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Spacing and Retrieval Practice in Health Professions

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Spacing and Retrieval Practice in Health Professions

The summary of the articles they found paints a picture of who is implementing and studying these learning strategies. Not surprisingly, introductory psychology courses were the most common setting to find studies (there were 16 studies like this) on the use of retrieval practice and spacing. After that, the most common setting to find these studies was in either anatomy (6 studies), physiology (8 studies), or anatomy and physiology (4 studies) courses. I think this is interesting because it speaks to the need to better understand how these learning strategies are applied within specific disciplines. While we have a good idea of how these strategies been implemented in psychology and anatomy & physiology course work – the success and the limitation; we know somewhat less about the potential success or limitations of implementing these strategies in, say, a cardiology course in medical school.

The authors also note that the most common type of retrieval practice was recognition or cued recall, with free recall being less frequent. In other words, retrieval was often assessed via multiple-choice assessments. They noted that when forms of retrieval practice were compared that free recall was more effective than recognition or cued recall. For more on differences in types of retrieval practice, check out one of our early podcasts (Episode 3 – Bite-Size Research on Retrieval Practice Formats). In terms of spaced practice they note that only 5 studies compared the different possible type of distributed practice (expanding, equal, and contracting; for more on schedules of spaced practice see this review by Carolina). Of those, 3 of the 5 found an expanding schedule to be better.

One of the major critiques that the authors had of the literature is that very few studies reported time on task or reported on stakes of assessments. The premise of their critique, I believe, is that time is a valuable resource for students. All things being equal, if the outcome of two learning strategies was the same, but one took less time, then that strategy would be superior. Certainly if a learning strategy produced less learning and took more time it would be considered much less effective than one that produced more learning and took less time. While this is true, I think this critique is much more applicable to post-graduate education.

For example, a typical Introductory Psychology course takes place over about 15 weeks. Further, introductory psychology courses are commonly used as electives and requirements for students who are not psychology majors. Therefore, students in these classes are typically first-year students with no interest in pursuing clinical psychology. As such, the educational goals and outcomes of these courses are often to give people a broad introduction and interest in the field. Compare that to the 6 week course on Neuroscience and Behavior that students get at my medical school. The educational goals and outcomes of this course are vastly different from the Introductory Psychology course. Here, the goal is to not only cover the basics, but to prepare students for clinical rotations in psychiatry where they will be expected to diagnose and develop treatment plans based on the DSM-5. Time on task is a much more pressing concern for these students at this level of education, as are the stakes. Clearly there are differences in what effectiveness or efficiency might mean across these two settings even though there is an overlap in content and learning strategy.

Overall, the authors conclude that both distributed practice and retrieval practice are effective at improving academic grades in health professions education (yay!). For me, this review highlighted the need to conduct more research on the implementation of these strategies in post-graduate settings in health professions education to better understand what successful implementation looks like in these settings.

References

  1. Trumble, E., Lodge, J., Mandrusiak, A., Forbes, R. (2024). Systematic review of distributed practice and retrieval practice in health professions education. Advances in Health Sciences Education, 29, 689-714. https://doi.org/10.1007/s10459-023-10274-3

Understanding AI: Myths, Hallucinations, and Realities

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Understanding AI: Myths, Hallucinations, and Realities

I wrote this article on LinkedIn, and heard from a lot of people who said I should repost it on my blog, for those folks who either do not follow, nor are aware of some of the stuff I post on my thread.

The article focuses on what you may have heard – which sadly contains a lot of misinformation, myths, misnomers, and so on.

Without further adieu

A lot of information is being presented and discussed around generative AI, which some folks, including other experts, refer to as AI.

What I do find, though, is that while people espouse websites to learn about AI, provide its pluses, or suggest products to use that have AI, they (as a whole) ignore quite a bit of data that, again, as a whole—people who are diving into AI—are unaware of.

For example, any LLM on the planet can produce fake or false information, which the industry calls hallucinations.

Some companies tell customers of their products that, because of guardrails, their product (SaaS) does not produce hallucinations (they refer to this as fake or false information).

Equally, they tell the company that it is 100% accurate because the content it puts into their system is 100% private and, therefore, 100% correct.

The company assumed that the private because it is only their content, would be accurate, as noted above.

This is a falsehood.

Another company hired a consultant who claimed to be an AI expert.

They recommended an LLM but failed to mention the hallucinations, potential AI bias, prompt leaking, and other continuously popping-up issues.

