The Traffic Light Approach to Reviewing Personal, Team and Project Development

What needs improvement? If you want to improve, you need to practice your sport, musical instrument, habit, or work project. You need to assess your progress.

Continuous improvement is the foundation of focused practice and a growth mentality. Not just individually. High-performing teams pursue improvement. Right? Why is it hard?

As a leadership coach, senior manager, and high-level athlete, I've found three key questions that may unlock high performance in individuals and teams.

Problems with Reviews

Reviewing and improving performance is crucial, however I hate seeing review sessions in my diary. I rarely respond to questionnaire pop-ups or emails. Why?

Time constrains. Requests to fill out questionnaires often state they will take 10–15 minutes, but I can think of a million other things to do with that time. Next, review overload. Businesses can easily request comments online. No matter what you buy, someone will ask for your opinion. This bombardment might make feedback seem bad, which is bad.

The problem is that we might feel that way about important things like personal growth and work performance. Managers and team leaders face a greater challenge.

When to Conduct a Review

We must be wise about reviewing things that matter to us. Timing and duration matter. Reviewing the experience as quickly as possible preserves information and sentiments. Time must be brief. The review's importance and size will determine its length. We might only take a few seconds to review our morning coffee, but we might require more time for that six-month work project.

These post-event reviews should be supplemented by periodic reflection. Journaling can help with daily reflections, but I also like to undertake personal reviews every six months on vacation or at a retreat.

As an employee or line manager, you don't want to wait a year for a performance assessment. Little and frequently is best, with a more formal and in-depth assessment (typically with a written report) in 6 and 12 months.

The Easiest Method to Conduct a Review Session

I follow Einstein's review process:

“Make things as simple as possible but no simpler.”

Thus, it should be brief but deliver the necessary feedback. Quality critique is hard to receive if the process is overly complicated or long.

I have led or participated in many review processes, from strategic overhauls of big organizations to personal goal coaching. Three key questions guide the process at either end:

• What ought to stop being done?

• What should we do going forward?

• What should we do first?

Following the Rule of 3, I compare it to traffic lights. Red, amber, and green lights:

• Red What ought should we stop?

• Amber What ought to we keep up?

• Green Where should we begin?

This approach is easy to understand and self-explanatory, however below are some examples under each area.

Red What ought should we stop?

As a team or individually, we must stop doing things to improve.

Sometimes they're bad. If we want to lose weight, we should avoid sweets. If a team culture is bad, we may need to stop unpleasant behavior like gossiping instead of having difficult conversations.

Not all things we should stop are wrong. Time matters. Since it is finite, we sometimes have to stop nice things to focus on the most important. Good to Great author Jim Collins famously said:

“Don’t let the good be the enemy of the great.”

Prioritizing requires this idea. Thus, decide what to stop to prioritize.

Amber What ought to we keep up?

Should we continue with the amber light? It helps us decide what to keep doing during review. Many items fall into this category, so focus on those that make the most progress.

Which activities have the most impact? Which behaviors create the best culture? Success-building habits?

Use these questions to find positive momentum. These are the fly-wheel motions, according to Jim Collins. The Compound Effect author Darren Hardy says:

“Consistency is the key to achieving and maintaining momentum.”

What can you do consistently to reach your goal?

Green Where should we begin?

Finally, green lights indicate new beginnings. Red/amber difficulties may be involved. Stopping a red issue may give you more time to do something helpful (in the amber).

This green space inspires creativity. Kolbs learning cycle requires active exploration to progress. Thus, it's crucial to think of new approaches, try them out, and fail if required.

This notion underpins lean start-build, up's measure, learn approach and agile's trying, testing, and reviewing. Try new things until you find what works. Thomas Edison, the lighting legend, exclaimed:

“There is a way to do it better — find it!”

Failure is acceptable, but if you want to fail forward, look back on what you've done.

John Maxwell concurred with Edison:

“Fail early, fail often, but always fail forward”

A good review procedure lets us accomplish that. To avoid failure, we must act, experiment, and reflect.

Use the traffic light system to prioritize queries. Ask:

• Red What needs to stop?

• Amber What should continue to occur?

