Roadmap to becoming Data Analyst in three months absolutely free. No need to pay a penny for this.

I have mentioned a roadmap with free resources.

A thread🧵👇

I have mentioned a roadmap with free resources.

A thread🧵👇

1. First Month Foundations of Data Analysis

A. Corey Schafer - Python Tutorials for Beginners:

B. StatQuest with Josh Starmer - Statistics Fundamentals:

C. Ken Jee - Data Analysis with Python

A. Corey Schafer - Python Tutorials for Beginners:

B. StatQuest with Josh Starmer - Statistics Fundamentals:

C. Ken Jee - Data Analysis with Python

2. Second Month - Advanced Data Analysis Techniques

A. Sentdex - Machine Learning with Python

B. StatQuest with Josh Starmer - Machine Learning Fundamentals

C. Brandon Foltz - Business Analytics

A. Sentdex - Machine Learning with Python

B. StatQuest with Josh Starmer - Machine Learning Fundamentals

C. Brandon Foltz - Business Analytics

1. Python Basics

Codecademy's Python Course (codecademy.com/learn/learn-py…)

Python for Everybody Course (py4e.com)

Codecademy's Python Course (codecademy.com/learn/learn-py…)

Python for Everybody Course (py4e.com)

2. Data Analysis Libraries

NumPy User Guide (numpy.org/doc/stable/use…)

Pandas User Guide (pandas.pydata.org/docs/user_guid…)

Matplotlib Tutorials (matplotlib.org/stable/tutoria…)

NumPy User Guide (numpy.org/doc/stable/use…)

Pandas User Guide (pandas.pydata.org/docs/user_guid…)

Matplotlib Tutorials (matplotlib.org/stable/tutoria…)

Yesterday I looked at the built-in `max()` function. Today, I'll explore #NumPy's version:

`np.max()` or `np.amax()`

There are many differences between the built-in function and NumPy's version. So let's explore…

/1

`np.max()` or `np.amax()`

There are many differences between the built-in function and NumPy's version. So let's explore…

/1

Here's yesterday's thread about the built-in `max()` if you'd like to start from there:

/2

/2

Interested in learning more about #DataScience, #MachineLearning or #AI? I’ve got a few places and resources for medics to start with. Anyone can do it with enough time and effort! Soon enough you’ll be making your own neural networks

1/16. A thread 🧵.

1/16. A thread 🧵.

2/16. Everyone has their preferences with programming languages. However if you’re starting from scratch, I highly recommend #Python. It is easy to learn, has a wide variety of applications and you will find it is much easier to perform even the most basic of statistics.

3/16. It also gives you access to multiple libraries that are used heavily by the machine learning community such as #Keras, #TensorFlow and #PyTorch.

A #Thread 🧵 on all the python libraries which are used in trading.

Save it for later.

#python #trading #stockmarket #trader #pythonprogramming #pythonlibraries #numpy #pandas #coding #Blog

Save it for later.

#python #trading #stockmarket #trader #pythonprogramming #pythonlibraries #numpy #pandas #coding #Blog

Where are all those values I need?

You can ask NumPy.

Just ask `np.where()`?

Let's see how this function works through an example…

👇🪡🧵

#python #numpy

1/

You can ask NumPy.

Just ask `np.where()`?

Let's see how this function works through an example…

👇🪡🧵

#python #numpy

1/

A teacher asks his students to take a test twice, a few weeks apart.

2/

2/

He thinks this is a fair system to use:

• If the difference in scores between the first and second tests is less than five marks, he'll keep the result of the first test

• Otherwise, he'll take the highest mark

3/

• If the difference in scores between the first and second tests is less than five marks, he'll keep the result of the first test

• Otherwise, he'll take the highest mark

3/

The `slice()` function returns a slice object representing the set of indices specified by `range(start, stop, step)`

Read more in the #Python docs here:

docs.python.org/3/library/func…

Read more in the #Python docs here:

docs.python.org/3/library/func…

If you want to learn about #bioimageanalysis I've written a free & open textbook that tries to help:

bioimagebook.github.io

Thanks to the wonder of @ExecutableBooks & other modern magic it's not quite like a normal book... (thread)

@OpenEdEdinburgh @NEUBIAS_COST @BioimagingNA

bioimagebook.github.io

Thanks to the wonder of @ExecutableBooks & other modern magic it's not quite like a normal book... (thread)

@OpenEdEdinburgh @NEUBIAS_COST @BioimagingNA

First, the book tries to cover the main concepts, independently of any software, in a practical way.