Instead, they told the client it was safe since it was not accessing the Internet.

Surprise – they were wrong.

Even the private content you have put into any platform with an LLM can still produce fake or false information.

Again, it is an inherent flaw of AI.

Let’s say the vendor uses a R.AG (“RAG “) and adds guardrails.

There is a probability that it will produce fake or false information.

Now, you can say, “Well, Craig says probability, which doesn’t sound like it will ever happen.”

Reality, though, says it will happen. Maybe not to you, Fred or Sarah using it, but someone in your company will experience the hallucination.

It doesn’t matter whether it is your employee or where they are accessing it; AI doesn’t care.

Another somewhat unaware statement is that solutions like Copilot, Gemini, or other AI products used within a browser can produce fake or false information.

The problem is that, overwhelmingly, end users have yet to learn. The idea that they are aware needs to be corrected.

In addition, people will see the references to where this information came from and consider this a verification of accuracy.

Yet, that reference is pulling information from a website, which may not exist, or if it does, can show the wrong information or, as everyone knows, is nowadays full of content marketing and just plain junk.

I once spoke with an artificial intelligence expert who told me that a family member went on the Internet and typed in some plant information, and the AI told them to put X product in there to help them grow.

What happened? It killed the plants.

There have been cases where the AI has told people to put all types of stuff on their pizza, which is harmful.

AI bias is another issue. Something many people do not know.

Strengths and Weaknesses of an LLM?

Yep, there is not one LLM that is perfect.

Thus, I recommend at least two LLMs, to help offset the weaknesses that any of them will present.

Prompt Leaking

Another issue with AI.

The idea that someone with a technical background is pulling this off must be more accurate.

It can be anybody.

The approach works this way: The person repeatedly types several letters or words, breaking through the AI.

The result?

The person can see all types of data the company does not want them to see.

Financial data is just one example.

Token Fees

When an end user uses AI, for example, by typing questions or statements into the prompt window, they see it as just the box that appears. Token fees start.

A token is a character in a word. Using the token calculator, which I recommend, the sentence, “More than 200 people are reading this article,” equated to 45 characters.

While the cost of token fees is minuscule, they can add up quickly depending on the number of people asking questions and doing so in a manner that isn’t specific right away.

The AI Summit in 2023

I attended the AI summit last year in Amsterdam, and throughout all the sessions, not one speaker or panelist who discussed generative AI mentioned token fees.

When I raised my hand and asked about token fees and their cost impact on companies, only one person—a professor emeritus of Economics from Oxford—noted that the question was correct and said that a risk-averse company should not implement AI.

Mass Amount of Energy

Training AI requires a massive amount of energy.

According to the International Energy Association, “energy consumption from data centers, cryptocurrency, and AI 2026 will be roughly equal to the amount of electricity used in Japan.” (IEA, https://bit.ly/4dJoczy)

The water needed to cool down the computers and the data centers isn’t a tiny bit.

Shaolei Ren, an associate professor from UC-Riverside, “projects that the water consumption of AI demand globally will equate to four to six Denmark’s water usage (withdrawal) by 2027.” (S.Ren, Newsweek, https://bit.ly/3NtF8zf)

SLMs can reduce the impact, yet a company should be aware of it.

That may be all they need, or they may need to get at least one LLM.

Job Gain and Job Losses

While many people say AI will create more jobs and then lose jobs, this is a misnomer.

It depends on what type of job one does.

If you are human-facing, then yes, your job is safe.

If you answer the phone for customer service, you are no longer needed as AI improves.

Using an AI, the synthetic voice will sound human.

Today, yes, companies need prompt engineers.

It could be anyone, as long as they have critical thinking skills.

Now?

They need coding skills.

But not all is lost.

Companies need to look beyond today with AI to the future and its impact.

Instead of eyeing upskilling for employees, they should be saying and implementing reskilling.

Let’s say an employee is learning skills for a current job role.

However, AI automation will replace that job in a year or two.

If the employee is someone you want to keep around, then focus on reskilling for a new role that will appear due to AI.

Companies, however, are ignoring this (as a whole)

Thus, upskilling, current skills for jobs that, if one looks enough at them, will likely be eliminated due to AI, are the same one’s companies focus on.

Clerical jobs will be lost. Specific accounting jobs will no longer be needed.

