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Career Planning and Salary  - Explore Careers

Career planning and salary  -, explore careers, career planning and salary explore careers.

Career Planning and Salary: Explore Careers

Lesson 2: explore careers.

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Explore careers

What types of positions are currently available in this field? What are the average working conditions and expectations? Will this career be in demand five years from now? Research is critical as you begin exploring different career paths.

In this lesson, you'll learn what to look for as you start researching careers. We'll also talk about various online and direct resources that will help you gather valuable information.

How to research

Before you begin exploring different career paths, it's important to understand what you should be looking for in your research. Asking the right questions can make finding a career a lot easier.

Click the arrows in the slideshow below to learn what information to gather in your career research.

Research strategies

You should make use of various methods and resources as you gather information about different careers. Taking a diverse approach to your career research will help to ensure the information you collect is accurate and helpful .

Career research guides

The Occupational Outlook Handbook from the U.S. Department of Labor is the most comprehensive resource for researching any career. You'll be able to learn about the nature of the work , normal working conditions , average salaries , projected job outlook , and more.

CareerOneStop is also a powerful career planning tool. A central resource in the new American Job Center Network , it allows you to browse careers that are in demand , compare different occupations , create a career plan , and learn valuable resume writing and interviewing skills.

MyNextMove , on the other hand, will guide you through the career research process and provide a quick, visual summary of the information you need to know. This is a great resource if you're having a difficult time getting started. You might also try another career planning guide, such as US.jobs .

General advice

Career research doesn't just happen online. There are several ways you can learn about careers directly. Let's take a look at some of the most helpful methods.

Obtain first-hand knowledge

Learn directly from someone in the field. You might visit a workplace , job shadow, or interview a person with a position in your potential career. Entry-level positions, internships, and volunteering allow for more in-depth knowledge. You can use volunteer search sites like AmeriCorps.gov or Idealist.org to look for internship and volunteer opportunities in your community.

Research industry organizations

Many industries have professional and trade organizations . These organizations can be valuable resources for learning the latest industry trends and training requirements, as well as making connections with the field. You can find different industry organizations by using this list of industry groups on Wikipedia.

Attend a career fair

Career fairs allow you to meet representatives from a variety of different industries and professions you might not otherwise meet. Talking directly with someone from a career field might give you a different perspective on a position or career path. You can find upcoming career fairs in your area on NationalCareerFairs.com .

Visit a career center

Many community centers, libraries, and colleges have career centers with resources that can help you learn more about what to expect from different careers. Visit the Career One Stop service locator page to find career centers near you.

• Download the Career Research Worksheet ( .doc or .pdf ).
• Navigate to one of the career research websites above to assist you in your research.
• Try answering the questions in the worksheet to start researching a career.
• Think about how you might use one of the methods above to learn more about a career path.

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Research Worksheets and Handouts

• Getting Started
• Evaluating Sources
• General Research

Getting Started Having trouble getting your research rolling? These handouts and worksheets can get you past that initial hurdle.

Topic Identification worksheet (pdf) This graphic organizer will help you understand your assignment, identify and focus your topic, create a search strategy and find sources in 6 easy steps! For more information about research topics, visit www.codlrc.org/research101/topics

Developing Your Research Question (pdf) An infographic of journalistic questions that can help you brainstorm potential research questions.

Finding Evidence worksheet (pdf) Before you start your research, consider what evidence you’ll need to support your claims and think about how to find it.

Subject vs. Keyword Searching (pdf) Learn how to use keyword searching and subject searching together to find what you're looking for in the Library catalog and article databases.

Boolean Logic, Truncation, and Nesting (pdf) An introduction to advanced search techniques you can use to help you find information efficiently and effectively.

Advanced Research Search Strategies and Techniques (pdf) A quick reference for the types of advanced searching techniques you can use in databases, the Library catalog and in search engines.

Tips for Evaluating Information (pdf) Whether a resource is print or electronic, text-based or image-based, researchers must carefully evaluate the quality of the source and the information found within. When evaluating the quality of resources, here are some things to consider.

CRAAP Test (pdf) Do your sources pass the CRAAP Test? Use this guide to help you consider whether a source is appropriate for your research needs.