• Green What might be initiated?

Take a moment to reflect on your day. Check your priorities with these three questions. Even if merely to confirm your direction, it's a terrific exercise!

I've summarized each.

Anne Lamott said books are important. Books help us understand ourselves and our behavior. They teach us about community, friendship, and death.

I read. One of my few life-changing habits. 100+ books a year improve my life. I'll list life-changing books you can read in a day. I hope you like them too.

Let's get started!

1) Seneca's Letters from a Stoic

One of my favorite philosophy books. Ryan Holiday, Naval Ravikant, and other prolific readers recommend it.

Seneca wrote 124 letters at the end of his life after working for Nero. Death, friendship, and virtue are discussed.

It's worth rereading. When I'm in trouble, I consult Seneca.

It's brief. The book could be read in one day. However, use it for guidance during difficult times.

My favorite book quotes:

• Many men find that becoming wealthy only alters their problems rather than solving them.

• You will never be poor if you live in harmony with nature; you will never be wealthy if you live according to what other people think.

• We suffer more frequently in our imagination than in reality; there are more things that are likely to frighten us than to crush us.

2) Steven Pressfield's book The War of Art

I’ve read this book twice. I'll likely reread it before 2022 is over.

The War Of Art is the best productivity book. Steven offers procrastination-fighting tips.

Writers, musicians, and creative types will love The War of Art. Workplace procrastinators should also read this book.

My favorite book quotes:

• The act of creation is what matters most in art. Other than sitting down and making an effort every day, nothing else matters.

• Working creatively is not a selfish endeavor or an attempt by the actor to gain attention. It serves as a gift for all living things in the world. Don't steal your contribution from us. Give us everything you have.

• Fear is healthy. Fear is a signal, just like self-doubt. Fear instructs us on what to do. The more terrified we are of a task or calling, the more certain we can be that we must complete it.

3) Darren Hardy's The Compound Effect

The Compound Effect offers practical tips to boost productivity by 10x.

The author believes each choice shapes your future. Pizza may seem harmless. However, daily use increases heart disease risk.

Positive outcomes too. Daily gym visits improve fitness. Reading an hour each night can help you learn. Writing 1,000 words per day would allow you to write a novel in under a year.

Your daily choices affect compound interest and your future. Thus, better habits can improve your life.

My favorite book quotes:

• Until you alter a daily habit, you cannot change your life. The key to your success can be found in the actions you take each day.

• The hundreds, thousands, or millions of little things are what distinguish the ordinary from the extraordinary; it is not the big things that add up in the end.

• Don't worry about willpower. Time to use why-power. Only when you relate your decisions to your aspirations and dreams will they have any real meaning. The decisions that are in line with what you define as your purpose, your core self, and your highest values are the wisest and most inspiring ones. To avoid giving up too easily, you must want something and understand why you want it.

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.

Weekend project: limitless design service. It was fun to see whether I could start a business quickly.

I use no-code apps to save time and resources.

TL;DR I started a business utilizing EditorX for my website, Notion for client project management, and a few favors to finish my portfolio.

First step: research (Day 1)

I got this concept from a Kimp Instagram ad. The Minimalist Hustler Daily newsletter mentioned a similar and cheaper service (Graphically).

I Googled other unlimited design companies. Many provide different costs and services. Some supplied solely graphic design, web development, or copywriting.

Step 2: Brainstorming (Day 1)

I did something simple.

• What benefits and services to provide

• Price to charge

Since it's a one-person performance (for now), I'm focusing on graphic design. I can charge less.

So I don't overwhelm myself and can accommodate budget-conscious clientele.

Step 3: Construction (Day 1 & 2)

This project includes a management tool, a website, and a team procedure.

I built a project management tool and flow first. Once I had the flow and a Notion board, I tested it with design volunteers. They fake-designed while I built the website.

Tool for Project Management

I modified a Notion template. My goal is to keep clients and designers happy.

Team Approach

My sister, my partner, and I kept this business lean. I tweaked the Notion board to make the process smooth. By the end of Sunday, I’d say it’s perfect!

Website

I created the website after they finished the fake design demands. EditorX's drag-and-drop builder attracted me. I didn't need to learn code, and there are templates.

