The 13-year-old, Milly Hardwick, said that she, her father and her grandfather had been out in a field with metal detectors for several hours on a Sunday in Royston, England, and had not found a single item. Then, just after a lunch of sandwiches and cookies, they tried a different part of the field, where an organized dig was taking place. After about 20 minutes of searching, Milly said she heard the high-pitched beeping noise — “a lovely-sounding signal” — that a possible find.
Why You Should Consider Mentoring Other Developers
No matter how smart, driven, or talented you are, everyone could use a little help. If you’ve got some experience under your belt and you’re considering mentoring other developers, you may be surprised to learn that it can often be a case of “who saved who?” Meaning: You can get as many benefits as your mentee.
Not sure what that looks like? Read on to learn the benefits of mentoring others, some of the best practices for mentors, and how the developer community fosters a supportive, mutually beneficial culture.
The benefits of developers mentoring other developers
There are so many reasons to mentor someone who’s been in your shoes before. But here are some of the most compelling pay-offs of mentoring a more junior developer:
You strengthen your own skillset
Sometimes the best way to learn something is to teach it. Perhaps you’ve heard of the rubber duck technique for debugging code. It goes like this:
You get a rubber duck, sit it down on your desk, politely request a few minutes of its time, and then start explaining everything from your debugging solution to the symptoms you see to the potential outcomes of the steps you’re thinking about taking. The verbalization process helps you self-review your approach and discover its strengths and weaknesses.
The mentoring process can have a similar outcome. As you teach your mentee, you reinforce your own knowledge of what you do while also gaining new insights into the all-important “why.”
This applies to both hard and soft skills. As you verbalize what you do and why, you get a chance to review your practices. You also undergo a kind of curation process: You end up only sharing stuff that’s worth sharing, segmenting the mental masterpieces from the muck.
You form professional connections that can pay off later
Each person you mentor may, hopefully, be in a position of power one day, and that can pay big dividends if you need a hand later on. This may be tough to picture if your potential mentee is younger, less experienced, and relatively new to development, but in this digitized business climate, change — and advancement — can happen quickly.
It’s possible that someone who’s now a green, wet-behind-the-ears mentee could eventually help you:
- Secure a position at another company, specifically one they work for.
- Put some political power behind an internal initiative you’re trying to champion at your company.
- Learn a new coding language that you don’t have the time to muddle through on your own.
You get the satisfaction of giving back
Of course, some of the best benefits don’t involve any tangible payback at all — it just feels good to give back. Perhaps you got a hand while you were learning to code in a corporate environment or figuring out your professional path.
On the other hand, like many others, maybe you needed help and never got it. Either way, you can be the one to make someone else’s path a little smoother and more comfortable.
Developers mentoring other developers add a human, personal element to a profession that can often be eclipsed by the shadow cast by endless lines of code, algorithms, and detailed syntax. Sitting down for a brew — caffeinated or otherwise — with a mentee can provide you with a sense of balance.
Best practices for developers mentoring other developers
To maximize the benefits of the mentor/mentee relationship — for both parties — you may find the following best practices helpful:
Establish the type of mentor relationship
One of the most important best practices while entering a mentor/mentee relationship is establishing the kind of mentoring you’ll be doing. Here are some of the most common mentorship arrangements:
- Onboarding. This is when you help a new team member understand how the company works, its internal development process, and the hard and soft skills they need to succeed there.
- Formal mentorship. In a formal mentorship, a senior developer mentors a more junior one. They do this through a series of regular, structured meetings between the two. In some cases, a company may already have a formal mentorship program in place.
- Informal mentorship. Informal mentorship happens more organically as people of different levels of experience work together. The mentorship happens informally — during code reviews, planning meetings, whiteboarding, and the like.
Carefully structure your interactions
If you’re entering into a more formal mentorship, there are ways to structure your interactions both when getting started and as the relationship rolls forward. This is best done using concrete objectives, questions, and long- and short-term goals. For example, when you’re just getting started, you can address questions like:
- What elements of your background would be helpful to share?
