Trending March 2024 # Bringing The Science Of Learning Into Classrooms # Suggested April 2024 # Top 3 Popular

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Years of research prompt a group of scientists to ask whether we should rethink the way we do school.

New research sheds light on the effects that childhood experiences—both good and bad—have on the developing brain. But are schools keeping up?

“The 20th-century education system was never designed with the knowledge of the developing brain,” says Pamela Cantor, MD, who is part of a cross-disciplinary team of experts studying the science of learning and development. “So when we think about the fact that learning is a brain function and we have an education system that didn’t have access to this critical knowledge, the question becomes: Do we have the will to create an education system that’s informed by it?”

Contrary to the long-held belief that brain maturation is largely complete by the age of 6, we now know that our brains are malleable and continue to change dramatically well into our 20s. This has profound implications for learning throughout the school-age years.

The good news is that while toxic stress and abusive relationships can inhibit learning, positive and supportive learning environments can stem the tide. A trusting relationship with an adult—a teacher or school counselor, for example—can be a protective buffer against the negative effects of stress.

Another key takeaway for teachers is that the science confirms that variability in a developing brain is actually the norm, not the exception. A room full of 5-year-olds spans the gamut of skills, developmentally speaking, and that continues to hold true for 10- and 16-year-olds. But while we are all highly variable, we are all on similar paths—eventually acquiring the same sets of skills in roughly the same order.


What Schools Can Do

“The science says to us that, in fact, the way the brain functions and grows, it needs safety, it needs warmth, it actually even needs hugs,” explains Stanford professor Linda Darling-Hammond. “We actually learn in a state of positive emotion much more effectively than we can learn in a state of negative emotion. That has huge implications for what we do in schools.”

Integrate practices that explicitly address belonging and safety: We now know that when schools are safe, supportive places that affirm individual identity, create paths for belonging for every student, and intentionally build strong, long-lasting relationships, they open the opportunity for greater intellectual learning because our brains are more responsive and open to learning in safe environments.

Both pedagogical and social strategies can be integrated into classrooms and school systems in ways that are consistent with the emerging science. According to a 2023 study, starting the day off with a simple relationship-building activity—welcoming students at the door—can increase academic engagement by 20 percentage points while decreasing disruptive behavior by 9 percentage points. And at King Middle School in Maine, for example, eighth-grade English language arts teacher Catherine Paul teaches talk moves—short sentence starters such as “I disagree because…”—to build a culture of tolerance and respect while maintaining rigorous academic standards. “I do talk moves because, in order to have a great discussion, everyone has to feel like they’re a part of it, and valued,” Paul explains. “And when they walk away, they really have bridged a gap with someone that maybe they wouldn’t necessarily have talked to, or talked to on that level.”

Other strategies from diverse schools representing a broad range of grade levels can be found in our video series on the science of learning and development, How Learning Happens.

What we now understand about human development and learning has come a long way since we began designing schools, and a shift can better align the way we teach with the way students learn. It should be noted, however, that for years great teachers have been doing things in classrooms that embody what the science now confirms. In a nutshell: Relationships matter deeply, learning happens when the brain feels safe and supported, and no child is a lost cause.

“What is so true in the science of human development is that it is an optimistic story,” Cantor says. “It tells a story that no matter what a child’s starting point is, that development is possible if it is intentionally encouraged in the experiences and relationships that children have.”

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12 Powerful Tips To Ace Data Science And Machine Learning Hackathons


Data science hackathons can be a tough nut to crack, especially for beginners

Here are 12 powerful tips to crack your next data science hackathon!


Like any discipline, data science also has a lot of “folk wisdom”. This folk wisdom is hard to teach formally or in a structured manner but it’s still crucial for success, both in the industry as well as in data science hackathons.

Newcomers in data science often form the impression that knowing all machine learning algorithms would be a panacea to all machine learning problems. They tend to believe that once they know the most common algorithms (Gradient Boosting, Xtreme Gradient Boosting, Deep Learning architectures), they would be able to perform well in their roles/organizations or top these leaderboards in competitions.

Sadly, that does not happen!

If you’re reading this, there’s a high chance you’ve participated in a data science hackathon (or several of them). I’ve personally struggled to improve my model’s performance in my initial hackathon days and it was quite a frustrating experience. I know a lot of newcomers who’ve faced the same obstacle.

So I decided to put together 12 powerful hacks that have helped me climb to the top echelons of hackathon leaderboards. Some of these hacks are straightforward and a few you’ll need to practice to master.

If you are a beginner in the world of Data Science Hackathons or someone who wants to master the art of competing in hackathons, you should definitely check out the third edition of HackLive – a guided community hackathon led by top hackers at Analytics Vidhya.

