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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

Swirl Package – Easy way to “learn R, in R”

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Guest Blog Last Updated : 19 Jul, 2020
6 min read

People usually quote a steep learning curve as one of the reasons against R, when comparing R vs. Python. The reality is that people miss out on some easier ways to learn R. In this article, we’ll introduce you with one such way to learn R in a fun and interactive way.

This way is none other than use of Swirl Package to learn R. Its tagline ‘Learn R in R’ gives a clear picture of what this package intends to establish. Yes! this inbuilt package can act as your R programming trainer and make you familiar with basics of R in a simplified manner. Moreover, the actions of this trainer will be based on the choices you enter in R console, like the kind of topic to study or the quiz you want to solve.

 swirl_logo

Why should you learn R?

Here are the reasons to learn R:

  1. The big data and data science industry is rapidly growing, so is the need for data scientists and statisticians. By 2018, United States could face a shortage of 140,000 to 190,000 people with deep analytics skills and 1.5 million managers and analyst who would know the use of big data to make information based decision making (Source: McKinsey Global Institute).
  2. R is increasingly becoming the required choice of tool in most of these jobs due to its advantages such as being open source, having external support of more than 5000 packages and gets easily integrated with big data technologies like Hadoop, MapReduce.
  3. For learning R online, there is plethora of options like taking up MOOCs on platforms like coursera, edX, udacity, or probably taking up courses taught by industry experts provided by various online data science training institutes like Jigsaw Academy or Edureka. Often these options can involve a lot of study time, week on week and a financial commitment, so if you want to just explore R then Swirl could be a great option.

 

Also See: Comprehensive learning path to learn R from scratch

 

Is Swirl really for YOU?

Here are few important points, such as for whom this package might be really useful, in which scenarios this package would work best and when should one look at other options for learning R rather than just using Swirl.

Swirl is great for those people who:

  • are trying to learn R just for fun
  • are just figuring out what R is about
  • have no interest in networking on MOOCs
  • like interactive learning without an instructor
  • don’t like watching online videos
  • are Google search experts and want to overcome technical hurdles

On the other hand, if you are planning to make a career out of R programming, then learning only with Swirl package would not suffice. You should definitely learn about the business applications that depend on R programming and further you need to understand how it is used in real-time industry scenarios. As such, you should go beyond just learning R with Swirl package if you:

  • want to take up R programming as a career option
  • want to learn from industry experts
  • plan to network on MOOCs
  • want to know more about the practical applications of R
  • require external support when struck with technical hurdles
  • want to learn about advanced topics in data science

All in all, depending on whichever situation you are in, definitely there is no harm in starting your learning on R programming with Swirl package.

 

How to get started with the Swirl Package?

Let’s walk through the steps involved for getting started with Swirl package.

Before we begin, it’s necessary to ensure both R and RStudio versions are already installed on your system. You can follow the video link for completing R installation on Windows platforms. In case, if you are having Linux or Mac platforms, lot of resources are available for helping you with respective R version installations.

Though RStudio installation is not mandatory, it’s highly recommended because of the unique GUI interface which will make your experience with R much more enjoyable. Once installation is done, next step is to install a package then load the same to use its functions. This can be attained by the below commands.

Learn R using swirl

After loading the swirl package in your R console, the first function in swirl package you should enter is,

swirl 2

 It will prompt you to enter your name, and there after all swirl package commands will address you by your name, which kind of gives a feeling of a customized training being provided by the computer. After entering the name, it will further prompt you to select any one of the listed course options to begin your training with.

 swirl 3

In terms of course selection, you can either enter one of options listed in R console or you can do it manually by downloading the respective course of your interest from Swirl course repository. This task can be achieved using the below commands shown on the help page.

swirl 4

swirl 5

Let’s select option 1 to get started with the “R Programming” course of Swirl package. This course will automatically get installed on your system. After the course is installed, it will further prompt you to enter which lesson you want to start with. As you can see below, there are different courses like Basic Building Blocks, Workspace and Files and Base Graphics that exist under R Programming course.

Swirl 6

If you manage to complete all the 15 lessons present under “R Programming” course, you can successfully call yourself a basic R programmer which would be your first achievement to have under R skills. Well, this seems to be a straight forward way for anyone to pick up R programming skills just with the help of Swirl package and no other external help.

However, this package falls short in providing more data science project examples that are widely used in the Industry. Definitely, one can become a good R programmer, but doing data science is all together a different game compared to picking up a programming language.It is all about how you can leverage R tool skills in solving a business problem as part of your data science project. Well, this would require another blog post all together, for now let us stick with how to acquire R tool skills using Swirl package.

Learn more about Swirl package offerings at below link. http://swirlstats.com/

 

End Notes

In this article, we discussed the significance of Swirl Package in R i.e how this package can be easily adapted by beginners to learn R in a more fun and interactive way. You also learnt the importance of R programming, if you are the right match to use Swirl and how you can get started with Swirl package in R

Did you find the article useful? Do let us know your thoughts about this guide in the comments section below.

Kiran_PV

Kiran is a graduate of IIT-Madras with more than five years of professional experience in business analytics. He is currently a faculty at Jigsaw Academy. Prior to Jigsaw, he worked at LatentView Analytics delivering advanced analytics and business consulting solutions to various clients across verticals such as E-Commerce, Insurance, Technology and Financial Services. He has strong proficiency in working with tools such as SAS, R, MySQL, Python, Hadoop, Tableau etc. In free time, he enjoys participating in data mining contests on open platforms like Kaggle and CrowdAnalytix.

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.

 

 

datascienceRR Programmingswirlswirlstats
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Responses From Readers

Clear

Saurav
Saurav

Great package to start with ! Thank You

123

Pierre L
Pierre L

The swirl package is great. I couldn't have gotten through the Coursera R Programming course without it!

123

Nelson Astorga
Nelson Astorga

Well, that´s really good to know. I am taking the R Programming course at Coursera and I could not find a match between the video lectures and getting ot actually using R, but then I have started using swirl and I´m getting it!

123
Ricardo
Ricardo

Great article. I just started learning R and Swirl seem the best so far. I started Edx and Datacamp, turns out to be the same, but the EDx has Microsoft's name, very easy to follow, but the Swirl way seems more natural. I also started in coursera, but had a bad experience before with the epidemiology course... Question: what will be the best to do to start learning r? I want to start using it in my research in public health, but also want to expand and work as a r programmmer. Thanks, Ricardo

123

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