- What elements of your mentee’s background would be good to share?
- What does the mentee hope to get from you as a mentor?
- What topics will be covered as you move forward?
- What’s the time commitment you’re both willing to give?
Then, as the arrangement takes shape, you’ll want to monitor its structure. Here are some questions that may be helpful to address:
- Is the frequency of your meetings sufficient? Or is it a little too often or not often enough?
- What are some short-term objectives you can tackle together?
- How can you continually evaluate the success of the relationship?
Of course, how rigid you want to be about the structure of the arrangement is 100% up to you. You may even find that just keeping these questions and ideas in mind, without explicitly discussing them with your mentee, will help you put the right boundaries in place and get the most out of the relationship.
The importance of community when working as a developer
Some may be surprised to know that even though it may often be just you and your computer much of the time, community plays a huge role in a developer’s success. The support you get pays off when it comes to both how you work and feel about what you’re doing. For instance, a sense of community amongst developers can:
- Give you allies for solving problems.
- Provide a listening ear when you feel frustration starting to creep in.
- Gather support for large-scale initiatives, making them happen quicker.
- Result in new technologies — easier ways to accomplish complex tasks.
Also, the developer community has a healthy sense of competition — not in a cutthroat sense, but in a way that ensures the bar is raised high and kept there. As part of the community, you’ll want to continue challenging yourself to keep up with — and impress — your peers.
Finding mentoring opportunities
Ready to share your knowledge with other developers? You can find tons of informal mentoring opportunities right here on Codecademy. Learners of all experience levels flock to our forums to share their projects and professional insights, helping each other solve problems and find new opportunities for growth.
Or, if you’re looking for a more formal, structured mentoring opportunity, why not check out your local Chapter? You’ll find Codecademy Chapters around the world, and they’re a great place to meet and connect with other developers. They’re also rife with collaborative opportunities, and you might even get a chance to guide new developers in a group project, like the React app built by members of our Detroit chapter in the video below:
Little Red House
Use your imagination to write the opening of a short story or poem inspired by this illustration — or, tell us about a memory from your own life that this image makes you think of.
Post it in the comments, then read the related article to learn more.
Want more Picture Prompts? Find them all in this column.
Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
Word of the Day: incognito
incognito ˌin-ˌkäg-ˈnē-(ˌ)tō adverb and adjective
adverb: without revealing one’s identity
adjective: with one’s identity concealed
_________
The word incognito has appeared in 32 articles on NYTimes.com in the past year, including on Sept. 3 in “Bianca Andreescu Returns to the U.S. Open With Winning on Her Mind” by Christopher Clarey:
So much has changed since her first visit, but Bianca Andreescu is still undefeated at the U.S. Open, where she swept to the 2019 title in precociously grand style, defeating Serena Williams in straight sets in the final at age 19.
After playing no official matches in 2020, she is back in the third round this year, but is also looking back.
… It felt so familiar, and yet so strange. Andreescu walked into the interview room as if she were intent on remaining incognito, mask on, hoodie up, eyes barely visible…
Daily Word Challenge
Can you correctly use the word incognito in a sentence?
Based on the definition and example provided, write a sentence using today’s Word of the Day and share it as a comment on this article. It is most important that your sentence makes sense and demonstrates that you understand the word’s definition, but we also encourage you to be creative and have fun.
Then, read some of the other sentences students have submitted and use the “Recommend” button to vote for two original sentences that stand out to you.
If you want a better idea of how incognito can be used in a sentence, read these usage examples on Vocabulary.com.
If you enjoy this daily challenge, try one of our monthly vocabulary challenges.
Students ages 13 and older in the United States and the United Kingdom, and 16 and older elsewhere, can comment. All comments are moderated by the Learning Network staff.