The 12 Tips to Ace Data Science Hackathons

Understand the Problem Statement

Build your Hypothesis Set

Team Up

Create a Generic Codebase

Feature Engineering is the Key

Ensemble (Almost) Always Wins

Discuss! Collaborate!

Trust Local Validation

Keep Evolving

Build hindsight to improve your foresight

Refactor your code

Improve iteratively

Data Science Hackathon Tip #1: Understand the Problem Statement

Seems too simple to be true? And yet, understanding the problem statement is the very first step to acing any data science hackathon:

Without understanding the problem statement, the data, and the evaluation metric, most of your work is fruitless. Spend time reading as much as possible about them and gain some functional domain knowledge if possible

Re-read all the available information. It will help you in figuring out an approach/direction before writing a single line of code. Only once you are very clear about the objective, you can proceed with the data exploration stage

Let me show you an example of a problem statement from a data science hackathon we conducted. Here’s the Problem Statement of the BigMart Sales Prediction problem:

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

The idea is to find the properties of a product and store which impact the sales of a product. Here, you can think of some of the factors based on your understanding that can make an impact on the sales and come up with some hypotheses without looking at the data.

Data Science Hackathon Tip #2: Build your Hypothesis Set

Next, you should build a comprehensive list of hypotheses. Please note that I am actually asking you to build a set of the hypothesis before looking at the data. This ensures you are not biased by what you see in the data

It also gives you time to plan your workflow better. If you are able to think of hundreds of features, you can prioritize which ones you would create first

Read more about hypothesis generation here

I encourage you to go through the hypotheses generation stage for the BigMart Sales problem in this article: Approach and Solution to break in Top 20 of Big Mart Sales prediction We have divided them on the basis of store level and product level. Let me illustrate a few examples here.

Store-Level Hypotheses:

City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there

Population Density: Stores located in densely populated areas should have higher sales because of more demand

Store Capacity: Stores that are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place

Ambiance: Stores that are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales

Product-Level Hypotheses:

Brand: Branded products should have higher sales because of higher trust in the customer

Packaging: Products with good packaging can attract customers and sell more

Utility: Daily products should have a higher tendency to sell as compared to the specific products

Promotional Offers: Products accompanied by attractive offers and discounts will sell more

Data Science Hackathon Hack #3: Team Up!

Build a team and brainstorm together. Try and find a person with a complementary skillset in your team. If you have been a coder all your life, go and team up with a person who has been on the business side of things

This would help you get a more diverse set of hypotheses and would increase your chances of winning the hackathon. The only exception to this rule can be that both of you should prefer the same tool/language stack

It will save you a lot of time and you will be able to parallelly experiment with several ideas and climb to the top of the leaderboard

Get a good score early in the competition which helps in teaming up with higher-ranked people

Here are some of the instances where hackathons were won by a team:

Data Science Hackathon Tip #4: Create a Generic Codebase

Save valuable time when you participate in your next hackathon by creating a reusable generic code base & functions for your favorite models which can be used in all your hackathons, like:

Create a variety of time-based features if the dataset has a time feature

You can write a function that will return different types of encoding schemes

You can write functions that will return your results on a variety of different models so that you can choose your baseline model wisely and choose your strategy accordingly

Here is a code snippet that I generally use to encode all my train, test, and validation set of the data. I just need to pass a dictionary on which column and what kind of encoding scheme I want. I will not recommend you to use exactly the same code but will suggest you keep some of the function handy so that you can spend more time on brainstorming and experimenting.

View the code on Gist.

Here is a sample of how I use the above function. I just need to provide a dictionary where the keys are the type of encoding I want and the values are the columns name that I want to encode:

View the code on Gist.

You can also use libraries like pandas profiling to get an idea about the dataset by reading the data:

Data Science Hackathon Tip #5: Feature Engineering is Key

“More data beats clever algorithms, but better data beats more data.”

– Peter Norwig

Feature engineering! This is one of my favorite parts of a data science hackathon. I get to tap into my creative juices when it comes to feature engineering – and which data scientist doesn’t like that?

Data Science Hackathon Tip #6: Ensemble (Almost) Always Wins

95% of winners have used ensemble models in their final submission on DataHack hackathons

Ensemble modeling is a powerful way to improve the performance of your model. It is an art of combining diverse results of individual models together to improvise on the stability and predictive power of the model

You will not find any data science hackathon that has top finishing solutions without ensemble models

You can learn more about the different ensemble techniques from the following articles:

Basics of Ensemble Learning

A Comprehensive Guide to Ensemble Learning

Data Science Hackathon Tip #7: Discuss! Collaborate!