Source Evaluation Worksheet (pdf) Use this form to help you determine if a source is appropriate for your research. For more information about evaluating sources, visit www.codlrc.org/evaluating/sources

Research Article Anatomy (pdf) Reading research gets easier once you understand and recognize the pieces and purposes of research studies, from abstract to references.

Reading (and Understanding) Research (pdf) Adapted from How to Read and Understand a Scientific Paper: A Guide for Non-Scientists by J. Raff.

Introduction to College Research (pdf) Helpful resources for every stage of the research process.

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FREE Content Analysis and Thematic Analysis Worksheets (AQA A-Level Psychology) Research Methods

Subject: Psychology

Age range: 16+

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12 May 2024

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This resource comprises two meticulously crafted worksheets focusing on Content Analysis and Thematic Analysis within the realm of Psychology. Tailored to cater to students of varying abilities, these worksheets are designed to simplify complex concepts, making them easily digestible for learners. Through clear explanations and relatable examples, students are guided through the process of applying these analytical techniques in a psychological context. What sets these worksheets apart is the inclusion of a ‘Checking Understanding’ section, strategically placed to ensure maximum comprehension. Whether utilised in the classroom or assigned as homework, these worksheets serve as invaluable tools for reinforcing key concepts and fostering a deeper understanding of content analysis and thematic analysis in Psychology.

• Two comprehensive worksheets: Content Analysis and Thematic Analysis
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Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work

May 8, 2024 | Jared Spataro - CVP, AI at Work

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One year ago, generative AI burst onto the scene and for the first time since the smartphone, people began to change the way they interact with technology. People are bringing AI to work at an unexpected scale — and now the big question is, how’s it going?

As AI becomes ubiquitous in the workplace, employees and businesses alike are under extreme pressure. The pace and intensity of work, which accelerated during the pandemic, has not eased, so employees are bringing their own AI to work. Leaders agree AI is a business imperative — and feel the pressure to show immediate ROI — but many lack a plan and vision to go from individual impact to applying AI to drive the bottom line.

At the same time, the labor market is set to shift and there’s a new AI economy. While some professionals worry AI will replace their job, the data tells a more nuanced story — of a hidden talent shortage, more employees eyeing a career change, and a massive opportunity for those willing to skill up.

“AI is democratizing expertise across the workforce,” said Satya Nadella, Chairman and Chief Executive Officer, Microsoft. “Our latest research highlights the opportunity for every organization to apply this technology to drive better decision-making, collaboration — and ultimately business outcomes.”

For our fourth annual Work Trend Index, out today, we partnered with LinkedIn for the first time on a joint report so we could provide a comprehensive view of how AI is not only reshaping work, but the labor market more broadly. We surveyed 31,000 people across 31 countries, identified labor and hiring trends from LinkedIn, analyzed trillions of Microsoft 365 productivity signals and conducted research with Fortune 500 customers. The data points to insights every leader and professional needs to know — and actions they can take — when it comes to AI’s implications for work.

1. Employees want AI at work — and won’t wait for companies to catch up.

Three in four knowledge workers (75%) now use AI at work. Employees, overwhelmed and under duress, say AI saves time, boosts creativity and allows them to focus on their most important work. While 79% of leaders agree AI adoption is critical to remain competitive, 59% worry about quantifying the productivity gains of AI and 60% worry their company lacks a vision and plan to implement it. While leaders feel the pressure to turn individual productivity gains into organizational impact, employees aren’t waiting to reap the benefits: 78% of AI users are bringing their own AI tools to work. The opportunity for every leader is to channel this momentum into ROI.

2. For employees, AI raises the bar and breaks the career ceiling .

We also see AI beginning to impact the job market. While AI and job loss are top of mind for some, our data shows more people are eyeing a career change, there are jobs available, and employees with AI skills will get first pick. The majority of leaders (55%) say they’re worried about having enough talent to fill open roles this year, with leaders in cybersecurity, engineering, and creative design feeling the pinch most.

