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Data Science Challenges in R Programming Language, Take-home challenges in data science will force you to step outside of your comfort zone.

But guess what?

That’s advantageous because it’s the one area where you’ll actually learn stuff. Both novice and expert R programmers must learn to master the take-home problems, but the challenges you’ll tackle will differ greatly depending on your degree of expertise.

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Data Science Challenges in R Programming Language

Today, we offer you five data science homework problems in the R programming language that is suitable for both those with little prior knowledge and those with a few years.

You may be sure that you will know how to solve all of the tasks because none of them call for any specific subject knowledge. Only technical knowledge is needed.

1. Titanic – Machine Learning from Disaster

It is the most well-known data science challenge, but there is a good reason for that.

It offers just enough data preparation to make you wonder how you might be able to improve, and it’s quite straightforward in terms of machine learning.

After all, it’s a binary categorization issue.

The objective is to identify the person who had the best probability of surviving. Women and children, perhaps? Does socioeconomic class factor into this? Or was it really a matter of luck? You have to do the research.

It truly is a take-home exercise in fundamental data science. To read comma-separated data (CSV), handle missing data, encode categorical data, visualize data, and train machine learning models, you’ll need to be proficient in these skills (binary classification).

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The fact that R is a general-purpose programming language is the best aspect. 14.5k competitors have submitted more than 54k submissions to the challenge on Kaggle as of April 2022.

There’s no denying that the competition is intense, so if you’re just getting started, don’t expect to be at the top of the leaderboard.

The only thing that matters, in the long run, is learning on your own and from other submissions.

To begin started, we advise using the materials listed below:

• Official Kaggle challenge overview and data
• Sample solution overview (86.7% accuracy)

2. Store Sales – Time Series Forecasting

Let’s face it: Over time, all businesses gather data. It’s best to get your hands dirty as soon as possible because, for you as a data scientist, that implies a lot of work including time series analysis and forecasting.

The objective of this data science homework assignment is to develop a model that can accurately predict the unit sales of thousands of products offered at various Corporación Favorita locations, a significant Ecuadorian grocery chain.

You won’t run out of things to adjust anytime soon because the dataset(s) contain a tonne of supplemental data, including holiday information, store specifics, specials, and more.

Time series data is challenging to work with, as you’ll discover as you become more comfortable with time series analysis and forecasting.

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Any method, from basic moving averages to cutting-edge deep learning LSTM variants, can be used. Any regression approach will also work because any time series problem may be reframed as a supervised problem.

The problem is that you can test and modify hundreds of algorithms, which will take a lot of your time.

There are now 1.36k contestants who have submitted 3.4k entries, therefore the level of competition is fair. Aim to learn, not to top the leaderboard as you did with the first take-home task.

Are you prepared to begin? The following materials may be helpful to you:

• Challenge overview and datasets
• A Medium article to get you started

3. Digit Recognizer

Without the MNIST dataset, this list of beginner-friendly data science take-home tasks would be incomplete. It serves as a de facto computer vision “Hello World” dataset. The variation we’ll demonstrate has a catch.

For newcomers, categorizing photographs can seem intimidating. What exactly is an image? It’s basically a group of pixels dispersed across one or three color channels (grayscale or colored image).

A pixel is, however, what? It’s a numeric value between 0 and 255. The more of that color is present in the pixel, the higher the value.

Tens of thousands of 2828 pixel images in a single color channel can be found in the MNIST collection. Thus, there are 784 pixels altogether.

These 784 characteristics determine what distinguishes a number 5 from a number 7 and what does not. But do all pixels have any use? Most of them will be extra padding around the digit, so it’s unlikely.

There are several options available here. You may use logistic regression to complete the problem by treating these 784 pixels as unique characteristics for a classification model.

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The dimensionality could be decreased beforehand. Alternatively, you might use the convolutional model on a 1-dimensional array of 784 pixels by transforming it into a 2-dimensional 2828 matrix.

The last choice will probably produce the finest outcomes, but we’ll let you experiment to find out.

Because MNIST is a well-known dataset, there have been 8.4k entries on Kaggle from 2.2k participants. There is a lot of competition, but if you understand it, it’s simple to attain close to 100% accuracy (hint: transfer learning).

To help you get started, we suggest the following sources:

• Kaggle challenge overview and dataset
• Appsilon’s guide to training a digit classification model in R and Tensorflow

4. TensorFlow – Help Protect the Great Barrier Reef

The largest reef in the world is the Great Barrier Reef in Australia. Recently, it has come under threat, in part due to an overabundance of COTS, the coral-eating crown-of-thorns starfish. Here is where you step in.

This data science homework assignment aims to construct an object detection model trained on underwater recordings of coral reefs that can recognize starfish in real-time.

It’s difficult to work because it presupposes that you are familiar with object detection, a sophisticated computer vision technology.

The decision on how to address the situation rests entirely with you. You are tasked with creating a model that can precisely recognize objects of interest from a total of 23.5k training photos taken from three videos.

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Annotations, or the bounding box coordinates surrounding the object(s) of interest for each image, are stored in a separate train.csv file.

Make sure you have a good hardware setup before starting this challenge. For quicker training, object detection algorithms require a GPU.

NVIDIA’s most recent products will work (RTX or better). You might try training the model on Google Colab if you don’t have access to that setting.

There is undoubtedly a lot of interest in this challenge since around 61k entries have been submitted by 2.6k competitors as of right now.

Even though it was shut down two months ago, you can still work on it for amusement and experience.

Are you prepared to begin? Listed below are a few sources you might find helpful:

• Challenge description and dataset
• Appsilon’s introduction to YOLO and YOLO object detection algorithm
• How this team ranked in the top 5% of the challenge

5. Bag of Words Meets Bags of Popcorn

Without an NLP-based task, the list of data science homework assignments would be incomplete. Bags of Popcorn can help in this situation.

With the use of fundamental NLP approaches and a deep learning-based methodology, Word2Vec’s natural language processing capabilities are to be introduced.

For sentiment analysis, 50,000 IMDB movie reviews were particularly selected for the dataset. The reviews’ sentiment is binary, therefore a rating on IMDB of less than five has a sentiment value of zero, and a rating of seven or higher has a sentiment score of one.

You’ll be working with raw text, so you’ll need to know how to preprocess and alter it. Four lessons on using bag-of-words, word vectors, and contrasting deep learning and non-deep learning approaches to working with text data are included on the Kaggle website for this challenge.

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In the event that you have little prior experience, it’s a great spot to start. Although the examples are in Python, they may be readily converted to R.

659 competitors had submitted 4.3k submissions as of April 2022. Even though the original posting was made seven years ago, there isn’t as much rivalry as there is with the other take-home tasks.

Are you prepared to take on your first NLP challenge? To help you started, check out these resources:

• Official challenge description and data
• A sample solution on GitHub

Summary of Data Science Challenges in R Programming Language

These are the five data science homework assignments you may start working on right away. Although the majority of the code examples are written in Python, most of them may be easily converted to R.

Any of these challenges requires a lot of time to work on. Expecting to develop a highly precise and completely functional solution in a single day is unrealistic.

Teams of people work on a project for weeks or even months before it is finished. Remember to be patient with yourself and to enjoy the process.

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