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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

DATAMIN – Unveiling the World’s Biggest Online Data Science Quizzing Platform

Tavish Srivastava
Tavish Srivastava Last Updated : 14 Jun, 2020
4 min read

We are thrilled to announce the launch of the world’s biggest online data science quizzing platform: Datamin!

datamin

Do you feel some of your peers just do better in their data science career for reasons you can’t explain? 

With all the interest of industries, venture capitalists, governments and various other functions, data science has expanded to a very wide domain. With this expansion, data scientists’ today need to demonstrate a huge array of skills to excel in the role.

A person would need to be:

  • A statistician to interpret probabilistic inferences
  • An expert programmer to code AI
  • An efficient presenter to present complex ML techniques in front of business audience
  • A person with strong quantitative skills to size and prioritize new opportunities
  • A domain expert to integrate AI with business processes
  • A data architect to comprehend complex data models etc.

The list keeps on getting bigger as the domain expands. This informative image captures this multi-faceted role:

There’s both good news and bad news here.

The Good News is that you can pick up any skill you want if you have the right mindset, the discipline and the determination to practice it daily. The Bad News? Well there are broadly two main challenges aspiring data scientists face:

  • They often can’t figure out and pinpoint the area(s) in their skillset which needs improvement. The ambiguity is dangerous to your career trajectory in the data science field
  • There might be certain skills you will initially struggle to pick up because of a lack of experience with it

The second challenge, of a person coming from a non-data science background, is all too common. I have seen people give up halfway to their goal because they feel they won’t ever improve their existing skillset. For instance, a person with no engineering background might feel he/she might never become an awesome data scientist because he might never become good at programming.

Sounds like a familiar situation?

 

So, how do we overcome these 2 challenges?

Here are a couple of solutions for these challenges that I will explain with relatable analogies:

Everyone knows Vitamins are essential for our body. If you struggle with any physical/mental illness, do you simply start taking Vitamin pills? No, right? You get your diagnostic tests for various Vitamins and then start the cure.

Similarly, a data scientist needs to master a set of skill elements. To understand which element is impeding his/her growth, the data scientist needs to go through a rigorous diagnostic test on various data science aspects.

Once you identify your knowledge gap, now what? What if the skill you need to master looks unachievable or simply takes too much time that you don’t have?

Turns out that the time to learn a new skill OR improve on an existing one is mainly dependent on our state of mind.

Looks too abstract? Let me illustrate. In your lifetime, when do you think you had the steepest learning curve? Probably you would have guessed right – when you were a toddler. Can you think of a reason? The main reason was the state of mind. At that age, every new lesson was a game. And learning through a game turns out to be the fastest way you can learn any new skill regardless of your age.

We at Analytics Vidhya, combined these concepts and leveraged our expertise in data science to create a new product called Datamin – Your Daily Dose of Data Science. We are now ready to test the waters and launch the Beta version of the product. 

Here’s Analytics Vidhya’s Founder and CEO with the launch announcement:

 

 

Datamin – Your Daily Dose of Data Science

Datamin is a unique data science quizzing platform that has been designed by industry experts. Datamin will:

  • Evaluate you on various data science dimensions
  • Accelerate your learning by giving relevant feedback on each user attempt
  • Engage you with live community chat and features like challenge your friend, and
  • Identify in real-time your opportunity areas with reading suggestions

These quizzes will be a week long with almost no limits on the number of questions you attempt. If this was not enough, every competition ends with prizes worth INR 200,000 (USD 3000).

The questions you will face will test you broadly on three main categories that are most relevant for data scientists:

  • Machine Learning
  • Statistics
  • Programming

The beta platform comes with a lot of community engagement levers and modes of giving us LIVE feedback.

 

Rules of the Game

  1. You can register anytime of the week while the quiz is running. We recommend you register as early as possible to get maximum number of attempts
  2. You can answer unlimited number of questions. We have only put a limit on your daily attempt at a very high number to restrict any bots.
  3. Every right answer gives you +3 points and wrong answer gets you -1
  4. Time you get to answer any question is 90 seconds
  5. You can check your standing on the leaderboard at any time
  6. Skipping a question does not get you any penalty or reward
  7. Top positions on the leaderboard after each week competition will be rewarded with cash prize and job opportunities

 

Gear up for the launch of Datamin

DATAMIN goes LIVE at 3 pm IST on 25 July 2019 (Thursday). Wear your data science hat, get your gaming self together and get ready for a super fast learning drive.

Make sure you register as soon as possible to allow yourself maximum time and maximum attempts. Note that there are no limits on the total number of questions you can attempt, so the only way to maximize your score is to maximize your attempts with high accuracy. Best of luck and happy learning!

data science questionsdata science quizDataminmachine learningmachine learning quizstatisticsstatistics quiz
Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Chirag
Chirag

The Keep Me Updated link does not seem to work! It is stuck on sibforms.com.

123

Pranav Dar
Pranav Dar

Hi Chirag - Datamin is now LIVE so you can go ahead and start your journey. :) All the best and let us know your experience.

123
Olamide Ogunmola
Olamide Ogunmola

502 ad Gateway. That's the error message the website gives when you try to register. Is it fully functional?

123

Pranav Dar
Pranav Dar

Hi Olaminde, The site is working perfectly fine from our end. Do let me know if you're still facing issues and we will resolve it for you.

123
Maritza Borunda
Maritza Borunda

I get an error message when trying to register with my US-based cell phone number. Is the contest open only to residents of India?

123

Pranav Dar
Pranav Dar

Hi Maritza, The contest is open for everyone around the world. :) Can you try to input your phone number with a +1 prefix and without any punctuations, brackets, etc.? This should work and you'll be able to register.

123

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Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
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