Tips on Chasing the Truth
BE SPECIFIC AND PRECISE Your investigation should include names, dates, legal and financial information, on- the- record interviews, and documents. Without those ingredients an investigation will lack bite, as Judd’s 2015 interview with Variety proved, and can even do harm, as was the case with the factually flawed Rolling Stone story about sexual assault at the University of Virginia. Impact in journalism comes from specificity— proof, patterns, and stories that come to life. In investigative journalism, knowing about incriminating documents is good; seeing them is excellent; and having copies is best.
FIND SOURCES Start by researching your topic. To look for sources with firsthand experience, you can search through public records like Nexis and social media sites like LinkedIn. But it’s also important to network, asking for introductions from experts and intermediaries. If your investigation is particularly sensitive, you may have to find back channels to contact the most relevant sources. In our case, we took all kinds of circuitous routes to reach actresses directly
is reliable and sound, advocates and policymakers may grab the ball and push for specific solutions to the problems you have uncovered. Investigative journalists follow facts, not an activist’s agenda.
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What’s Going On in This Graph? | Jan. 12, 2022
4. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button to address that student directly.
On Wednesday, Jan. 12, teachers from our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern time.
5. By Friday morning, Jan. 14, we will reveal more information about the graph, including a free link to the article that includes this graph, at the bottom of this post. We encourage you to post additional comments based on the article, possibly using statistical terms defined in the Stat Nuggets.
Reveal
We’ll post more information here on Thursday afternoon. Stay tuned!
More?
• See all graphs in this series or collections of 60 of our favorite graphs, 28 graphs that teach about inequality and 24 graphs about climate change.
• View our archives that link to all past releases, organized by topic, graph type and Stat Nugget.
• Learn more about the notice and wonder teaching strategy from this 5-minute video and how and why other teachers are using this strategy from our on-demand webinar.
• Sign up for our free weekly Learning Network newsletter so you never miss a graph. Graphs are always released by the Friday before the Wednesday live-moderation to give teachers time to plan ahead.
• Go to the American Statistical Association K-12 website, which includes teacher statistics resources, Census in the Schools student-generated data, professional development opportunities, and more.
Students 13 and older in the United States and the Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
Film Club: ‘Grieving Our Old Normal’
“Grieving Our Old Normal” is a four-minute film that touches on themes of loss, acceptance and our all-too-human struggle to make sense of life in a pandemic and all that we can never get back. In this Opinion video, Lindsay Crouse, one of the filmmakers, asks us to reckon with a new, cold reality: After all of the deaths, the canceled weddings, the lonely birthday celebrations, the boredom and the terror, “There is no going back to normal. Your old life is gone. But whether you wanted to or not, you were building a new one. You still are. What do you want your new life to be?”
How do you make sense of this moment, two years into the pandemic? Do you agree with Ms. Crouse’s advice? How do we mourn everything we’ve lost? How do we begin to accept — and perhaps, one day, attempt to move on?
Students
1. Watch the short film above. While you watch, you might take notes using our Film Club Double-Entry Journal (PDF) to help you remember specific moments.
2. After watching, think about these questions:
What questions do you still have?
What connections can you make between this film and your own life or experience? Why? Does this film remind you of anything else you’ve read or seen? If so, how and why?
3. An additional challenge | Respond to the essential question at the top of this post: How do we mourn everything we’ve lost to Covid?
4. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment, although teachers of younger students are welcome to post what their students have to say.)
5. After you have posted, try reading back to see what others have said, then respond to someone else by posting another comment. Use the “Reply” button or the @ symbol to address that student directly.
6. To learn more, read “Grieving Our Old Normal.” Lindsay Crouse, Kirby Ferguson and Emily Holzknecht, the filmmakers, write:
That’s right: We’re all two years older than when we first shut ourselves inside for lockdown. If we assumed the Covid pandemic would be brief, we were wrong. So if you feel like a withered old sloth these days wallowing around and yearning for your old life, that’s understandable. But there’s no use. It’s gone.