Productivity with AI

There are companies using AI that see increased productivity because it eliminates all the tasks needed for an employee to do their job.

Think though about an employee who either has a B personality or one, let’s admit it – is a slacker.

Why complete the remaining tasks when AI could do it for them?

As for the productivity boost, what happens when a person who is not told or aware of the potential pitfalls of AI, thus not checking (a human element is crucial here) to see if it is correct, sends it to whomever?

Their boss needs to be made aware of the pitfalls or potential issues.

The boss then presents it, and the company’s senior executive, or the CEO, thinks it is correct.

In today’s real world, employees use ChatGPT (the free version) without telling their managers.

In one case, an executive told me that an employee provided materials they were asked to complete, failing to mention to the executive that they used ChatGPT.

Unaware of AI usage and potential issues, the executive accepted it as accurate.

I know some executives using ChatGPT and are unaware of fake or false information, such as AI bias.

How would they know?

The idea that people know is wrong. People as a whole need to read the latest around AI.

Nor should a company assume they are.

Profitability with AI today?

There is not one company out there – that is profitable—not one.

Microsoft, Amazon, and Google can handle those losses.

On the other hand, there is Open AI.

Their valuation is 157 billion dollars (USD).

Open AI projects a loss (2024) of five billion dollars, and it says it is not expecting to be profitable until 2029. (Information Report, Information, https://bit.ly/3BYV4Ha)

Companies pushing out AI products/systems

Many companies that have developed AI products and systems will fail and disappear.

This is due to the number of products flooding the market and the lack of profitability that many of us will find similar to the days of the dot.com era.

For those of us who were part of that experience (including myself), we all witnessed the pluses—getting capital—and the minuses—many companies failing and closing up shop or being acquired.

The job losses were tremendous.

Is this first stage of AI going to see the same implosion, where plenty of companies will go out of business, and others will survive?

To me, I see that coming.

Anti-AI?

Please don’t assume that I am anti-AI because I am not.

Rather, I am a huge supporter of it, and there are possibilities for where it can go.

AI will do wonders in the medical field, pharma, and other places where it can make a substantial positive difference.

It will help companies, regardless of size, too.

But, today, AI is at the flea, infantile stage.

Just remember that, and you should be ok.

At least for now.

E-Learning 24/7

Why Building a Culture of Learning is So Difficult

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Why Building a Culture of Learning is So Difficult

Building a culture of learning is essential for any organisation that wants to remain competitive and thrive – yours included. But achieving this isn’t always straightforward. Several challenges can stand in the way, from psychological barriers to logistical obstacles. Let’s explore the key hurdles you must overcome to create a successful learning environment.

The Key Barriers to Building a Learning Culture

Cost

You’ve likely realised that L&D often comes at a significant cost. You need substantial budgets to hire trainers, purchase learning materials, facilitate in-person sessions, and sometimes engage external consultants. Smaller and medium enterprises (SMEs) operating on tight budgets hesitate to invest heavily in learning initiatives simply because they lack the resources of bigger organisations.

Resources

Even if you have the required financial resources, you will soon realise that a learning culture demands equally valuable time, effort, and human resources. You need to give your employees time away from their regular duties so they can participate in training. You need skilled trainers and adequate technology to develop and deliver these programmes. The scarcity of these resources can hinder the development of a robust learning culture. Your employees may feel overwhelmed by their existing workloads without the opportunity for growth.

Risk of Getting it Wrong

You must try different ways to implement a learning culture before striking gold. There’s always the risk that your chosen methods or programs won’t achieve the desired results. This uncertainty about the effectiveness of a L&D programme can raise fears of wasted resources and effort. It can keep leadership from investing heavily in innovative training solutions, however potentially valuable they may be. The unhappy result is suppressed creativity and reduced experimentation with new training methods or technologies.

Convincing Stakeholders

You need the buy-in of all stakeholders, including top management, about the long-term benefits of a learning culture. Demonstrating the ROI of learning initiatives to stakeholders can be difficult, especially if they do not see immediate benefits. To convince them, you will need solid data and success stories, which may not always be readily available.

