THE PANDAS CHEAT SHEET
A well detailed guide to data wrangling using pandas

Reasons Why Data Goes Missing
Understanding the reason for the missing data in your dataset is important because it helps you determine the type of missing data and what you need to do about it. Lets get our brain to grasp this concept shall we?😁😁
Missing Completely at Random(MCAR): This is a fact that a certain missing value has nothing to do with its hypothetical value and values of other variables. eg:
You collect data on end-of-year holiday spending patterns. You survey adults on how much they spend annually on gifts for family and friends in dollar amounts.
You note that there are a few missing values in your holiday spending dataset. Some people started answering your survey but dropped out or skipped a question.
However, you note that you have data points from a wide distribution, ranging from low to high values.
Therefore, you conclude that the missing values aren’t related to any specific holiday spending amount range.

Missing at Random(MAR):This means that the propensity for a data point to be missing is unrelated to the missing data but related to some observed data. eg:
You repeat your data collection with a new group. You notice that there are more missing values for adults aged 18–25 than for other age groups.
But looking at the observed data for adults aged 18–25, you notice that the values are widely spread. It’s unlikely that the missing data are missing because of the specific values themselves.
Instead, some younger adults may be less inclined to reveal their holiday spending amounts for unrelated reasons (e.g., more protective of their privacy).

Missing Not at Random(MNAR): This is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). eg:
If some participants with low incomes avoid reporting their holiday spending amounts because they are low in your datast, then this is a MNAR problem

Deep Learning free courses

Introduction to Deep Learning
🎬 10 video lesson
Duration ⏰: 1 week worth of material
🏃‍♂️ Self paced
📄 Notes, 👨‍🏫 Labs and many more
☢️ Projects, Competitions
Teacher: Alexander Amini, Ava Soleimany
Source: MIT
🔗 Course link

Practical Deep Learning For Coders
🎬 8 video lessons
📔 Book Read online
📄 Notes, 👨‍🏫 Labs and many more
Duration ⏰: 7 weeks long, 10 hours a week
🏃‍♂️ Self paced
Teacher: Jeremy Howard
Source: fast.ai
🔗 Course link

Deep Learning
by Kaggle, on youtube
🎬 13 video lesson
Duration ⏰: 2 hours worth of material
🔗 Course link

Learn Deep Learning and TensorFlow, without a Ph.D.
🎬 8 video lesson
Duration ⏰: 3 hours worth of material
🏃‍♂️ Self paced
📄 Notes, slides
Teacher: Martin Görner
Source: Google Cloud
🔗 Course link

Explore Deep Learning for Natural Language Processing
🎬 9 video lesson
Duration ⏰: 7-8 hours worth of material
🏃‍♂️ Self paced
Resource: Trailhead
🔗 Course link

Deep Learning Summer School
🎬 35 video lesson
Duration ⏰: 35+ hours
🏃‍♂️ Self paced
Resource: deeplearning
🔗 Course link

Deep Learning Prerequisites: The Numpy Stack in Python V2
Rating ⭐️: 4.5 out of 5
Students 👨‍🎓: 2230
Duration ⏰: 1hr 59min
Created by Lazy Programmer Team, Lazy Programmer Inc.
🔗 Course link

AI 101 Video Presentation
presentation given by 👨‍🏫: MIT’s Brandon Leshchinskiy
🔗 Presentation link

Deep Learning in Life Sciences - Spring 2021
🎬 22 video lesson
Duration ⏰: 31 hours worth of material
🏃‍♂️ Self paced
Teacher: Manolis Kellis
Resource: Class Central
🔗 Course link

Intro to Deep Learning
by Kaggle
Use TensorFlow and Keras to build and train neural networks for structured data.
Duration ⏰: 4 hours
🔗 Course link


Deep Learning An MIT Press book 📚
Authers: Ian Goodfellow, Yoshua Bengio and Aaron Courville
🔗 Book link

#Deep_Learning #deeplearning #dl #machinelearning
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👉Join @bigdataspecialist for more👈