The stunning number of lives lost to Covid is its own appalling tragedy. But the video above is about a different kind of grief many of us are experiencing right now: the kind that comes with the gnawing realization that we really need to grieve the parts of our lives that have disappeared, even as they continue to slip away. As another year ends and Covid surges again, it’s clear nothing will change soon.
Forget resilience. Forget silver linings. Right now, it’s time to decide: How do we mourn everything we’ve lost? Once we do that, hopefully at some point, we can attempt something even harder: moving on.
Want more student-friendly videos? Visit our Film Club column.
Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
What Does an A.I. Engineer Do?
A.I. Engineers are in demand in most industries, and there’s a good reason for this. If you’re wondering what an A.I. Engineer does, we’ll break it down for you.
Businesses can use the massive amounts of data they generate daily to improve and simplify common, everyday tasks. With the right A.I. systems, companies can take these tasks off the hands of their teams so they can focus on more important work. Technologies like speech recognition, business process management, and image processing are only some of the A.I. technologies changing the world.
Companies need A.I. Engineers to put these systems in place, maintain them, and adapt them to changes in the business. In this article, we’ll explore what A.I. Engineers do, what kind of skills they need, and how you can get started on the A.I. engineering career path.
But first, let’s examine what A.I. engineering is and how it relates to machine learning.
What is artificial intelligence?
A.I., or artificial intelligence, uses computers and machines to emulate how the human mind operates to accomplish problem-solving and decision-making tasks. It combines the robust data sets we generate daily with computer science to achieve this goal in its simplest form.
In A.I., machines learn the outcomes of specific actions by crunching mountains of past data. They then use the insights gained from this process to make decisions about future actions and solve problems. At the same time, data is collected on the machine’s decisions and is used to correct and perfect future actions and decisions.
What’s the difference between A.I. and machine learning?
Machine learning and artificial intelligence are often lumped together in the same definition, but they aren’t necessarily the same. In our forums, one of our learners, J, provides a helpful explanation:
“Artificial intelligence can be described as when machines carry out tasks in an intelligent or smart way, based on set rules to solve certain problems. Artificial intelligence, or A.I., makes decisions, learns, and solves problems similar to how humans would.
Machine learning, on the other hand, is a subset of artificial intelligence. It’s when we give machines data and have them learn from that data on their own, without being explicitly programmed. Machine learning models learn from the data and try to make improvements to its predictions over time.”
So machine learning is a subset of the A.I. field, but not all A.I. is machine learning. A.I. is a broader field. Check out our article on what a Machine Learning Engineer does to learn more.
What does an A.I. Engineer do?
A.I. Engineers develop new applications and systems to:
- Enhance the performance and efficiency of business processes
- Help the business make better decisions
- Lower costs
- Increase revenue and profits
Simply put, they use software engineering and data science to streamline a business with automation.
Many of an A.I. Engineer’s tasks overlap with those of a Machine Learning Engineer. Some of the responsibilities of an A.I. Engineer include:
- Coordinating with business leaders and software development teams to determine what business processes can be improved by using A.I.
- Creating and maintaining the A.I. development process and the infrastructure that it runs on.
- Applying machine learning techniques for image recognition.
- Applying natural language processing techniques to text and voice transcripts to pull insights and analytics from this data.
- Building and maintaining chatbots that interact with customers.
- Developing AI-driven solutions that mimic human behavior to accomplish repetitive tasks currently done by people.
- Building, training, and perfecting machine learning models.
- Simplifying the machine learning process so that other business applications can interact with them using APIs.
- Building recommendation engines for shopping sites, streaming services, and other applications.
- Developing data pipelines that streamline the process of transforming raw data into the structured data necessary for A.I. processes.
Required skills for an A.I. Engineer
A.I. is a broad field, and an A.I. Engineer requires both the skills of a Software Engineer and those of a Data Scientist. It may even help to know mathematics and statistics.
An A.I. Engineer definitely needs to know at least one programming language and will usually end up learning multiple during their career. Many of the tools that A.I. Engineers use to make their job easier will require knowledge of Python, R, or Java.