Stay up to date with forum discussions to make sure that you are not missing out on any obvious detail regarding the problem

Do not hesitate to ask people in forums/messages:

Data Science Hackathon Tip #8: Trust Local Validation

Do not jump into building models by dumping data into the algorithms. While it is useful to get a sense of basic benchmarks, you need to take a step back and build a robust validation framework

Without validation, you are just shooting in the dark. You will be at the mercy of overfitting, leakage and other possible evaluation issues

By replicating the evaluation mechanism, you can make faster and better improvements by measuring your validation results along with making sure your model is robust enough to perform well on various subsets of the train/test data

Have a robust local validation set and avoid relying too much on the public leaderboard as this might lead to overfitting and can drop your private rank by a lot

In the Restaurant Revenue Prediction contest, a team that was ranked first on the public leaderboard slipped down to rank 1962 on the private leaderboard

“The first we used to determine which rows are part of the public leaderboard score, while the second is used to determine the correct predictions. Along the way, we encountered much interesting mathematics, computer science, and statistics challenges.”

Source: Kaggle:  BAYZ Team

Data Science Hackathon Tip #9 – Keep Evolving

It is not the strongest or the most intelligent who will survive but those who can best manage change. –Charles Darwin

If you are planning to enter the elite class of data science hackers then one thing is clear – you can’t win with traditional techniques and knowledge.

Employing logistic regression or KNN in Hackathons can be a great starting point but as you move ahead of the curve, these won’t land you in the top 100.

Let’s take a simple example – in the early days of NLP hackathons, participants used TF-IDF, then Word2vec came around. Fast-forward to nowadays, there are state-of-the-art Transformers. The same goes for computer vision and so on.

Keep yourself up-to-date with the latest research papers and projects on Github. Although this will require a bit of extra effort, it will be worth it.

Data Science Hackathon Tip #10 – Use Hindsight to build your Foresight

Has it ever happened that after the competition is over, you sit back, relax, maybe think about the things you could have done, and then move on to the next competition? Well, this is the best time to learn!

Do not stop learning after the competition is over. Read winning solutions of the competition, analyze where you went wrong. After all, learning from mistakes can be very impactful!

Try to improve your solutions. Make notes about it. Refer to it to your friends and colleagues and take back feedback.

This will give you a solid head-start for your next competition. And this time you’ll be much more equipped to go tackle the problem statement. Datahack provides a really cool feature of late submission. You can make changes to your code even after the hackathon is over and submit the solution and check its score too!

Data Science Hackathon Tip #11 – Refactor your code

Just imagine living in a room, where everything is messy, clothes lying all around, shoes on the shelves, and food on the floor. It is nasty. Isn’t it? The same goes for your code.

When we get started with a competition, we are excited and we probably write rough code, copy-paste from earlier your earlier notebooks, and some from stack overflow. Continuing this trend for the complete notebook will make it messy. Understanding your code will consume the majority of your time and make it harder to perform operations.

The solution is to keep refactoring your code from time to time. Keep maintaining your code at regular intervals of time.

This will also help you team up with other participants and have much better communication.

Data Science Hackathon Tip #12 – Improve iteratively

Many of us follow the linear approach of model building, going through the same process – Data Cleaning, EDA, feature engineering, model building, evaluation. The trick is to understand that it is a circular and iterative process.

For example, we are building a sales prediction model and we get a low MAE, we decide to analyze the samples. Hence, it turns out, that our model is giving spurious results for female buyers. We can then take a step back and focus on the Gender feature, do EDA on it and check how to improve from there. Here we are going back and forth and improving step-by-step.

We can also look at some of the strong and important features and combine some of them and check their results. We may or may not see an improvement but we can only figure it out by moving iteratively.

It is very important to understand that the trick is to be a dynamic learner. Following an iterative process can lead you to achieve double or single-digit ranks.

Final Thoughts

These 12 hacks have held me in good stead regardless of the hackathon I’m participating in. Sure, a few tweaks here and there are necessary but having a solid framework and structure in place will take you a long way towards achieving success in data science hackathons.

Do you want more of such hacks, tips, and tricks? HackLive is the way to go from zero-to-hero and master the art of participating in a data science competition. Don’t forget to check out the third edition of HackLive 3.


Celebrate Year Of The Rooster With The Best Chicken Science

Saturday is the first day of the Chinese Lunar New Year, and celebrations have already started around the globe. This year is the Year of the Chicken (or Rooster, if you will). And what better way to celebrate than with a roundup of chicken science?

Chickens are more than meals on our table. From sacrificing embryos for the early research of developmental biology, to serving as handy dinosaur stand-ins for research, chickens deserve more than just their reputation as a protein source.


Domestic chickens, like pigeons, navigate with built-in compasses, or biological sensors that detect the magnetic field of the Earth. As researcher Wolfgang Wiltschko told LiveScience, some scientists believe that chickens’ magnetic sensors probably lie somewhere in their eyes, because chickens seem to need short wavelengths (like blue light) to navigate. In Wiltschko’s experiment, their sense of direction was lost under longer wavelengths.