I used a template wireframe.

This project's hardest aspect is developing the site. It's my first time using EditorX and I'm no developer.

People answer all your inquiries in a large community forum.

As a first-time user developing a site in two days, I think I performed OK. Here's the site for feedback.

4th step: testing (Day 2)

Testing is frustrating because it works or doesn't. My testing day was split in two.

• testing the workflow from payment to onboarding to the website

• the demand being tested

It's working so far. If someone gets the trial, they can request design work.

I've gotten a couple of inquiries about demand. I’ll be working with them as a start.

Completion

Finally! I built my side project in one weekend. It's too early to tell if this is successful. I liked that I didn't squander months of resources testing out an idea.

Many of us have struggled for years to master a second language (in my case, English). Because (at least in my situation) we've always used an input-based system or method.

I'll explain in detail, but briefly: We can understand some conversations or sentences (since we've trained), but we can't give sophisticated answers or speak fluently (because we have NOT trained at all).

What exactly is input-based learning?

Reading, listening, writing, and speaking are key language abilities (if you look closely at that list, it seems that people tend to order them in this way: inadvertently giving more priority to the first ones than to the last ones).

These talents fall under two learning styles:

• Reading and listening are input-based activities (sometimes referred to as receptive skills or passive learning).

• Writing and speaking are output-based tasks (also known as the productive skills and/or active learning).

What's the best learning style? To learn a language, we must master four interconnected skills. The difficulty is how much time and effort we give each.

According to Shion Kabasawa's books The Power of Input: How to Maximize Learning and The Power of Output: How to Change Learning to Outcome (available only in Japanese), we spend 7:3 more time on Input Based skills than Output Based skills when we should be doing the opposite, leaning more towards Output (Input: Output->3:7).

I can't tell you how he got those numbers, but I think he's not far off because, for example, think of how many people say they're learning a second language and are satisfied bragging about it by only watching TV, series, or movies in VO (and/or reading a book or whatever) their Input is: 7:0 output!

You can't be good at a sport by watching TikTok videos about it; you must play.

“being pushed to produce language puts learners in a better position to notice the ‘gaps’ in their language knowledge”, encouraging them to ‘upgrade’ their existing interlanguage system. And, as they are pushed to produce language in real time and thereby forced to automate low-level operations by incorporating them into higher-level routines, it may also contribute to the development of fluency. — Scott Thornbury (P is for Push)

How may I practice output-based learning more?

I know that listening or reading is easy and convenient because we can do it on our own in a wide range of situations, even during another activity (although, as you know, it's not ideal), writing can be tedious/boring (it's funny that we almost always excuse ourselves in the lack of ideas), and speaking requires an interlocutor. But we must leave our comfort zone and modify our thinking to go from 3:7 to 7:3. (or at least balance it better to something closer). Gradually.

“You don’t have to do a lot every day, but you have to do something. Something. Every day.” — Callie Oettinger (Do this every day)

We can practice speaking like boxers shadow box.

Speaking out loud strengthens the mind-mouth link (otherwise, you will still speak fluently in your mind but you will choke when speaking out loud). This doesn't mean we should talk to ourselves on the way to work, while strolling, or on public transportation. We should try to do it without disturbing others, such as explaining what we've heard, read, or seen (the list is endless: you can TALK about what happened yesterday, your bedtime book, stories you heard at the office, that new kitten video you saw on Instagram, an experience you had, some new fact, that new boring episode you watched on Netflix, what you ate, what you're going to do next, your upcoming vacation, what’s trending, the news of the day)

Who will correct my grammar, vocabulary, or pronunciation with an imagined friend? We can't have everything, but tools and services can help [1].

Lack of bravery

Fear of speaking a language different than one's mother tongue in front of native speakers is global. It's easier said than done, because strangers, not your friends, will always make fun of your accent or faults. Accept it and try again. Karma will prevail.

Perfectionism is a trap. Stop self-sabotaging. Communication is key (and for that you have to practice the Output too ).

“Don’t forget to have fun and enjoy the process.” — Ruri Ohama

[1] Grammarly, Deepl, Google Translate, etc.