How Alison’s Free LMS Can Help

Alison’s Free LMS (Learning Management System) is designed to offer practical solutions to many of the challenges mentioned above:

  • Cost-effective: Our Free LMS provides access to over 5,000 high-quality courses at no cost. Thus, it becomes easier for organisations such as SMEs and non-profits to implement comprehensive training programs and upskill their employees without financial strain.
  • Resource efficiency: Thanks to the platform’s user-friendly design and ease of setup, you can quickly deploy training resources, requiring minimal time and effort from your organisation. Its pre-curated learning paths and the option to create custom courses make it easier to build a sustainable learning culture. Additionally, it supports unlimited users, ensuring all your employees can participate without extra costs or logistical hurdles.
  • Reducing risk: You will be pleasantly surprised at our Free LMS’s wide range of courses and learning paths. Your organisation can now experiment with different training methods without experiencing significant financial risk. The platform includes comprehensive reporting and real-time analytics so you can easily track progress and measure the effectiveness of the chosen learning initiatives. This data-driven approach reduces the risk associated with training investments by providing insights into what works and doesn’t.
  • Stakeholder buy-in: Customisable learning paths tailored to individual employee needs enable improved employee performance and satisfaction. You can use Alison’s detailed reports and analytics to quickly demonstrate to stakeholders the impact of the chosen learning programs. Success stories and verified reviews posted by satisfied organisations can also help convince your stakeholders that Alison’s Free LMS is worth its weight in gold.

Alison’s Free LMS offers a versatile platform that can be customised to meet the unique needs of various industries. It delivers value across multiple sectors with features like industry-specific learning paths, pre-curated courses, real-time analytics, and custom course content.

Industry-Specific Benefits

  • Healthcare: Offers training for medical professionals, administrative staff, and support roles, helping maintain high standards of care and compliance.
  • Information Technology: Provides programming, cybersecurity, and IT management courses to keep professionals up-to-date in this ever-evolving field.
  • Business and Management: Develops leadership skills, project management expertise, and entrepreneurial knowledge to support organisational growth.
  • Hospitality: Supports tourism, hotel management, and customer service roles with focused training that enhances operational efficiency and customer satisfaction.
  • Manufacturing: Covers critical areas such as safety training, quality control, and operational efficiency to ensure smooth and secure operations.
  • Finance: Delivers courses on accounting, financial analysis, and investment strategies to sharpen financial acumen and decision-making skills.
  • Retail: Equips employees with customer service training, sales techniques, and inventory management skills, all key to thriving in a competitive retail landscape.

Leadership’s Role in Building a Learning Culture

While your organisation may be ready to press the green button on learning and development, top management might hesitate to invest. However, successful organisations demonstrate that leadership commitment is crucial to creating a strong learning culture. Here are some real-world examples:

  • Google: Known for its strong learning culture, driven by leadership’s commitment to continuous improvement. Google offers access to online courses, workshops, and mentorship programs, promoting a growth mindset where employees learn from failures and embrace challenges.
  • McKinsey & Company: Chief Learning Officer Matthew Smith emphasises understanding employee needs and offering tailored learning opportunities. McKinsey combines external resources with in-house programs to create top-tier learning experiences.
  • Microsoft: Under CEO Satya Nadella, Microsoft has transformed its culture by prioritising a “learn-it-all” mindset over a “know-it-all” approach. The company invests heavily in training programs, helping employees continuously update their skills to stay competitive in the fast-changing tech industry.
  • IBM: Recognising the value of a learning culture, IBM offers extensive training and a system for tracking and rewarding employee learning. Leaders promote knowledge-sharing and collaboration, making learning an integral part of daily work.
  • Unilever: Leadership at Unilever has embedded learning into the company’s core values. Employees are encouraged to take ownership of their learning, with leaders supporting this culture by providing resources and recognising continuous improvement.

These examples show how leadership involvement can be instrumental in fostering a learning culture and driving individual and organisational growth.

Building a culture of learning may seem challenging, but Alison’s Free LMS offers solutions to common barriers such as cost concerns, resource limitations, and stakeholder engagement. Adopting this tool allows your organisation to create an environment that prioritises continuous learning and development.

Sign up for Alison’s Free LMS today to unlock these benefits across your organisation.

 

Once Upon a Time

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What do you think this image is communicating?

Let’s Discuss: The Gender Gap Among Gen Z Voters

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Why do more young women favor Kamala Harris while more young men favor Donald Trump? Read this pair of articles and post your comments and questions for Claire Cain Miller by Oct. 31.

Should Parents Be Up Front About Ugliness in the World or Try to Hide It?

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When it comes to natural disasters, wars and other tragedies, do you want your parents to be honest with you or to reassure you that everything is going to be OK?