So next time when you meet a chicken that’s crossing the road, don’t honk at it. Be patient. It knows where to go. Probably.

Running with a steady head


A handy substitute for a dinosaur


Watch the gif. This might be how dinosaurs took a stroll.

Chicken are actually the last living dinosaurs, so researchers decided to stick a fake tail onto some chick butts in order to study how those ancient ancestors might have walked. As the researchers announced in the abstract of their PLOS paper: “Here we show that, by experimentally manipulating the location of the centre of mass in living birds, it is possible to recreate limb posture and kinematics inferred for extinct bipedal dinosaurs.”

Chicks raised with the fake tail indeed changed their postures, and had a more vertical femur structure as result, confirming “a shift from hip-driven to knee-driven limb movements through theropod evolution.” Not too surprisingly, this groundbreaking first-of-its-kind study won the Ignobel Prize for Biology in 2024.

Becoming “Chickenosaurus”

Make way for the Chickenosaurus. Pexels

Resurrecting dinosaurs by tweaking the chicken genome is not a new idea. Last year, Chilean researchers successfully grew a dinosaur-like bone structure in the legs of a chicken, making some solid progress towards the Chickenosaurus goal—an ambitious idea envisioned and popularized by the American paleontologist Jack Horner.

However, (as humble and cautious as scientists always are) the authors of that study told Motherboard that Chickenosaurus was not their ultimate goal. They just wanted to better understand how birds evolved from earlier dinosaurs.

Establishing the pecking order


Chickens don’t usually peck buttons in a power plant, except in the Simpsons. But they do peck to establish a social order, a hierarchy within the flock for access to food and water. The pecking order of a flock is often set in their chick stage, but can also be slightly changed with later fights.

Thorleif Shjelderup-Ebbe, a Norwegian zoologist, was the first one to coin the term pecking order from his close observations of chicken flocks. Social hierarchy has also been found in other living organisms such as insects, fish, and primates.

The ultimate form of rejection

Female chickens have an amazing birth control method—postcopulatory sexual selection. pexels

Chicken social status actually matter a lot. Hens selectively reject sperm from low-status roosters. If the mating male is low in rank, researchers found, the female is more likely to eject his sperm.

“It is beginning to appear females can play a much more subtle, but powerful role in the battle for fertilization,” the study author Tommaso Pizzari of Oxford University told LiveScience.

The Cosmopolitan Chicken


The idea of breeding a Cosmopolitan Chicken—a hybrid of all types of chicken in the world—is one of the more grandiose chicken-related endeavors. Rather than using chickens for food, Belgian artist Koen Vanmechelen thinks of them as a symbol of multiculturalism and diversity, which led him to start the Cosmopolitan Chicken Project (CCP) in the 1990s.

“The CCP is a mirror. Every organism needs another organism to survive,” says Vanmechelen on the CCP website. The Cosmopolitan Chicken is up to its 20th iteration now, and such hybrid chickens with diverse DNA might be healthier than purebred poultry, according to Modern Farmer.

All this human inquiry is great, but what do the chickens think? As the Year of the Chicken begins, some of us might be curious about what intelligence and emotional depth these creatures have. Here’s a starting point for that intellectual journey: Neuroscientist Lori Marino, science director of the Nonhuman Rights Project, recently penned a review article called “Think Chickens”.

5 Challenges Of Machine Learning!

This article was published as part of the Data science Blogathon.

Introduction :

In this post, we will come through some of the major challenges that you might face while developing your machine learning model. Assuming that you know what machine learning is really about, why do people use it, what are the different categories of machine learning, and how the overall workflow of development takes place.

Image Source

What can possibly go wrong during the development and prevent you from getting accurate predictions?

So let’s get started, during the development phase our focus is to select a learning algorithm and train it on some data, the two things that might be a problem are a bad algorithm or bad data, or perhaps both of them.

Table of Content :

Not enough training data.

Poor Quality of data.

Irrelevant features.

Nonrepresentative training data.

Overfitting and Underfitting.

1. Not enough training data :

Let’s say for a child, to make him learn what an apple is, all it takes for you to point to an apple and say apple repeatedly. Now the child can recognize all sorts of apples.

2. Poor Quality of data:

Obviously, if your training data has lots of errors, outliers, and noise, it will make it impossible for your machine learning model to detect a proper underlying pattern. Hence, it will not perform well.

So put in every ounce of effort in cleaning up your training data. No matter how good you are in selecting and hyper tuning the model, this part plays a major role in helping us make an accurate machine learning model.

“Most Data Scientists spend a significant part of their time in cleaning data”.