To build and work with machine learning models, an A.I. Engineer will also need to know the fundamentals of various machine learning frameworks, like TensorFlow, Theano, PyTorch, and Caffe. They’ll also need to know how to turn raw data into the features that machine learning models use.
Additionally, an A.I. Engineer must have experience with a variety of machine learning model types and what type of jobs they work best for. These types include:
- Neural networks
- Recurrent neural networks
- K-nearest neighbors algorithms
- General adversarial networks
- Supervised learning
- Unsupervised learning
- Random forests
- Reinforcement learning
To actually create new models and understand how they work, an A.I. expert may have to know linear algebra, probability, and statistics instead of using pre-built models. These topics help you understand hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis — the techniques used in machine learning.
Data is also a vital part of an A.I. Engineer’s job. A lot of that data is stored in relational database management systems, so having a basic knowledge of SQL, the language of databases, comes in handy. Still, some of this data will be stored in unstructured or semi-structured data stores — so knowing big data technologies like Apache Spark, Apache Hadoop, Cassandra, and MongoDB is a big plus.
A.I. Engineers require more than technical skills, though. They must also:
- Be meticulous and detail-oriented because small inconsistencies in data can cause big discrepancies in machine learning models.
- Have excellent communication skills because many of the people they work with won’t understand much of what they do. They’ll have to explain the results of their tasks in a way that anyone can understand.
- Be good at big-picture thinking so they can understand business needs and build A.I. systems that benefit the company.
A.I. Engineer salary
A.I. Engineers make good money. The average salary for an A.I. Engineer in the U.S. is over $160,000. In states like California, the average reaches close to $200,000.
The demand for A.I. Engineers has always been high, so expect job openings and pay to increase in the future. The U.S. Bureau of Labor Statistics expects all Software Developer jobs to increase by 22% over the next decade, and this includes A.I. Engineers.
How to become an A.I. Engineer
Gone are the days when a computer science degree or even any college degree would be required to become an A.I. Engineer. Good Artificial Intelligence Engineers are just in too much demand to require a degree, and employers have learned that many skilled A.I. experts don’t even need one. They do it because they love the work.
If A.I. is the career path for you, and you don’t have a degree or want to spend four years learning artificial intelligence, you don’t have to. There are plenty of educational opportunities to learn A.I. online whenever you have the time and wherever you are in the world. Plus, most of the tools you need for the learning process are open-source and freely available online.
If you’re new to artificial intelligence and looking for the best place to start your journey, why not try Codecademy? Since knowing at least one programming language is a prerequisite for becoming an A.I. Engineer, a great place to start is our Learn Python 3 course.
Python is one of the top languages used by Data Scientists and A.I. Engineers. It’s also a requirement of our Learn the Basics of Machine Learning course, which will introduce you to the field. You can also check out our Data Scientist Career Path that covers many of the skills you’ll need as an A.I. Engineer.
While taking these courses, make sure to also learn and work on custom A.I. projects on your own time and add both your course projects and side projects to your portfolio. Also, keep your LinkedIn profile updated with your new learning achievements and projects to make it stand out for recruiters and companies looking for A.I. Engineers. It also pays to practice interviewing skills to be ready when you get a call from a recruiter.
Keep learning today
Never stop learning. A.I. is a broad field, and learning Python and machine learning fundamentals is a great start, but each skill you add to your resume can increase your value to a company. Building Chatbots with Python will teach you how to build software that can carry on conversations like a human. Learn to Program Alexa will teach you how to write software for Amazon’s bot.
For even more courses to build your A.I. skills, check out our machine learning course catalog and revisit the skills section of this article. Good luck with your A.I. career path!
Machine Learning Courses & Tutorials | Codecademy
Machine Learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.
