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 πŸ‘‡πŸ½πŸ§΅
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…
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…
3/ Coursera's Data Visualization with Python

The most important skills of successful data analysis is the ability to tell a compelling story by visualizing data and findings.

Spend 1 hour a day, everyday, and you'll be done in 2.5 weeks.

coursera.org/learn/python-f…
4/ Coursera's Applied Plotting, Charting, and Data Representation in Python.

This course will teach you reporting and charting using the matplotlib library

Spend 1 hour a day, everyday, and you'll be done in 4 weeks.

coursera.org/learn/python-p…
There you have it: a 13-week learning plan that will turn you into a rock star data analyst!
For deep learning and machine learning content, follow me!

For SQL resources follow: @nevrekaraishwa2

For more Python and ML follow: @SanthoshKumarS_ @GiftOjeabulu_@Sumanth_077

For generative AI follow @Saboo_Shubham_

β€’ β€’ β€’

Missing some Tweet in this thread? You can try to force a refresh
γ€€

Keep Current with Harpreet Sahota πŸ₯‘

Harpreet Sahota πŸ₯‘ Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

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

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @DataScienceHarp

Jan 3
🀯 Say goodbye to lifeless textbooks and hello to an exciting way to learn statistics! πŸ’ͺ

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?πŸ‘‡πŸ½πŸ§΅

#66DaysOfData
πŸ“šπŸ§ Who says learning statistics has to be boring?!

πŸ€“ 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.… Image
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. Image
Read 13 tweets
Jan 2
You don't need a bootcamp to get started in machine learning.

All you need are the right resources, discipline, and time.

Here are 6 of my favourite FREE resources to get you started.
1/ edX's Machine Learning with Python: A Practical Introduction

This course will give you all the tools you need to get started with supervised and unsupervised learning.

Time commitment: 4 hours a week and you're done in 2 weeks.

edx.org/course/machine…
2/ Cognitive class' Machine Learning with Python

You'll learn about real-life examples of Machine learning and how it affects society in ways you may not have guessed!

Time commitment: 7 hours a week and you'll be done in 2 weeks

cognitiveclass.ai/courses/machin…
Read 8 tweets
Jan 2
The curse of dimensionality is a major roadblock for machine learning practitioners.

But most don't fully understand it.

Don't be left in the dark - join me in this thread as I clarify and demystify this concept πŸ‘‡πŸ½πŸ§΅
The Curse of Dimensionality (let's just call it "The Curse") refers to problems that occur when you try to use statistical methods in high-dimensional space.
As the number of features (dimensionality) increases, the data becomes relatively more sparse, and often exponentially more samples are needed to make statistically significant predictions.
Read 7 tweets
Dec 30, 2022
Feature selection is a crucial part of building a good machine learning model.

But most data scientists don't think before they select features.

The fact is: feature selection in machine learning is not always necessary.

Here are 5 situation when you don't need it πŸ‘‡πŸ½πŸ§΅
1. You have a small dataset that doesn't have many features.

If the data you're using is small and doesn't have many features, you don't need to do feature selection.
2. The features are already carefully selected

If the features you're using have already been carefully chosen and are important for the task you are trying to do, you don't need to do feature selection.
Read 7 tweets
Dec 29, 2022
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 πŸ‘‡πŸ½πŸ§΅

#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/
2/ CognitiveClass' Python for Data Science

Spend 1 hour a day and you'll be done in a week.

cognitiveclass.ai/courses/python…
Read 7 tweets
Dec 29, 2022
The number one cause of machine learning model failure is data set drift.

Yet most data scientists and machine learning practitioners don't know why their data sets are drifting.

Here are 6 of the most common reasons for data set drift in machine learning πŸ‘‡πŸ½πŸ§΅
What is dataset drift? It's when the statistical properties of a dataset change over time, which can negatively impact the performance of a machine learning model.
1. Changes in the data distribution:

The distribution of the data used to train the model may change over time, leading to dataset drift. This could be due to changes in the underlying process that generates the data, or due to changes in the data collection process itself.
Read 9 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(