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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

DSAT – First Ever Adaptive Learning Platform for Data Science Professionals

Tavish Srivastava
Tavish Srivastava Last Updated : 03 Dec, 2019
5 min read

“Every once in a while, a revolutionary product comes along that changes everything.” – Steve Jobs

We are thrilled to announce the launch of the first-ever adaptive learning platform for data science professionals around the world – DSAT, or the Data Science Adaptive Test!

Here’s one of the most common scenarios we have seen in the data science industry:

Hiring Manager – “It’s been 6 months since we opened up this job position. All you have got me are at best round 1 rejects. We have already lost more than $1 Million in opportunity.”

Human Resource Partner – “Your demand is just too specific. You need a data science professional with an understanding of the banking domain, a master of the digital marketing function and ability to work with both Big Data and unstructured data. I have already given you my best leads from the pool. All the future leads will be even worse. You need to relax your criterion to close this position.”

Believe me, this is not an exaggeration. I have seen far more aggressive discussions and frustration in the hiring process in the data science industry.

With domains like Data Science, Cognitive Tech, Information Tech, Software Engineering, Semiconductors, Data Architecture, etc. all converging to solve real-world problems, jobs are increasingly becoming more skill-demanding and recruitment is becoming quite a complex problem to solve.

This learning and recruitment challenge, combined with about a 20% quarterly increment in job openings, accounts for huge economic losses across industries.

So how can we cope up with such an increasingly complex challenge of learning and recruitment? To answer this question, we need to take a step back and zoom out.

Note: If you prefer visual to verbal, here’s a quick video explaining DSAT, the logic behind it, and why it’s a revolutionary product for both data science professionals as well as hiring managers:

 

Reflection on Historically Solved Problems

Back in 1953, every business school was facing the same challenge of low application pool, a poor and extremely operations intensive selection process, and a very difficult time establishing a comparison of selected candidates across these schools.

Necessity is the mother of invention – the top 9 business schools including Harvard, University of Chicago/Michigan/Pennsylvania, etc. met for this burning need and established a state-of-the-art admission test that could solve all these challenges at once.

But how can a single test solve everything?

It all boils down to quantitative metrics that can be easily measured. What used to be a top 4 city selection process can now scale across the United States, penetrating even rural areas. All the candidates are scored on a single metric that can judge how good/bad the candidate is.

With this score, the fierce battle to get the best candidate came in the competitive market instead of the agency model that existed. In 1997, they even solved the only challenge left in this admission process – evaluation across the entire pool through a computer adaptive test – a state of the art technology at that time.

Unlike fixed tests, this computer adaptive test can distinguish every candidate from each other regardless of their position in the crowd. Fast forward 67 years – now 250,000+ tests are taken across 100+ countries. What made this test so popular? Standardized Adaptive Test on globally agreed subjects!

The same challenge was solved through GRE in other educational domains, through CAT in the Indian Business Education industry, through SAT in US undergrad admission, etc. None of these exams is pure computer-adaptive yet – but all of them strive to get there someday.

 

What Makes Computer-Adaptive Tests so Difficult to Build?

There is a significantly complex process that goes into creating and designing a computer adaptive test.

A simple (fixed) test of 40 questions will need you to answer those 40 questions only. But an adaptive test of 40 questions will need at least 400 questions with various difficulty levels. Tagging these difficulty levels will in-turn need at least 1000 attempts per question. So, you basically need 400 questions with about 4,00,000 attempts from more than 1000 individuals.

This should give you a good flavor of what it takes to make an adaptive test and why so many standardized tests have not scaled up to being truly adaptive.

DSAT

So the question we wanted to answer was – can we bring this intelligence/experience in our industry to solve our challenge of learning and recruiting?

We rolled out a product a few months back called Datamin to validate if a single product can solve both the learning and recruiting challenges of the industry. Long story short – the answer is YES!

We believe that a single product similar to GMAT can revolutionize this entire industry.

After the successful launch of Datamin, we wore our community hat and thoughtfully built a product for our community that could do both. Here is what our community was able to accomplish with Datamin:

  • 10,000+ Users
  • 500,000+ Questions Attempted
  • 99% Questions now tagged as unambiguous

Imagine the kind of intelligence we were able to create. We used this intelligence to upgrade our plain vanilla but addictive Datamin to the first-ever data science adaptive test – DSAT. DSAT stands for Data Science Adaptive Test.

 

How will DSAT solve the existing recruitment and learning challenges of the Data Science industry?

We built an illustrated infographic to explain:

DSAT

 

What skills does the DSAT test?

There are primarily four skills you will work on (and be evaluated on) in the DSAT. Here’s the lowdown on each skill:

DSAT

 

How will the DSAT evolve over time and how useful will it be for you?

There are multiple avenues the DSAT will open up for you. Here are two broad ones that we want to highlight. At the very least, you get to work on your data science skills, evaluate them, understand which areas need improvement, and then work on them:

DSAT_career

The other scenario, catering to everyone from aspiring data scientists to hiring managers, is all about getting noticed by potential data science recruiters. Work on your DSAT score, highlight it to hiring managers, and stand a chance to land your dream data science role:

 

End Notes

DSAT is a product which is truly democratic – for the people, by the people, and of the people.

We have done our job by creating the underlying technology that can foster a higher degree of learning. Now, it’s your turn to use it to the fullest.

The score is as good as its adoption. Any community is as good as the people within. Hence, make sure your DSAT score reflects your true potential, and if you believe this score does justice to your knowledge, make sure to bring in your peers to have more credibility to your score.

Get started with DSAT today!

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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