Lesson of the Day: ‘Chasing the Truth: A Young Journalist’s Guide to Investigative Reporting’
8. Now that you have read the first chapter of “Chasing the Truth,” revisit your response to the warm-up. What did you learn about investigative journalism that you didn’t know? Why is it important? Would you want to be an investigative journalist? Why, or why not?
9. Make a prediction: After finishing the first chapter, how do you think Jodi and Megan will continue their investigation? What are some major steps they will need to take?
10. Finally, based on what you just read, what questions do you have for the authors? (We hope to feature video versions of some of them on our Jan. 27 panel, so if you’d like to ask yours that way, follow Step 2, below.)
Going Further | Plan an Investigative Project
Now that you’ve read Chapter 1, you probably realize that an investigative journalist is likely to spend considerably more time reporting a story than writing it up. Asking the right questions, finding sources who are willing to speak on the record, identifying clear evidence, and checking and double-checking all the facts takes time.
Using your own curiosity, some of the techniques you read about in “Chasing the Truth” and the advice from Megan and Jodi you’ll hear in our “Live Panel for Students: How Investigative Journalism Works,” think about what an investigative journalism project of your own could look like.
Before the Panel
Step 1: Brainstorm your topic.
On your own or with a classmate or small group, brainstorm a list of possible issues, large or small, you’d like to investigate. Keep in mind that you’re likely to have more success investigating topics in your school or community than chasing a national or international story. Why are you interested in these subjects? Why are they relevant or important?
Our related Student Opinion question, “What Do You Want to Investigate?,” can help you get started, and offers this advice:
Think about the problems or injustices in your community or school. Consider questions you or others have about how local systems work — or don’t. Think about big investigative pieces you have read in national news outlets, and “localize” them: How does that same issue look in your area? (To help you brainstorm, you might scroll through this list of investigative pieces that have won Pulitzer Prizes or through this list of “21 Excellent Stories of Student Journalism Against the Odds.”)
When you have some ideas, we encourage you to post them and read what other students from around the world have to say in the comments section of our forum. Or, proceed to Step 2 to make a video.
What Do You Want to Investigate?
Use Ms. Kantor and Ms. Twohey’s groundbreaking 2017 article as your guide. You can read it either as it was originally published in The Times or via this PDF from their book, in which the authors annotate their work throughout to show you their reporting process.
Two decades ago, the Hollywood producer Harvey Weinstein invited Ashley Judd to the Peninsula Beverly Hills hotel for what the young actress expected to be a business breakfast meeting. Instead, he had her sent up to his room, where he appeared in a bathrobe and asked if he could give her a massage or she could watch him shower, she recalled in an interview.
“How do I get out of the room as fast as possible without alienating Harvey Weinstein?” Ms. Judd said she remembers thinking.
In 2014, Mr. Weinstein invited Emily Nestor, who had worked just one day as a temporary employee, to the same hotel and made another offer: If she accepted his sexual advances, he would boost her career, according to accounts she provided to colleagues who sent them to Weinstein Company executives. The following year, once again at the Peninsula, a female assistant said Mr. Weinstein badgered her into giving him a massage while he was naked, leaving her “crying and very distraught,” wrote a colleague, Lauren O’Connor, in a searing memo asserting sexual harassment and other misconduct by their boss.
“There is a toxic environment for women at this company,” Ms. O’Connor said in the letter, addressed to several executives at the company run by Mr. Weinstein.
An investigation by The New York Times found previously undisclosed allegations against Mr. Weinstein stretching over nearly three decades, documented through interviews with current and former employees and film industry workers, as well as legal records, emails and internal documents from the businesses he has run, Miramax and the Weinstein Company.
During that time, after being confronted with allegations including sexual harassment and unwanted physical contact, Mr. Weinstein has reached at least eight settlements with women, according to two company officials speaking on the condition of anonymity. Among the recipients, The Times found, were a young assistant in New York in 1990, an actress in 1997, an assistant in London in 1998, an Italian model in 2015 and Ms. O’Connor shortly after, according to records and those familiar with the agreements.