There are a couple of examples when you’d want to clean up the data :

If you see some of the instances are clear outliers just discard them or fix them manually.

If some of the instances are missing a feature like (E.g., 2% of user did not specify their age), you can either ignore these instances, or fill the missing values by median age, or train one model with the feature and train one without it to come up with a conclusion.

3. Irrelevant Features:

“Garbage in, garbage out (GIGO).”

Image Source

In the above image, we can see that even if our model is “AWESOME” and we feed it with garbage data, the result will also be garbage(output). Our training data must always contain more relevant and less to none irrelevant features.

The credit for a successful machine learning project goes to coming up with a good set of features on which it has been trained (often referred to as feature engineering ), which includes feature selection, extraction, and creating new features which are other interesting topics to be covered in upcoming blogs.

4. Nonrepresentative training data:

To make sure that our model generalizes well, we have to make sure that our training data should be representative of the new cases that we want to generalize to.

If train our model by using a nonrepresentative training set, it won’t be accurate in predictions it will be biased against one class or a group.

For E.G., Let us say you are trying to build a model that recognizes the genre of music. One way to build your training set is to search it on youtube and use the resulting data. Here we assume that youtube’s search engine is providing representative data but in reality, the search will be biased towards popular artists and maybe even the artists that are popular in your location(if you live in India you will be getting the music of Arijit Singh, Sonu Nigam or etc).

So use representative data during training, so your model won’t be biased among one or two classes when it works on testing data.

5. Overfitting and Underfitting :

What is overfitting?

Image Source

Let’s start with an example, say one day you are walking down a street to buy something, a dog comes out of nowhere you offer him something to eat but instead of eating he starts barking and chasing you but somehow you are safe. After this particular incident, you might think all dogs are not worth treating nicely.

So this overgeneralization is what we humans do most of the time, and unfortunately machine learning model also does the same if not paid attention. In machine learning, we call this overfitting i.e model performs well on training data but fails to generalize well.

Overfitting happens when our model is too complex.

Things which we can do to overcome this problem:

Simplify the model by selecting one with fewer parameters.

By reducing the number of attributes in training data.

Constraining the model.

Gather more training data.

Reduce the noise.

What is underfitting?

Image Source

Yes, you guessed it right underfitting is the opposite of overfitting. It happens when our model is too simple to learn something from the data. For E.G., you use a linear model on a set with multi-collinearity it will for sure underfit and the predictions are bound to be inaccurate on the training set too.

Things which we can do to overcome this problem:

Train on better and relevant features.

Reduce the constraints.

Conclusion :

Machine Learning is all about making machines better by using data so that we don’t need to code them explicitly. The model will not perform well if training data is small, or noisy with errors and outliers, or if the data is not representative(results in biased), consists of irrelevant features(garbage in, garbage out), and lastly neither too simple(results in underfitting) nor too complex(results in overfitting). After you have trained a model by keeping the above parameters in mind, don’t expect that your model would simply generalize well to new cases you may need to evaluate and fine-tune it, how to do that? Stay tuned this is a topic that will be covered in the upcoming blogs.

Thank you,

Karan Amal Pradhan.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.


“Pillownaut” Stays In Bed For The Sake Of Science

When humans eventually live on the moon and Mars, the discomforts of eating freeze-dried food and drinking our own urine will hardly be our only space nuisances. Apparently, our feet will tingle, we’ll get headaches and toothaches, our eyes will be runny, and we’ll have chronically stuffy noses.

Scientists have a pretty good notion of what will happen to your body when you’re walking on the moon or traveling gravity-free for two years en route to Mars — thanks to a cadre of bed-ridden test subjects.

Twice in the past year, self-described “pillownaut” Heather Archuletta agreed to lie still for what some Americans might consider a nice break — she signed up to snooze in the name of science.

NASA needs more volunteers for its bed-rest study, which helps doctors understand what a lack of gravity does to the body. You can earn $160 per day, plus all travel and lodging expenses, if you make the cut. Five grand a month to lay down and play Xbox? It might sound too good to be true, but beware — leaky eyes, throbbing feet, and bed pans await you.

“You have this notion in your head of, ‘Oh, I can do it.’ But it is strange to feel everything that changes,” said Archuletta, 39. “You don’t think about it; oh, God, you just feel pain. But when you learn why the body compensates the way it does, it is actually very enlightening.”

Lying on your back for long periods simulates what happens to your muscles and bones in space. When you have no gravity pulling you down, your muscles don’t have to work hard to move you across the room, and your bones don’t have much weight to support, so they weaken. People living in micro-gravity have to exercise frequently to avoid that, which is why astronauts on the International Space Station are getting the new “Combined Operational Load Bearing External Resistance Treadmill” (named for comedian Stephen Colbert) later this year.