A GUIDE TO UNDERSTANDING HYPOTHESIS TEST

A Guide to Understanding Mathematics for Deep Learning

Amazing Free Resources on Data Science and Machine Learning for Beginners

1) Data Science for Beginners - A Curriculum
By: Azure Cloud Advocates at Microsoft
Stars ⭐️: 15K
Fork: 2.4K
Repo: https://microsoft.github.io/Data-Science-For-Beginners/#/?id=lessons

2) Machine Learning for Beginners - A Curriculum
By: Azure Cloud Advocates at Microsoft
Stars ⭐️: 38K
Fork: 7.4K
Repo: https://microsoft.github.io/ML-For-Beginners/#/

Head First SQL
Here's a brain friendly guide to learning SQL for beginners

Author:Lynn Beighley
Pages: 586
Link: Click Me!

Statistics Guide for Data Science
Learning Statistics for Data Science can be quite overwhelming for beginners without a Statistics background. One can get confused on which topics to learn or books to read up to equip their knowledge

You don't have to learn it all. Here are essential topics you can learn

1) Know what a p value is and its limitations
2) Linear Regression and its Assumptions
3) Different Statistical Distributions and when to use them
4) Mean, Variance for Normal, Poisson, and Uniform Distribution
5) Sampling Techniques and Common Designs(eg: A/B)
6) Bayes Theorems and it's application
7) Measurements and Interpretation of Confidence Intervals
8) Logistics Regressions and ROC curves
9) Resampling(Cross Validation and Bootstrapping)
10) Tree Based Models

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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

Where to find Data for Machine Learning

High quality data is key for building useful machine learning models. Models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.

This article gives a concise explanation on finding the right data for your models.

https://towardsdatascience.com/where-to-find-data-for-machine-learning-e375e2a515c8

SQL Free Resources
Looking to learn SQL for free? Here is a curated list of websites you can use to upgeade your SQL skill level or practice writing queries. Remember SQL is a necessary skill to have in your toolkit as a data professional.

1. W3 Schools

https://w3schools.com/sql

2. SQL Zoo

http://sqlzoo.net

3. SQLBolt

http://sqlbolt.com

4. Khan Academy

https://khanacademy.org/computing/computer-programming/sql

5. FreeCode Camp

https://youtu.be/HXV3zeQKqGY

To Practice what you have learned and build your skill at hte same time , you can use these:

6. Hacker Rank

https://hackerrank.com/domains/sql

7. SQL Murder Mystery Game

https://mystery.knightlab.com

#datascience #SQL

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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

Machine Learning with Python: Zero to GBMs

This is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. This is a self-paced course where you can:

👌Watch hands-on coding-focused video tutorials
👌Practice coding with cloud Jupyter notebooks
👌Build an end-to-end real-world course project
👌Earn a verified certificate of accomplishment
👌Interact with a global community of learners
👌You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets

Link: https://jovian.ai/learn/machine-learning-with-python-zero-to-gbms

Text Classification with TensorFlow

This is an intermediate-level Python course taught by MIT grad student Kylie Ying. You can code along at home in your browser.

You'll use TensorFlow to train Neural Networks, visualize a diabetes dataset, and perform Text Classification on wine reviews. (2 hour YouTube course)

Link: https://www.freecodecamp.org/news/text-classification-tensorflow/

Introduction to Machine Learning, IIT Kharagpur

🆓 Free Online Course
💻 44 Lecture Videos
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. S. Sarkar

🔗 https://nptel.ac.in/courses/106105152

The Scikit-Learn Guide

Looking to improve your knowledge on machine Learning ALgorithms, there's no better place to start from than to check the sklearn documentation

There is alot of interesting information you can gain there

https://scikit-learn.org/stable/

Want to make sure your Spark applications reach the best performance?

We invite you to our Dynamic Talks #90 | Spark performance mastery!
⏰ Date and time: July 20, 6:30 pm (CET)

The speaker is Iñigo San Aniceto Orbegozo, Staff Big Data Engineer at Grid Dynamics.

💻 Participation is free but registration is required: https://forms.gle/UVvfWG5LeZAXTuNQ6

More about event: https://fb.me/e/1U9Vq4epw

Just wanted to share this 👆 here as well in case somebody is interested.