Word of the Day: emblematic

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This word has appeared in 274 articles on NYTimes.com in the past year. Can you use it in a sentence?

How to Think Like a Programmer

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How to Think Like a Programmer

A common misconception that people have about programmers is that they know everything. In reality, most programmers aren’t human encyclopedias — they search things on Google and Stack Overflow constantly and rely on autocomplete just like the rest of us.

What programmers do have is a unique approach to solving problems that comes with knowledge and experience. Ahead, we’ll explore what that programmer mentality is all about and share tips to help you get there, too.

What is the programming mentality?

Let’s think about programming as speaking for a moment. When you speak, you’re aiming to communicate effectively with the people around you. When you program, you’re determining instructions to give to a computer so it can execute a task. Code transforms ideas into a written language that a computer can understand. In both cases, you don’t need to know every single word or element of a language, you just need to know enough that you convey your ideas so that they are understood.

When you use code to solve a problem, it’s less about memorizing snippets of code and using them. It’s more about knowing the fundamentals and applying them. When you understand how to break a problem down into smaller pieces and apply the principles of programming, you don’t need to memorize everything. You simply learn how to approach a challenge or problem, and that is a skill you can apply every time you code.

Before you dive into a particular language, you can get familiar with the core concepts of coding in the skill path Code Foundations. This beginner-friendly path is an awesome overview of the main branches of programming (computer science, web development, and data science) and common concepts that show up across all domains.

Break things down into building blocks

Let’s look at a simple example of how you break a problem down into its building blocks, like a programmer approaches a problem.

If you were to describe to a machine (or a person who’s new to a task) how to open a jar of pickles, you couldn’t just say “open jar.” You’ll need to explain in a language they understand the steps required to open that jar. For example, you might offer the following set of instructions:

  1. Pick up jar with left hand
  2. Put right hand over the lid
  3. Tighten both hands
  4. Rotate right hand counterclockwise, and rotate left hand clockwise
  5. Rotate until lid separates from bottom jar
  6. Release tension in both hands

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Simple tips to get you thinking like a programmer

Everyone’s programmer perspective is different; meaning, each programmer thinks in a different way and learns how to approach and break down problems uniquely. Through practice, you’ll develop your programming perspective, and it’ll get easier to communicate with machines to solve increasingly complex problems.

As you develop your own perspective, here are a few tips and tricks you can use to learn to think like a programmer and develop this mentality.

1. Talk through the problem aloud

First, you can talk through the problem by calling up a friend, family member, or fellow learner in the Codecademy community and explaining to them what you’re trying to tackle. By having a conversation with someone, you’ll be able to determine how you can communicate with them to clearly explain the problem and your solution.

Don’t have anyone to talk to right now? No problem! Just try speaking through the problem out loud. Explain your approach and understanding to your pets or even a stuffed animal. It’s speaking aloud that is the key.

2. Collaborate (work with other programmers)

Programmers don’t always work by themselves at their computers. They like to learn from each other. When the problem is complex, working with other programmers helps bring together the best ideas from many people, making it easier to come up with innovative solutions.

Try pair programming, which is when two programmers share a computer to solve a problem together. One programmer (the driver) writes the code, and the other (the navigator) reviews the code and directs how the problem is broken down and solved. Every few minutes, the two switch roles. This gives you the chance to see another programming perspective at work. How your partner solves a problem will be different from you, and this helps expand your mindset and learn to approach problems from multiple angles.

3. Take it one step at a time

Whether you are programming or starting on any big project, large problems can seem scary at first, and it’s easy to get overwhelmed. The best way to combat this feeling is to look at the problem from a high level and then break it down into smaller chunks.

Just like we talked about earlier, when you split a problem down into smaller pieces, you can then apply the fundamentals of programming to solve each piece. Approach each chunk of work separately so that the task is manageable, and you can start to make some real progress, one step at a time. This technique can also be applied to goals that you want to achieve!

4. Start simply: how would you solve a similar, simpler problem?

You can also consider how you might solve a problem that is similar to what you’re attempting to solve, but much simpler. Then write the code to solve that small problem. Slowly but surely, introduce complexity to solve the larger problem you were presented with at the beginning.

5. Practice, don’t memorize

Memorizing code is tough, and you don’t need to go down that road to think like a programmer. Instead, focus on the fundamentals. Learn the principles and ideas behind programming, and you’ll get much further than trying to remember everything.