Zero-Gravity Simulation

In micro- or zero-gravity environments, your blood thickens; blood and other fluids pool in your head, causing headaches, toothaches, and a stuffy nose; your feet get tingly, because less blood flows to them; and your eyes might leak tears for no reason. These are just some of the weird side effects.

But most people who experience those effects get the compensation of actually being in space. Why would a healthy person volunteer to go through the same thing in a bed on boring old Earth?

“I did have a couple of those moments where I was like, ‘Geez, what have I done?” Archuletta said. “But it’s a real privilege to get in … I am passionate about this in a way I haven’t been for an office job in a lot of years.”

In the lunar study, you lie down for 21 days, with your body tilted so your feet support one-sixth of your weight, which mimics gravity on the moon. The longer-term study simulates the lack of gravity you would experience in a space station sojourn or while traveling to Mars. In that study, Archuletta lay in bed for 50 days, with her head declined, six degrees below the rest of her body. It would have been 90 days, but Hurricane Ike forced the evacuation of Galveston Island, where the tests take place.

Other than the persistent headaches at first, it wasn’t too bad, she said — she got to stay in bed and watch movies. But with a bed pan. “After you get used to that, it’s just the same four or five minutes out of your day that it always was,” she said.

Bed-rest study subjects have to pass the Air Force medical exam, and can’t be on any medication. They cannot imbibe alcohol or caffeine, which might make the 16-hour “awake times” a little less fun. But subjects get paid to play Monopoly, watch movies, and catch up on reading, and they get a massage every other day, so it’s not exactly punishment, either.

Though it’s meant to study micro-gravity, the research could also help pregnant women who have been ordered to stay in bed for medical reasons, or people whose injuries or disabilities render them immobile.

Archuletta started a blog, The Pillow Astronaut, where chronicles her sometimes-bizarre experiences and answers the persistent question: Why would anyone in her right mind do this?

“I really want to see us get back to the moon and to Mars. I want to see that in my lifetime,” she said.

Now at least she knows what it will feel like.

Test Subject in the Lunar Gravity Study

19 Moocs On Mathematics & Statistics For Data Science & Machine Learning


Before creation, God did just pure mathematics. Then he thought it would be pleasant change to do some applied

                                                                                       -John Edensor Littlewood

Mathematics & Statistics are the founding steps for data science and machine learning. Most of the successful data scientists I know of, come from one of these areas – computer science, applied mathematics & statistics or economics. If you wish to excel in data science, you must have a good understanding of basic algebra and statistics.

However, learning Maths for people not having background in mathematics can be intimidating. First, you have to identify what to study and what not. The list can include Linear Algebra, calculus, probability, statistics, discrete mathematics, regression, optimization and many more topics. What do you do? How deep to you want to get in each of these topics? It is very difficult to navigate through this by yourself.

If you have faced this situation before – don’t worry! You are at the right place now. I have done the hard work for you. Here is a list of popular open courses on Maths for Data science from Coursera, edX, Udemy and Udacity. The list has been carefully curated to give you a structured path to teach you the required concepts of mathematics used in data science.

Get started now to learn & explore mathematics for data science.

Which course is suitable for you?

Few courses may require you to finish the preceding course for better understanding. So, make sure that you either know the subject or have undergone these courses.

Read on to find out the right course for you!

Table of Content

Beginners Mathematics / Statistics

Data Science Maths Skills

Intro to Descriptive Statistics

Intro to Inferential Statistics

Introduction to Probability and Data

Math is Everywhere: Applications of Finite Math

Probability: Basic Concepts & Discrete Random Variables

Mathematical Biostatistics Boot Camp 1

Applications of Linear Algebra Part 1

Introduction to Mathematical Thinking

Intermediate Mathematics / Statistics

Bayesian Statistics: From Concept to Data Analysis

Game Theory 1

Game Theory II: Advanced Applications

Advanced Linear Models for Data Science 1: Least Squares

Advanced Linear Models for Data Science 2: Statistical Linear Models

Introduction to Linear Models and Matrix Algebra

Maths in Sports

Advanced Mathematics / Statistics

Discrete Optimization

Statistics for Genomic Data Science

Biostatistics for Big Data Applications

Beginners Mathematics & Statistics

Duration: 4 weeks

Led by: Duke University (Coursera)

If you are a beginner with very minimal knowledge of mathematics, then this course is for you. In this course, you will learn about concepts of algebra like set theory, inequalities, functions, coordinate geometry, logarithms, probability theory and many more.

This course will take you through all the basic maths skills required for data science and would provide a strong foundation.

The course starts from 9 Jan 2023 and is lead by professors from Duke University. 