And professionals are looking. Forty-six percent across the globe are considering quitting in the year ahead — an all-time high since the Great Reshuffle of 2021 — a separate LinkedIn study found U.S. numbers to be even higher with 85% eyeing career moves. While two-thirds of leaders wouldn’t hire someone without AI skills, only 39% of users have received AI training from their company. So, professionals are skilling up on their own. As of late last year, we’ve seen a 142x increase in LinkedIn members adding AI skills like Copilot and ChatGPT to their profiles and a 160% increase in non-technical professionals using LinkedIn Learning courses to build their AI aptitude.

In a world where AI mentions in LinkedIn job posts drive a 17% bump in application growth, it’s a two-way street: Organizations that empower employees with AI tools and training will attract the best talent, and professionals who skill up will have the edge.

3. The rise of the AI power user — and what they reveal about the future.

In the research, four types of AI users emerged on a spectrum — from skeptics who rarely use AI to power users who use it extensively. Compared to skeptics, AI power users have reoriented their workdays in fundamental ways, reimagining business processes and saving over 30 minutes per day. Over 90% of power users say AI makes their overwhelming workload more manageable and their work more enjoyable, but they aren’t doing it on their own.

Power users work for a different kind of company. They are 61% more likely to have heard from their CEO on the importance of using generative AI at work, 53% more likely to receive encouragement from leadership to consider how AI can transform their function and 35% more likely to receive tailored AI training for their specific role or function.

“AI is redefining work and it’s clear we need new playbooks,” said Ryan Roslansky, CEO of LinkedIn. “It’s the leaders who build for agility instead of stability and invest in skill building internally that will give their organizations a competitive advantage and create more efficient, engaged and equitable teams.”

The prompt box is the new blank page

We hear one consistent piece of feedback from our customers: talking to AI is harder than it seems. We’ve all learned how to use a search engine, identifying the right few words to get the best results. AI requires more context — just like when you delegate work to a direct report or colleague. But for many, staring down that empty prompt box feels like facing a blank page: Where should I even start?

Today, we’re announcing Copilot for Microsoft 365 innovations to help our customers answer that question.

YouTube Video

• Catch Up, a new chat interface that surfaces personal insights based on your recent activity, provides responsive recommendations , like “You have a meeting with the sales VP on Thursday. Let’s get you prepared — click here to get detailed notes.”

These features will be available in the coming months, and in the future, we’ll take it a step further, with Copilot asking you questions to get to your best work yet.

LinkedIn has also made free over 50 learning courses to empower professionals at all levels to advance their AI aptitude.

Head to WorkLab for the full Work Trend Index Report , and head to LinkedIn to hear more from LinkedIn’s Chief Economist, Karin Kimbrough, on how AI is reshaping the labor market.

And for all the blogs, videos and assets related to today’s announcements, please visit our  microsite .