Every time you solve a simple problem using programming, you’ll develop your fundamentals even further, making it easier as you progress. Codecademy’s practice projects are a great place to start applying your skills to real-world scenarios. Practice is key to your programming perspective becoming second nature.

When you use code to solve a problem, it’s less about memorizing snippets of code and using them. It’s more about knowing the fundamentals and applying them.

6. Don’t worry if you need to look up the right syntax

Even some of the best programmers around need to look up syntax when they don’t remember things. So don’t sweat it if you need to search online or in developer documentation for the syntax that you need.

You don’t need to be an expert at all things, because you can program if you know the fundamentals, and we’re all capable of learning new things.

7. Shortcuts can be dangerous

Taking shortcuts while you’re learning to program can be more hurtful than helpful. Try to think of the learning process like training for a running race.

Rather than searching for an answer right away when you encounter a problem, try to solve it yourself first. When you rely on other people’s coding solutions, you don’t get to develop that programming muscle yourself.

Focus on the basics, put in the practice, and stick to your training plan. The tried and true training methods are that for a reason; they work! We know that it can be frustrating when it takes time to learn to program, but that’s all part of the process of forming your own programming perspective.

8. Get help after you’ve exhausted other options

If you’ve really tried your hardest and approached the problem from many different perspectives, but you’re still struggling, now’s the time to ask for help. Reach out to someone in the Codecademy community to get some guidance, or use our AI Learning Assistant to get to the bottom of your specific coding problem.

9. Debugging tests your knowledge

Debugging your own code allows you to take a step back and see the opportunities for improvement in your own work. It can be easy to blame the machine every time something goes wrong. But if you take a moment and analyze how you’re approaching the problem, you can see where you might have gone wrong. Understanding this will help you to identify errors in the future quicker, and you’ll naturally get better by avoiding these issues in the long run.

10. Get familiar with reading documentation and applying it to your code

Documentation for programming is just like a recipe for cooking. It lays out how the code is intended to work and is an excellent resource to help you understand programming better. Learning to read documentation will also steer you away from looking for a shortcut or an easy solution to your problems or bugs.

A great resource to explore is Codecademy Docs, our community-driven documentation for popular programming languages and frameworks. Read up on concepts you know, and consider contributing to Docs for extra practice with open-source projects.

Putting your programming into practice

If you’re itching to start practicing to learn how to think like a programmer, we have lots of resources to help. Take a look at our free professional skills courses to become a better communicator, critical thinker, collaborator, and emotionally intelligent leader. Explore projects that you can build for your portfolio or just for practice. And get involved with the Codecademy community to meet other budding programmers who are in the same position as you!

Remember to focus on how to solve a problem and to learn as you go. Don’t get hung up on needing to do things “right” or to be “perfect.” There are many ways to solve a problem, and, with practice, you’ll build your own unique programming perspective!

The Role of Data Science in Electoral Politics & 3 Careers You Can Have

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The Role of Data Science in Electoral Politics & 3 Careers You Can Have

Election Day is Tuesday, November 5 in the United States. Whether you’ve been volunteering leading up to the big day or are planning to proudly wear your “I Voted” sticker, getting involved with an election cycle is invigorating.

Some people turn their passion for politics into a career. In fact, there are lots of ways that people who understand data science can apply their technical skills to elections. Data science is all about turning data into information of value. Using code, we can take huge amounts of data and use it to answer questions, predict possible outcomes, understand trends, and visualize relationships and patterns.

From polls and surveys to votes and demographics, the US electoral system is chock-full of data that political candidates and elected officials use to make informed decisions. Curious what types of political careers you can have in data science? Here’s an overview of the data science careers you can have in politics, plus the skills you need to get hired. Be sure to check out our full catalog of data science courses and paths to start learning these impactful (and marketable) skills.

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Campaign Data Analyst

In recent years, it’s become common for political campaigns to hire teams of Data Analysts who can translate data into actionable plans that help guide a campaign. For example, a Data Analyst for a political campaign might look at demographic data to pinpoint areas where a candidate should extend outreach and mobilize voters. Or, they might use data to guide a candidate’s messaging strategy in advertisements and fundraising campaigns. 

Deciding where a campaign should focus its funds is also a big part of this specialty. “The job of analytics on these big campaigns and on small campaigns is how do we spend our money,” Becca Siegel, Senior Advisor to Kamala Harris, said in a keynote address at the Political Analytics Conference in 2024. Becca, who served as Chief Analytics Officer for the 2020 Biden campaign, started her career in political organizing before getting into technology, statistics, and mathematics. 

