or how well you understand this, that or the other.
That’s why you need to do a project.
A hiring manager isn’t going to be able to open your head, peek inside your brain, and verify if you know how to do this thing or that, or how well you understand this, that or the other.
That’s why you need to do a project.
2/ You need experience to get a job
But that doesn’t mean you need a job to get experience.
Data is everywhere.
You don’t need to be in a job at a company to get access to data.
If you know where to look and how to search for it, you’ll quickly see that it is everywhere.
But that doesn’t mean you need a job to get experience.
Data is everywhere.
You don’t need to be in a job at a company to get access to data.
If you know where to look and how to search for it, you’ll quickly see that it is everywhere.
This story took 3 months to create
A Deep Learning beginner to top 1% on Kaggle's latest Computer Vision competition
Here's my journey 👇
A Deep Learning beginner to top 1% on Kaggle's latest Computer Vision competition
Here's my journey 👇
Competitions are a great place to learn from!
And beginners actually get the most value out of learning from comps.
If you're one of them, don't be intimidated and jump right in.
And beginners actually get the most value out of learning from comps.
If you're one of them, don't be intimidated and jump right in.
Humble beginnings
I first started learning DL when the competition started - October.
I picked up the amazing "Deep Learning with Pytorch" book.
I first started learning DL when the competition started - October.
I picked up the amazing "Deep Learning with Pytorch" book.
Resources for getting started in #DataScience
Where to start? What to read? What to learn? I got you covered.
🧵 See thread below 👇
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…
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…
3/ Create your own #datascience learning curriculum
Everyone's interest or needs are different. Therefore the destiny of everyone's learning journey is also different.
Create your own curriculum. Here's how:
👉 Video
👉 Blog towardsdatascience.com/how-to-create-…
Everyone's interest or needs are different. Therefore the destiny of everyone's learning journey is also different.
Create your own curriculum. Here's how:
👉 Video
👉 Blog towardsdatascience.com/how-to-create-…
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...
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
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
Here’s a cartoon illustration I’ve drawn a while back:
The #machinelearning learning curve
👀🧵👇 See thread below
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.
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.
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.
Day 58 of #66DaysOfData Day 11 of UMAP. Here's the main algorithm behind t-SNE, LargeVis and UMAP, with a few notes on key differences. BAM! (1 of 13 panels!!!!)
(2 of 13)
(3 of 13)
15 Days roadmap to master #Python basics for #DataScience & #MachineLearning without having any Prior Experience.
[ Join the #100DaysOfCode & #66daysofdata challenge to keep yourself motivated ]
Thread 🧵👇
[ Join the #100DaysOfCode & #66daysofdata challenge to keep yourself motivated ]
Thread 🧵👇
Few things to keep in mind before starting
- Learn By Doing, Practicing & Not Just Reading
- Code By Hand [very effective]
- Share, Teach, Discuss and Ask For Help
- Use Online Resources
- Be consistent
- Learn to Use Debugger
- Learn By Doing, Practicing & Not Just Reading
- Code By Hand [very effective]
- Share, Teach, Discuss and Ask For Help
- Use Online Resources
- Be consistent
- Learn to Use Debugger
I have done all the below-mentioned concepts as part of the #100DaysOfCode challenge and the code can be found in my @github profile.
[Projects & exercise not done. let me know if you want the solutions]
github.com/Piyal-Banik/10…
[Projects & exercise not done. let me know if you want the solutions]
github.com/Piyal-Banik/10…
1. Towards Data Science
TDS is a Medium publication having audience-oriented content about Data Science, along with blogs on related fields such as Machine Learning, Programming, Visualization, and Artificial Intelligence.
towardsdatascience.com
TDS is a Medium publication having audience-oriented content about Data Science, along with blogs on related fields such as Machine Learning, Programming, Visualization, and Artificial Intelligence.
towardsdatascience.com
2. Data Science Central
DSC is one of the leading repositories of Data Science content that is regularly updated with the latest trends across domains such as Artificial Intelligence, Machine Learning, Deep Learning, Analytics, Big Data, and much more.
datasciencecentral.com
DSC is one of the leading repositories of Data Science content that is regularly updated with the latest trends across domains such as Artificial Intelligence, Machine Learning, Deep Learning, Analytics, Big Data, and much more.
datasciencecentral.com
