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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

10 Most Popular Data Science Articles on Analytics Vidhya in 2020

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Analytics Vidhya Last Updated : 08 Feb, 2023
7 min read

Overview

  • Here is a list of the top 10 articles published this year by Analytics Vidhya
  • The articles have been ranked in descending order, on the basis of their views
  • Feel free to add more articles in the comment section that you feel the community should read

 

Introduction

Writing is the best way to improve retention. Converting your learnings into your own words not only leads to a better understanding but also leads to innate observation, which in turn leads to improving curiosity.

In short, writing elevates your learning process to unfathomable levels.

Writing is at the core of Analytics Vidhya’s principles. We have always tried to deliver the best content possible and 2020 was no different for us. With more than 500+ articles published this year, the writing journey never stops for us.

In this article, we highlight the 10 most read articles by the Data Science community on our blog, published this year.

So let’s get the ball rolling!

 

1. 6 Open Source Data Science Projects to Impress your Interviewer

The best performing article on our blog is the article that is based on the most fundamental questions you ask a data scientist or data analyst in an interview-

“How many data science projects have you completed so far?”

github_open_source_data_science_projects

The answer makes all the difference. Data Science is not a field where theoretical understanding helps you start apart. It is the projects you do and the practice you have that determines your probability of success.

Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.

But which data science project should you choose? We at Analytics Vidhya love collecting the best data science projects every month and in this article, we have collected and the best open-source data science projects for the month of June 2020.

You can check it out here.

2. Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization

data science articles - feature_scaling

Feature scaling helps you convert various variables having myriad units of measurement such as kilogram, Rupees, Years, etc into unitless measures. But the question is which method of Scaling to use?

One of the obstacles that every data scientist faces is the dilemma to choose between Normalization and Standardization. The majority of the courses do not focus on this topic. Feature scaling is one of the most important preprocessing steps and playing around with this concept without proper knowledge may lead to an inaccurate or biased model.

The article also talks about why some machine learning models improve drastically with feature scaling while others do not even move a little.

You can read the article here.

 

3. 22 Widely Used Data Science and Machine Learning Tools in 2020

“What are the best tools for performing data science tasks? And which tool should you pick up as a newcomer in data science?”

data_science_tools - data science articles

The essence of the article is covered in the question asked above. Once we identify what to learn at a personal level, or do at a professional level with the data, we need to identify the tools that best suit the task. This article is all about identifying the best fitting tool.

Data science is a very vast topic with each spectrum requiring the data to be dealt with in a unique way. And since your models are prone to have a huge impact on the decisions of the organization, it is really important to identify which tools to use.

The article is divided into 2 parts, with the first part focussing on tools for handling Big data in terms of- Volume, Variety, and Velocity. The next part talks about tools for data science in terms of- Reporting and Business Intelligence, Predictive Modelling and Machine Learning, Artificial Intelligence.

You can read the article here.

 

4. Boost Model Accuracy of Imbalanced COVID-19 Mortality Prediction Using GAN-based Oversampling Technique

2020 will go down in the books of history as the year which changed the entire humanity. Every facet of life was impacted by the Coronavirus and it was imperative for people from all domains to come together and to contribute towards solving this problem.

Generative adversarial networks GAN

The article covers the use of Generative Adversarial Networks (GAN), an Oversampling technique on real word skewed Covid-19 data in predicting the risk of mortality. This story gives us a better understanding of how data preparation steps like handling imbalanced data will improve our model performance.

The data and the core model for this article are considered from the recent study (July 2020) on “COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm” by Celestine Iwendi, Ali Kashif Bashir, Atharva Peshkar. et al. This study used the Random Forest algorithm boosted by the AdaBoost model and predicted the mortality of individual patients with 94% accuracy. In this article, the same model and model parameters were considered to clearly analyze the improvement of existing model accuracies by using GAN- based Oversampling Technique.

You can read the article here.