There’s no one path to working in the data science field. Rayid Ghani, Chief Data Scientist of President Obama’s 2012 election campaign, gave this advice for aspiring data professionals: “In addition to getting the technical skills in statistics, machine learning, and computer programming, take some classes in the social sciences, learn how to define problems, and communicate with people about the solution you’re developing as well as its impact,” he told the American Statistical Association. 

Political Scientist

Broadly speaking, the role of a Political Scientist is to research political ideas and analyze governments, policies, and political trends, according to the U.S. Bureau of Labor Statistics. A Political Scientist will use statistical analysis to gather and interpret survey data on voters, research the effects of policies on people, or come up with solutions to political problems, for example.

R and Python are a couple popular programming languages that are used for statistical analysis. In Learn Statistics with R, you’ll grasp some fundamental statistics concepts and understand how to use the popular programming language.

If you already know Python, you can also check out Learn Statistics with NumPy, where you’ll use a Python module to perform numerical operations on large quantities of data. One of the portfolio projects in this course will have you reviewing survey results from a fictional election using binomial distributions to see how the responses compare to actual election results. (If “binomial distributions” sounds like a foreign language, don’t worry — you’ll learn what that means in the course.)

Data Journalist

A Data Journalist is a Reporter who uses analytical and coding skills to tell stories about current events and news. Using code, Data Journalists can examine structured data and find nuanced trends and observations that might be missed in traditional news coverage and interviews. From tracking election returns in real time to analyzing voter polls, Data Journalists play a crucial role in a news organization’s political coverage.

The website FiveThirtyEight, for example, is a widely-read publication that’s known for its data-driven election and sports forecasting. The New York Times has a dedicated Elections Data Analytics team made up of Data Journalists and programmers who cover election cycles. (You might remember the infamous “needle” visualization that the Times introduced during the 2016 Presidential election to illustrate election night forecasts — that’s data journalism in action!) The Wall Street Journal also has a data team of reporters who specialize in turning large datasets into narratives around topics like business, government, healthcare, and more.

Data Journalists need strong writing skills, plus programming knowledge. R is commonly used by Data Journalists, because they can use it to create compelling data visualizations that help to tell a story and communicate findings to readers. Python, with its English-like syntax and versatility, is another popular programming language used by journalists.

Get started with data science

Inspired to learn more about data science? Codecademy has lots of data science courses for all levels.

A great place to start your coding journey is with our free course Getting Started with Python for Data Science. There’s also our Data Science Foundations skill path, and in the no-code course Principles of Data Literacy, you’ll learn how to analyze data confidently and responsibly. Or you can jump in with Analyze Data with Python to learn the popular programming language used for data analytics. (If you’re on the fence or don’t know whether data science is right for you, take our programming personality quiz.)  

If you’re preparing for any of these careers in data science, you might want to try our career path Data Scientist: Analytics Specialist. You’ll get comfortable “talking” to databases and creating visualizations that drive big picture decision-making, plus work on portfolio-grade projects that you can use to apply for jobs. The beginner-friendly career path Data Scientist: Machine Learning Specialist will teach you all the skills you need to draw predictions from data.

Here are some additional ways you can combine your interests in data science and politics for fun or a new career:

  • Explore datasets: The MIT Election Data and Science Lab has lots of free datasets related to past US elections that you can download and examine.
  • Analyze State of the Union Addresses: Apply a machine learning technique called natural language processing that uses AI to comb the text of past presidents’ State of the Union Addresses. You can also perform sentiment analysis to detect positive or negative sentiment in the speeches.
  • Become a volunteer: Data Scientists and Engineers can apply to be a volunteer for The Center for New Data, a non-profit that uses data science to measure and address voter suppression.
  • Browse job boards: Want a technical job in government? The job board TechToGov has lots of resources for folks who want to work in public interest technology and government service, like a guide to reading government job titles and tips for breaking into civic tech.  

For information about how to vote in the US, head to vote.org. 

This blog was originally published in November 2022 and has been updated to include relevant information about the 2024 U.S. presidential election. 

Do You Have a Hard Time Letting People Down?

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“Put yourself first” is often good advice when it comes to taking care of your mental health. Are you able to follow it?

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