Tags: AI , LinkedIn , Microsoft Copilot , Work Trend Index

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• Published: 08 May 2024

Accurate structure prediction of biomolecular interactions with AlphaFold 3

• Josh Abramson   ORCID: orcid.org/0009-0000-3496-6952 1   na1 ,
• Jonas Adler   ORCID: orcid.org/0000-0001-9928-3407 1   na1 ,
• Jack Dunger 1   na1 ,
• Richard Evans   ORCID: orcid.org/0000-0003-4675-8469 1   na1 ,
• Tim Green   ORCID: orcid.org/0000-0002-3227-1505 1   na1 ,
• Alexander Pritzel   ORCID: orcid.org/0000-0002-4233-9040 1   na1 ,
• Olaf Ronneberger   ORCID: orcid.org/0000-0002-4266-1515 1   na1 ,
• Lindsay Willmore   ORCID: orcid.org/0000-0003-4314-0778 1   na1 ,
• Andrew J. Ballard   ORCID: orcid.org/0000-0003-4956-5304 1 ,
• Joshua Bambrick   ORCID: orcid.org/0009-0003-3908-0722 2 ,
• Sebastian W. Bodenstein 1 ,
• David A. Evans 1 ,
• Chia-Chun Hung   ORCID: orcid.org/0000-0002-5264-9165 2 ,
• Michael O’Neill 1 ,
• David Reiman   ORCID: orcid.org/0000-0002-1605-7197 1 ,
• Kathryn Tunyasuvunakool   ORCID: orcid.org/0000-0002-8594-1074 1 ,
• Zachary Wu   ORCID: orcid.org/0000-0003-2429-9812 1 ,
• Akvilė Žemgulytė 1 ,
• Eirini Arvaniti 3 ,
• Charles Beattie   ORCID: orcid.org/0000-0003-1840-054X 3 ,
• Ottavia Bertolli   ORCID: orcid.org/0000-0001-8578-3216 3 ,
• Alex Bridgland 3 ,
• Alexey Cherepanov   ORCID: orcid.org/0000-0002-5227-0622 4 ,
• Miles Congreve 4 ,
• Alexander I. Cowen-Rivers 3 ,
• Andrew Cowie   ORCID: orcid.org/0000-0002-4491-1434 3 ,
• Michael Figurnov   ORCID: orcid.org/0000-0003-1386-8741 3 ,
• Fabian B. Fuchs 3 ,
• Hannah Gladman 3 ,
• Rishub Jain 3 ,
• Yousuf A. Khan   ORCID: orcid.org/0000-0003-0201-2796 3 ,
• Caroline M. R. Low 4 ,
• Kuba Perlin 3 ,
• Anna Potapenko 3 ,
• Pascal Savy 4 ,
• Sukhdeep Singh 3 ,
• Adrian Stecula   ORCID: orcid.org/0000-0001-6914-6743 4 ,
• Ashok Thillaisundaram 3 ,
• Catherine Tong   ORCID: orcid.org/0000-0001-7570-4801 4 ,
• Sergei Yakneen   ORCID: orcid.org/0000-0001-7827-9839 4 ,
• Ellen D. Zhong   ORCID: orcid.org/0000-0001-6345-1907 3 ,
• Michal Zielinski 3 ,
• Augustin Žídek   ORCID: orcid.org/0000-0002-0748-9684 3 ,
• Victor Bapst 1   na2 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 1   na2 ,
• Max Jaderberg   ORCID: orcid.org/0000-0002-9033-2695 2   na2 ,
• Demis Hassabis   ORCID: orcid.org/0000-0003-2812-9917 1 , 2   na2 &
• John M. Jumper   ORCID: orcid.org/0000-0001-6169-6580 1   na2  

Nature ( 2024 ) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

• Drug discovery
• Machine learning
• Protein structure predictions
• Structural biology

The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.3 7,8 . Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.

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Author information.

These authors contributed equally: Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore

These authors jointly supervised this work: Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis, John M. Jumper

Authors and Affiliations

Core Contributor, Google DeepMind, London, UK

Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J. Ballard, Sebastian W. Bodenstein, David A. Evans, Michael O’Neill, David Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvilė Žemgulytė, Victor Bapst, Pushmeet Kohli, Demis Hassabis & John M. Jumper

Core Contributor, Isomorphic Labs, London, UK

Joshua Bambrick, Chia-Chun Hung, Max Jaderberg & Demis Hassabis

Google DeepMind, London, UK

Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexander I. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B. Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Kuba Perlin, Anna Potapenko, Sukhdeep Singh, Ashok Thillaisundaram, Ellen D. Zhong, Michal Zielinski & Augustin Žídek

Isomorphic Labs, London, UK

Alexey Cherepanov, Miles Congreve, Caroline M. R. Low, Pascal Savy, Adrian Stecula, Catherine Tong & Sergei Yakneen

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Corresponding authors

Correspondence to Max Jaderberg , Demis Hassabis or John M. Jumper .

Supplementary information

Supplementary information.

This Supplementary Information file contains the following 9 sections: (1) Notation; (2) Data pipeline; (3) Model architecture; (4) Auxiliary heads; (5) Training and inference; (6) Evaluation; (7) Differences to AlphaFold2 and AlphaFold-Multimer; (8) Supplemental Results; and (9) Appendix: CCD Code and PDB ID tables.

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Abramson, J., Adler, J., Dunger, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature (2024). https://doi.org/10.1038/s41586-024-07487-w

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Received : 19 December 2023

Accepted : 29 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1038/s41586-024-07487-w

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