 

5. CNN vs. RNN vs. ANN – Analyzing 3 Types of Neural Networks in Deep Learning

Why deep learning?

This is a perfect question. We are swamped with machine learning algorithms. There is no dearth in the count and any kind of data can be solved using any of these algorithms.

types of neural networks

Also, deep learning algorithms require huge computing power. So is it necessary to use these algorithms?

This article is a testament to all the queries that question the need for deep learning and its neural networks like convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), etc. Deep learning supersedes machine learning in terms of decision boundaries and feature engineering.

You can read the article here.

 

6. Business Analytics vs. Data Science – Which Path Should you Choose?

Many of us still do not know the different domains in the data sector. We still use these terms interchangeably and it causes great confusion during communication.

Business Analytics Vs. Data Science

There is a surge in demand for both Business Analytics and Data Science. Their market size is expected to reach $100 Billion and $140 billion respectively by 2025. Thus, it only makes sense to understand what both the domains actually mean, their responsibilities, and what are the similarities that lead to these terms being used interchangeably.

We at Analytics Vidhya, have come across a lot of aspiring analytics professionals who want to choose “Business Analytics” or “Data Science” as their career, but they’re not even sure about the distinction between these two roles. Before diving into your own choice, you should be clear about which path you want to take, right? It could be a career-defining choice!

This article explores the similarities and differences between business analytics and data science and tries to give you a better picture.

You can read the article here.

 

7. Joins in Pandas: Master the Different Types of Joins in Python

Some of the simplest tasks such as joining tables may seem tricky in python. This article is a simple guide to join 2 tables using the pandas library seamlessly.

how_to_join_dataframes_python

Our 7th best performing article will help you understand the different types of Joins in Pandas:

  • Inner Join in Pandas
  • Full Join in Pandas
  • Left Join in Pandas
  • Right Join in Pandas

You can read the article here.

 

8. 6 Open Source Data Science Projects to Try at Home!

data_science_projects_github

This is the second data science open source project article to feature in this list. We take it as a clear sign that learning has not taken a backseat when it comes to data science aspirants.

This article contained the top open-source data science projects for the month of April. The list includes-

  • Convert Any Image into a 3D Photo
  • Transform an Image into a Cartoon Illustration
  • One-Shot Multi-Object Tracking
  • OpenAI’s Jukebox: A Generative Model for Music
  • ShyNet – Privacy-Friendly and Cookie-Free Web Analytics
  • Soccer Analytics Handbook

You can read the article here.

 

9. 5 Powerful Python IDEs for Writing Analytics and Data Science Code

Top 5 Python IDEs

Coding is a very personal experience for any data scientist, business analyst, data analyst, or any programmer.

We have all been at a point in our coding journey when we feel that a particular tool is detrimental to our efficiency. The reason can range from your style of coding, your position in the learning path, or any other reason that makes the tool incompatible for you.

That’s where identifying the right IDE comes in. An IDE helps us write and execute Python code for analytics, data science, software development, and a plethora of other tasks. There are multiple IDEs in the market right now, with their own set of features, pros, and cons.

You can read the article here.

10. 5 Powerful Excel Dashboards for Analytics Professionals

How do we represent that data in a way that’ll help our leadership team or decision-makers come to a consensus quickly?

The answer to the above question is a concise visualization. You cannot create a model in excel or python and simply expect the stakeholders to understand the implications.

excel_dashboards

Excel has been a market leader when it comes to EDA and visualization tasks for 35+ years now. It is trusted by the majority of businesses, especially small businesses for its features.

In this article, we discuss the following dashboards-

  • Online Sales Tracking
  • Marketing Analytics
  • Project Management
  • Revenue Tracking
  • HR Management

You can read the article here.

 

End Notes

The year 2020 was a leap for the machine learning community. I hope you find these data science articles fruitful in your learning journey. Let us know your thoughts in the comments below.

Keep Learning! And never stop writing!

top articles of 2020
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Analytics Vidhya

Analytics Vidhya Content team

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