This includes common pitfalls & problems, like data clipping, that can doom analysis from the start (2/n)

This includes common pitfalls & problems, like data clipping, that can doom analysis from the start (2/n)

It also includes tricky stuff important for a lot of microscopy image analysis, like noise distributions & the signal-to-noise ratio... (3/n)

What's an image made of?

There are many correct answers.

But the most fascinating one is: << sines & cosines >>

Read on if you're intrigued👇🧵🪡

#python #images #fourier

There are many correct answers.

But the most fascinating one is: << sines & cosines >>

Read on if you're intrigued👇🧵🪡

#python #images #fourier

*Any* image can be reconstructed from a series of sinusoidal gratings.

A sinusoidal grating looks like this…

#sinusoidal #grating

A sinusoidal grating looks like this…

#sinusoidal #grating

NumPy is a powerful #python library that helps us compute operations on primarily numbers, faster. It is an important tool for data science. 💪

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

Here are 5 powerful #NumPy functions that will help you in your projects!

#Thread ☕

#MachineLearning #datascience #AI

Let's go! ⬇️

First let’s see the advantages of using NumPy : 📈

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

1️⃣ Uses low memory to store data.

2️⃣ We can create n-dimensional arrays.

3️⃣ Operations like indexing, broadcasting, slicing and matrix multiplication.

4️⃣ Finding elements in the array is easy.

5️⃣ Good documentation.

Encouraging! #AppleM1 Silicon (MBA) smokes my 2017 MBP15" i7 on #rstats #tidverse tidymodels hotel example, random forests (last fit 100 trees). Experimental arm-R build = extra speedup. Thanks @fxcoudert for gfortran build & @juliasilge @topepos + team for the nice API + DOC.

And it’s wonderful to see that essential R packages are working on the M1 platform.

Another implication might be that 4 cores are a good default for parallel processing with this configuration. The original tidymodels example would select 8 cores here. tidymodels.org/start/case-stu…

Want to become a Data Scientist? Here are some great resources that you should watch in order;Statistics & Linear Algebra Not Included 🧵

#100DaysOfCode #100DaysOfMLCode #DataScience #AI #MachineLearning #ArtificialIntelligence #ML #Coding #Coders #PyTorch #Tensorflow #Developer

#100DaysOfCode #100DaysOfMLCode #DataScience #AI #MachineLearning #ArtificialIntelligence #ML #Coding #Coders #PyTorch #Tensorflow #Developer

Part 1 of Microsoft's Intro to Python Series:

Gets you up and running in python and introduces you to the basics of setting up your development environment.

youtube.com/playlist?list=…

Gets you up and running in python and introduces you to the basics of setting up your development environment.

youtube.com/playlist?list=…

Continuation of the series above.

youtube.com/playlist?list=…

youtube.com/playlist?list=…

Will Slovakia avoid „Czech scenario“?

Health minister #marekkrajčí 16.10. first time publicly compared the second wave COVID-19 trends in Slovakia and Czech republic in a chart showing that Slovakia is following the scenario in Czech republic with a delay of 2~3weeks.:

Health minister #marekkrajčí 16.10. first time publicly compared the second wave COVID-19 trends in Slovakia and Czech republic in a chart showing that Slovakia is following the scenario in Czech republic with a delay of 2~3weeks.:

Trend chart from few days ago (22.10.) was even worse and clearly showing that Slovakia is repeating the Czech scenario:

Somewhere here we should be looking for motivation of the government in Slovakia and prime minister #igormatovič to solve the problem of rising second wave of epidemics and dark vision of collapsing health care by unusual way - #masstesting in combination with limited lockdown.

If you are using matplotlib in your day to day coding activity and didn't no the crux of it, I want to you take a look at this below thread. I'm sure you will learn and apply it.

So, here is what all we need to know about matplotlib, an excellent visualization library in python

So, here is what all we need to know about matplotlib, an excellent visualization library in python

I used Matlab for image processing for years. Tried to switch to Python 10 years ago but too many tools were still missing. Tried again 5 years ago and haven't touched Matlab ever since! The combination scikit-image + @ProjectJupyter was a real game-changer! A few more things:

On top of the great classics scientific stack (#numpy, #scipy, #pandas, #matplotlib) there's an entire ecosystem of new tools to handle all sorts of complex problems. E.g. #napari to visualize and annotate multi-dimensional data. @dask_dev to handle very large images.

Complex ML tools for image denoising like content-aware image restoration #CARE (github.com/csbdeep/csbdeep) or point-scanning super-resolution #PSSR (github.com/BPHO-Salk/PSSR) which are documented as Jupyter notebooks that really work "out of the box".