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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

21 Reason why you should NOT become a Data Scientist

saurav kaushik
saurav kaushik Last Updated : 02 Dec, 2016
5 min read

Introduction

Time for some Friday Fun!

In last few years, the growth of Data Scientists has been following the growth in data . You can see it all around – people attending webinars, info sessions, undergoing multiple certifications and what not!

While everyone is going North, I thought Friday is a good time to take a moment and head South!

start

Source: Pinterest

 

Here is my list of 21 reasons why not to become a Data Scientist. If you have a few others to add, please do so!

1. Trump: When every data scientist in the world got it wrong!

1-1

Source: Uproaxx

If no one could predict such a macro phenomena, how would they know who is going to buy what?

 

2. At times you’ll get it horribly wrong. Did I just say ‘wrong’? Epic FAILURES! 

2-wrong

Remember the Twitter bot from Microsoft? She started saying great things about Hitler with 1 day of training on live data.

 

3. Ever participated in Kaggle? This is how I feel when you have to spend days getting that small increase in the 5th decimal place.

3-5th-place-decimal-accuracySource: megaaa.kyu.mobi

Not to mention the days when I don’t get even a slight increase in my scores irrespective of what I try.

 

4. And in case, I got awesome results from a black box model – this is how it feels! 

I have no clue what happened.

5. Torture the data and it’ll confess what you want it to.

5-torture-data

Source: Pintrest

At times, you can use data to create any point you want!

 

6. Why study so many algorithms when XGBoost always does the trick for you!

giphy

Source: Giphy

 

7. Automation! My job is to make machines replace me.

7-automation

Who on earth in sane mind wants that?

 

8. I must learn the languages that are going to pass out in 10 5 years anyway.

Or at times the ones, which many people have not even heard of!

 

9. Looks like I’m the only one who calculates the p-value of getting an increment everyday.

9-p-value

 

10. I get bashed by the CEO daily while everybody stands and watches.

10-ceo

Source: Knowyourmeme

What people don’t realise is that I’m the only person whom they take advice from!

 

11. Nothing is impossible until you start to explain Data Science at a social gathering.

337739692617cab84e748b4dde7e35ac

Source: Giphy

 

12. Human thinking ends at 3-D. My work starts at 100-D.

13-slicing-and-dicingSource: Giphy

 

13. Carrying cool laptops is a dream. Carrying servers is a necessity.

12-cool-laptops

Source: ASUS

 

14.   Only god knows the future. Whom am I to predict.

14-god

 

15.  Astrologers have been doing it for years.

16-astrologers

Source: Imgflip

 

16. Why spoil weekends over a hackathon/competition? Coldplay?

15-weekend

 

17. I am expected to teach domain expertise to domain experts.

17-domain-experts

Source: France Etiquette Limited

 

18. Any my expertise depends on where I’m giving the interview.

18-interview

Source: Pinterest

 

 19. Don’t doubt me. Alternative hypothesis stands true!

19-null-hypothisis

Source: Pinterest

 

 

20. No one knows who a Data Scientist is?

20

But everyone is searching for one!

 

21. The world is a strange place. And believe me, it’s not at all like what you think.

 

Disclaimer: This is just a fun take on life as a Data Scientist. We don’t intend to hurt any sentiments or endorse any products / services mentioned above. We truly believe, data science is here to stay, else we would not have bet our careers on it 🙂

end

 

End Notes

I hope you enjoyed reading this article as much as I did while writing it. If you are a data scientist, you will definitely relate to some of the points above. Which one did you relate to the most? Also, any point you think we missed in this list?

Share them with us in the comments below.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

saurav kaushik
saurav kaushik

Saurav is a Data Science enthusiast, currently in the final year of his graduation at MAIT, New Delhi. He loves to use machine learning and analytics to solve complex data problems.

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Responses From Readers

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Vinay
Vinay

At times this is me... 4. And in case, I got awesome results from a black box model – this is how it feels!

123

Nitesh
Nitesh

Haha good one!

123

Dev
Dev

Ha ha hilarious!!!

123

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