# Most recents (17)

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🧵👇
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
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
Python project ideas for beginners with source code

A thread 🧵👇
1. Calculator App
Source Code Link: github.com/programiz/Calc…
2. Expense Tracker
Source Code Link: github.com/prtm/Expense-T…
Python for data science beginners roadmap

A thread 🧵👇
1. Python Basics
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…)
🐍Python is easy!

You can learn enough #Python in 8-10 hours to pick the rest up with active practice.

Here are my top 5 #free places to catch the basics:

🧵👇
1/ Codeacademy (where I first learnt Python) -
lnkd.in/eZnqKD_x
2/ MITx 6.0001 (this series is a brilliant intro to CS) -
lnkd.in/eeBXsQqr
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
Here's yesterday's thread about the built-in `max()` if you'd like to start from there:

/2
Let's start by importing NumPy and creating an array to use to explore `np.amax()`

You can read more about using `np.random.default_rng()` to create a `Generator` instance, which is NumPy's preferred way of creating random numbers, in the quoted thread

<Code in ALT text>

/3
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 🧵.
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
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/
A teacher asks his students to take a test twice, a few weeks apart.

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/
Did you know that #Python has a `slice()` function! 🐍

Let's learn more about that now!

🧵👇🐍
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…
You can use the `slice()` function to created NAMED SLICES in #Python!

Here's an example:
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
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)
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
*Any* image can be reconstructed from a series of sinusoidal gratings.

A sinusoidal grating looks like this…

#sinusoidal #grating
It’s called a sinusoidal grating because the grayscale values vary according to the sine function.

If you plot the values along a horizontal line of the grating, you’ll get a plot of a sine function
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! ⬇️
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.
Function 1 : np.sort()

This returns a sorted copy of the array. The original array is not changed.

The arguments the function normally takes are -

1. The Array to be sorted.

2. Axis along which to sort.

3. The method/ algorithm used for sorting as “kind = ”.
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
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=…
Continuation of the series above.
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.:
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
1. In this thread we will go through some basic patterns and practises to help you get started with the library

It will basically help us plot the data into the figures and can contain axes, i.e. an area where points can be specified in x/y/z co-ordinates

First let's import it
2. The simple way of creating axes is using pyplot.subplots and we can Axes.plot to draw some data on the axes.

Below is how we can do it.
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".