Prerequisites: Basic maths skills

Duration: 8 weeks

Led by: Udacity

This course by Udacity is an excellent beginners guide for learning statistics. It is fun, practical and filled with examples. The Descriptive Statistics course will first make you familiar with different terms of statistics and their definition. Then you will learn about statistics concepts like central tendency, variability, standard normal distribution and sampling distribution.

This course doesn’t require any prior knowledge of statistics and is open for enrollment. 

Prerequisites: None

Duration: 8 weeks

Led by: Udacity

After you have gone through the Descriptive Statistics course, it is time for Inferential statistics. The same practical approach to the subject continues in this course.

In this course, you will learn concepts of statistics like estimation, hypothesis testing, t-test, chi-square test, one-way Anova, two-way Anova, correlation, and regression.  

There are problem set and quiz questions after each topic. You will also be able to test your learning on a real-life dataset at the end of the course. The course is open for enrollment. 

Prerequisites: Complete understanding of Descriptive Statistics (the course mentioned above)

Alternate Course: You can also look at Statistics: Unlocking the World of Data. It is a 6 weeks long course run by University of Edinburgh (edX)

Duration: 5 weeks

Led by: Duke University (Coursera)

It will provide you hands on experience in data visualization and numeric statistics using R and RStudio.

The course will first take you through basics of probability and data exploration to give a basic understanding to get started. Then, it will individually explain various concepts under each topic in detail. At the end, you will be tested on a data analysis project using a real-world dataset.

The course is led by a Professor in Statistics at Duke University and is also a prerequisite for Statistics in R specialization. If you are looking forward to learn R for data science, then you must take this course. The course is open for enrollment.

Prerequisites: Basic Statistics and knowledge of R

Duration: 1 week

Led by: Davidson College (Udemy)

As the name suggests, this course tells you how maths is being used everywhere from Angry birds to Google. It is a fun approach to applied mathematical concepts.

In this course, you will learn how equation of lines is used to create computer fonts, how graph theory plays a vital role in angry birds, linear systems model the performance of a sports team and how Google uses probability and simulation to lead the race in search engines.

The course is led by the mathematics professor at Davidson College and is open for enrollment.

Prerequisites: Understanding of linear algebra and programming

Duration: 6 weeks

Led by: Purdue University (edX)

This course is designed for anyone looking for a career in data science & information science. It covers essentials of mathematical probabilities.

In this course, you will learn the basic concepts of probability, random variables, distributions, Bayes Theorem, probability mass functions and CDFs, joint distributions and expected values. 

After taking this course you will have a thorough understanding of how probability is used in everyday life. The course is open for enrollment. 

Prerequisite: Basics Statistics

Duration: 4 weeks

Led by: Johns Hopkins University (Coursera)

Honestly, the “Bio” in “Biostatistics” is misleading. This course is all about fundamental probability and statistics techniques for data analysis.

The course covers topics on probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and logs.

A well-paced course with a complete introduction to mathematical statistics. 

Prerequisites: Basic  Linear algebra, calculus and programming useful but not mandatory

Duration: 5 weeks

Led by: Davidson College (edX)

This is an interesting course on applications of linear algebra in data science.

The course will first take you through fundamentals of linear algebra. Then, it will introduce you to applications of linear algebra for recognizing handwritten numbers, ranking of sports team along with online codes.

The course is open for enrollment.

Prerequisite: Basic linear algebra

Duration: 8 weeks

Led by: Stanford University (Coursera)

In this mathematical thinking course from Stanford, you will learn how to develop analytical thinking skills. The course teaches you interesting ways to develop out-of-the-box thinking and helps you remain ahead of the competitive curve.

In this course, you will learn about analysis of a language, quantifiers, brief introduction to number theory and real analysis. To make the most of this course one must have familiarity with algebra, number system and elementary set theory.

The course starts from 9 Jan 2023 and is led by professors at Stanford. It is open for enrollment.

Prerequisites: Basic algebra, number system and elementary set theory.

Intermediate Mathematics & Statistics

By this time, you know all the basic concepts a data scientist needs to know. This is the time to take your mathematical knowledge to the next level.

Duration: 4 weeks

Led by: University of California (Coursera)

Bayesian Statistics is an important topic in data science. For some reason, it does not get as much attention.

In this course, the first section covers basic topics like probability like conditional probability, probability distribution and Bayes Theorem. Then you will learn about statistical inference for both Frequentist and Bayesian approach, methods for selecting prior distributions and models for discrete data and Bayesian analysis for continuous data.

Prior knowledge of statistics concepts is required to take this course. The course starts from 16 Jan 2023.

Prerequisite: Basic & Advanced Statistics

Duration: 8 weeks

Led by: Stanford University and University of British Columbia (Coursera)

Game theory is an important component of data science. In this course, you will learn about basics of game theory and its applications. If you are looking to master Re-inforcement learning this year – this course is a must learn for you.

