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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Hilarious Jokes & Videos on Statistics and Data Science

A
Analytics Vidhya Last Updated : 21 Dec, 2015
5 min read

Introduction

 Take it Easy. Data Science is Fun-tastic!

If you love data science, you’d find many aspects to it. A month back, I found 10 Best Movies on Machine Learning. A week later, I found 7 Documentaries on Statistics. It’s time to explore the funny side of analytics.

I’ve compiled a list of best hilarious jokes (including images, videos) based on numbers, statistics, big data, machine learning. Hope you’d enjoy reading them. During this process, I found many jokes. In fact too many. Hence, I’ve listed the best ones I liked.

P.S. To unlock the funny aspect of these jokes, you should have basic knowledge of data science related topics. All The Best!

jokes data science statistics machine learning

 

Just Videos

Imagine a world without proper search!

Duration – 1:32 mins

 

A ‘Thrilling’ Style of Overfitting in Machine Learning

Duration – 5:10 mins

 

Geoff Hinton knows how the brain works

Duration – 2:15 mins

 

 

Are you a Statistician / Data Analyst? You are Funny!

A statistician’s wife had twins. He was delighted. He rang the minister who was also delighted. “Bring them to church on Sunday and we’ll baptize them,” said the minister. “No,” replied the statistician. “Baptize one. We’ll keep the other as a control.”

———————————-:D—:D—:D—:D—————————

Two statisticians were traveling in an airplane from LA to New York. About an hour into the flight, the pilot announced that they had lost an engine, but don’t worry, there are three left. However, instead of 5 hours it would take 7 hours to get to New York.

A little later, he announced that a second engine failed, and they still had two left, but it would take 10 hours to get to New York.

Somewhat later, the pilot again came on the intercom and announced that a third engine had died. Never fear, he announced, because the plane could fly on a single engine. However, it would now take 18 hours to get to New York.

At this point, one statistician turned to the other and said, “Gee, I hope we don’t lose that last engine, or we’ll be up here forever!”

———————————-:D—:D—:D—:D—————————

Patient: “Will I survive this risky operation?”
Surgeon: “Yes, I’m absolutely sure that you will survive the operation.”
Patient: “How can you be so sure?”
Surgeon: “9 out of 10 patients die in this operation, and yesterday died my ninth patient.”

———————————-:D—:D—:D—:D—————————

One day there was a fire in a wastebasket in the office of the Dean of Sciences. In rushed a physicist, a chemist, and a statistician. The physicist immediately starts to work on how much energy would have to be removed from the fire to stop the combustion.

The chemist works on which reagent would have to be added to the fire to prevent oxidation. While they are doing this, the statistician is setting fires to all the other wastebaskets in the office.

“What are you doing?” the others demand. The statistician replies, “Well, to solve the problem, you obviously need a larger sample size.”

———————————-:D—:D—:D—:D—————————

Three statisticians went out hunting, and came across a large deer. The first statistician fired, but missed, by a meter to the left. The second statistician fired, but also missed, by a meter to the right. The third statistician didn’t fire, but shouted in triumph, “On the average we got it!”

———————————-:D—:D—:D—:D—————————

A statistics major was completely hung over the day of his final exam. It was a true/false test, so he decided to flip a coin for the answers. The statistics professor watched the student the entire two hours as he was flipping the coin … writing the answer … flipping the coin … writing the answer.

At the end of the two hours, everyone else had left the final except for the one student. The professor walks up to his desk and interrupts the student, saying, “Listen, I have seen that you did not study for this statistics test, you didn’t even open the exam. If you are just flipping a coin for your answer, what is taking you so long?” The student replies bitterly (as he is still flipping the coin), “Shhh! I am checking my answers!

———————————-:D—:D—:D—:D—————————

A company manager is flying across the desert in a hot air balloon when he realizes he is lost. He calls down to a man riding a camel below him and asks where he is.

The man replies “You’re 42 degrees and 12 minutes, 21.2 seconds north, 122 degrees , 10 minutes west, 212 metres above sea level, heading due east by north east.”

“Thanks,” replies the balloonist. “By the way, are you a data analyst?”

“Yes,” replies the man, “how did you know?”

“Everything you told me was totally accurate, you gave me way more information than I needed and I still have no idea what I need to do.”

“I’m sorry,” replied the camel-riding analyst. “By the way, are you a company manager?”

“Yes,” said the balloonist, “how did you know?”

“Well,” replied the analyst, “You’ve got no idea where you are, no idea what direction you’re heading in, you got yourself into this fix by blowing a load of hot air, and now you expect me to get you out of it.”

———————————-:D—:D—:D—:D—————————

Question: What’s the difference between an introverted data analyst & an extroverted one?

Answer: the extrovert stares at YOUR shoes.

 

‘Innocent’ Dilbert & XKCD Special

d6

 

d1

 

d8

 

d2

 

j10

 

d7

 

d5

 

d4

 

End Notes

I enjoyed compiling this funny ‘data’ on data science & statistics. After all, “Statistics may be dull, but it has its moments.” My idea behind this article was to introduce you to the ‘lighter’ side of data science. Just to prove that intensive topics can be funnier too. Hence, don’t get too busy in your work and you fail to explore its entertaining side.

In this article, I’ve listed the best hilarious jokes, videos, memes on data science and statistics. I hope you liked this post.

Did I miss anything? Do share your favorite funny jokes/images/videos in data science. Share you views / suggestions in the comments section below.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

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A
Analytics Vidhya

Analytics Vidhya Content team

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Responses From Readers

Clear

Tuhin
Tuhin

Good one Manish. Liked it.

123

Analytics Vidhya Content Team
Analytics Vidhya Content Team

I'm glad you liked it. Thank you Prof. Tuhin :)

123
AND
AND

http://2378nh2nfow32gm3mb25krmuyy.wpengine.netdna-cdn.com/wp-content/uploads/2014/05/Type-I-and-II-errors1-625x468.jpg Follow this link, it is also a good one.

123

JB
JB

I liked this. Especially the "thriller" video.

123

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