The course provides basic understanding of representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games. Each concept has been explained with the help of examples and applications.

The course is led by professors from the Stanford University and The University of British Columbia. The course is open for enrollment. 

Prerequisite: Basic probability and mathematical thinking

Duration: 5 weeks

Led by: Stanford University and The University of British Columbia (Coursera)

You will learn about how to design interactions between agents in order to achieve good social outcomes. The three main topics covered are social choice theory, mechanism design, and auctions. The course starts from 30 Jan 2023 and is led by professors from Stanford University & The University of British Columbia. 

The course is open for enrollment.

Prerequisite: Basics of Game Theory

Duration: 4 weeks

Led By: Harvard University (edX)

Matrix algebra is used in various tools for experimental design and analysis of high-dimensional data.

For easy understanding, the course has been divided into seven parts to provide you a step by step approach. You will learn about matrix algebra notation & operations, application of matrix algebra to data analysis, linear models and  QR decomposition.

The language used throughout the course is R. Feel free to choose which part of the course caters more to your interest and take the course accordingly.

The course is conducted by biostatistics professors at Harvard University and is open for enrolment now.

Prerequisite: Basic Linear algebra and knowledge of R

Duration: 6 weeks

Led by: Johns Hopkins University (Coursera)

In this course, you will learn about one & two parameter regression, linear regression, general least square, least square examples, bases & residuals.

Before you proceed further let me clear, to take this course you need a basic understanding of linear algebra & multivariate calculus, statistics & regression models, familiarity with proof based mathematics and working knowledge of R. The course starts from 23 Jan 2023.

 Prerequisite: Linear Algebra, calculus, statistics and knowledge of R 

Duration: 6 weeks

Led by: Johns Hopkins University

In this course, you will learn about basics of statistical modeling multivariate normal distribution, distributional results, and residuals. 

Before you proceed further let me clear, to take this course you need basic understanding of linear algebra & multivariate calculus, statistics & regression models, familiarity with proof based mathematics and working knowledge of R. The course starts from 23 Jan 2023. 

Prerequisite: Linear Algebra, calculus, statistics and knowledge of R 

Duration: 8 weeks

Led by: University of Notre Dam (edX)

I am someone who is very curious to know how mathematics can be used to drive deeper insights in sports and everyday life.

I came across this course, which shows how your favorite sport uses mathematics to analyze data and know the trends, performance of players and their fellow teams.

In this course, you will learn how inductive reasoning is used in mathematical analysis, how probability is used to evaluate data, assess the risk and outcomes of any event.

All the major team sports, athletic sports, and even extreme sports like mountain climbing have been covered in the course. The course is led by professors of the University of Notre Dam and is currently open for enrolment.

Prerequisite: Statistics & Linear Algebra

Advanced Mathematics & Statistics

Bravo, by now – you would be on your own. You would have developed a knack for mathematics & statistics and would feel confident about continuous learning – way to go!

Duration: 8 weeks

Led by: University of Melbourne (Coursera)

Every industry & company makes use of optimization. Airlines use optimization to ensure fixed turn-around-time, E-commerce like Amazon uses optimization for on time delivery of products.  Macro-level applications of optimization includes deploying electricity to millions of people,  way for new medical drug discoveries and many more.

The prerequisites to take this course are good programming skills, knowledge of fundamental algorithms, and linear algebra. The course starts from 16 Jan 2023 and is conducted by professors at Melbourne University.

Prerequisite: Programming, algorithms and linear algebra

Duration: 4 weeks

Led by: Johns Hopkins University

If you aspire to become a generation sequencing data scientist then you must take this course.

In this course, you will learn about exploratory analysis, linear modeling, hypothesis testing & multi-hypothesis testing, different types of process like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.  This course is part of Genomic Data Scientist specialization from Johns Hopkins. The course starts from 16 Jan 2023.

This course is part of Genomic Data Scientist specialization from Johns Hopkins. The course starts from 16 Jan 2023.

Prerequisite: Advanced Statistics and algorithms

Duration: 8 weeks

Led by: utmb Health (edX)

This course is an introduction to data analysis using biomedical big data.

In this course, you will learn about fundamental components of biostatistical methods. Working with biomedical big data can pose various challenges for someone not familiar with statistics.

Learn how basic statistics is used in biomedical data types. You will learn about basics of R programming, how to create & interpret graphical summaries of data and inferential statistics for parametric & non-parametric methods. It will provide you hands on experience in R with biomedical problem types.

The course is open for enrolment.

Prerequisite: Advanced statistics and knowledge R

End Notes

I hope you found this article useful. By now, you would have identified the learning areas for yourself. If you are from mathematics background, you can choose the right courses for yourself. On the other hand, if you do not have a mathematics background, then start from the beginners sections and move ahead.


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