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.

Don't say this in public!

Want to wow your audience? Want to deliver a successful presentation? Do you want practical takeaways from your presentation?

Then avoid these phrases.

Public speaking is difficult. People fear public speaking, according to research.

"Public speaking is people's biggest fear, according to studies. Number two is death. "Sounds right?" — Comedian Jerry Seinfeld

Yes, public speaking is scary. These words and phrases will make your presentation harder.

Using unnecessary words can weaken your message.

You may have prepared well for your presentation and feel confident. During your presentation, you may freeze up. You may blank or forget.

Effective delivery is even more important than skillful public speaking.

Here are 10 presentation pitfalls.

1. I or Me

Presentations are about the audience, not you. Replace "I or me" with "you, we, or us." Focus on your audience. Reward them with expertise and intriguing views about your issue.

Serve your audience actionable items during your presentation, and you'll do well. Your audience will have a harder time listening and engaging if you're self-centered.

2. Sorry if/for

Your presentation is fine. These phrases make you sound insecure and unprepared. Don't pressure the audience to tell you not to apologize. Your audience should focus on your presentation and essential messages.

3. Excuse the Eye Chart, or This slide's busy

Why add this slide if you're utilizing these phrases? If you don't like this slide, change it before presenting. After the presentation, extra data can be provided.

Don't apologize for unclear slides. Hide or delete a broken PowerPoint slide. If so, divide your message into multiple slides or remove the "business" slide.

4. Sorry I'm Nervous

Some think expressing yourself will win over the audience. Nerves are horrible. Even public speakers are nervous.

Nerves aren't noticeable. What's the point? Let the audience judge your nervousness. Please don't make this obvious.

5. I'm not a speaker or I've never done this before.

These phrases destroy credibility. People won't listen and will check their phones or computers.

Why present if you use these phrases?

Good speakers aren't necessarily public speakers. Be confident in what you say. When you're confident, many people will like your presentation.

6. Our Key Differentiators Are

Overused term. It's widely utilized. This seems "salesy," and your "important differentiators" are probably like a competitor's.

This statement has been diluted; say, "what makes us different is..."

7. Next Slide

Many slides or stories? Your presentation needs transitions. They help your viewers understand your argument.

You didn't transition well when you said "next slide." Think about organic transitions.

8. I Didn’t Have Enough Time, or I’m Running Out of Time

The phrase "I didn't have enough time" implies that you didn't care about your presentation. This shows the viewers you rushed and didn't care.

Saying "I'm out of time" shows poor time management. It means you didn't rehearse enough and plan your time well.

9. I've been asked to speak on

This phrase is used to emphasize your importance. This phrase conveys conceit.

When you say this sentence, you tell others you're intelligent, skilled, and appealing. Don't utilize this term; focus on your topic.

10. Moving On, or All I Have

These phrases don't consider your transitions or presentation's end. People recall a presentation's beginning and end.

How you end your discussion affects how people remember it. You must end your presentation strongly and use natural transitions.

Conclusion

10 phrases to avoid in a presentation. I or me, sorry if or sorry for, pardon the Eye Chart or this busy slide, forgive me if I appear worried, or I'm really nervous, and I'm not good at public speaking, I'm not a speaker, or I've never done this before.

Please don't use these phrases: next slide, I didn't have enough time, I've been asked to speak about, or that's all I have.

We shouldn't make public speaking more difficult than it is. We shouldn't exacerbate a difficult issue. Better public speakers avoid these words and phrases.

“Remember not only to say the right thing in the right place, but far more difficult still, to leave unsaid the wrong thing at the tempting moment.” — Benjamin Franklin, Founding Father

This is a summary. See the original post here.

These four, when combined, destroy procrastination.

You're not rushed. You waste it on busywork.

You'll accept this eventually.

• In 2022, the daily average usage of a user on social media is 2.5 hours.

• By 2020, 6 billion hours of video were watched each month by Netflix's customers, who used the service an average of 3.2 hours per day.

When we see these numbers, we think "Wow!" People squander so much time as though they don't contribute. True. These are yours. Likewise.

We don't lack time; we just waste it. Once you realize this, you can change your habits to save time. This article explains. If you adopt ALL 4 of these simple behaviors, you'll see amazing benefits.

Time-blocking

Cal Newport's time-blocking trick takes a minute but improves your day's clarity.

Divide the next day into 30-minute (or 5-minute, if you're Elon Musk) segments and assign responsibilities. As seen.

Here's why:

• The procrastination that results from attempting to determine when to begin working is eliminated. Procrastination is a given if you choose when to begin working in real-time. Even if you may assume you'll start working in five minutes, it won't take you long to realize that five minutes have turned into an hour. But if you've already determined to start working at 2:00 the next day, your odds of procrastinating are greatly decreased, if not eliminated altogether.

• You'll also see that you have a lot of time in a day when you plan your day out on paper and assign chores to each hour. Doing this daily will permanently eliminate the lack of time mindset.

5-4-3-2-1: Have breakfast with the frog!

“If it’s your job to eat a frog, it’s best to do it first thing in the morning. And If it’s your job to eat two frogs, it’s best to eat the biggest one first.”

Eating the frog means accomplishing the day's most difficult chore. It's better to schedule it first thing in the morning when time-blocking the night before. Why?

• The day's most difficult task is also the one that causes the most postponement. Because of the stress it causes, the later you schedule it, the more time you risk wasting by procrastinating.

• However, if you do it right away in the morning, you'll feel good all day. This is the reason it was set for the morning.

Mel Robbins' 5-second rule can help. Start counting backward 54321 and force yourself to start at 1. If you acquire the urge to work on a goal, you must act within 5 seconds or your brain will destroy it. If you're scheduled to eat your frog at 9, eat it at 8:59. Start working.

Micro-visualisation

You've heard of visualizing to enhance the future. Visualizing a bright future won't do much if you're not prepared to focus on the now and develop the necessary habits. Alexander said:

People don’t decide their futures. They decide their habits and their habits decide their future.

I visualize the next day's schedule every morning. My day looks like this

“I’ll start writing an article at 7:30 AM. Then, I’ll get dressed up and reach the medicine outpatient department by 9:30 AM. After my duty is over, I’ll have lunch at 2 PM, followed by a nap at 3 PM. Then, I’ll go to the gym at 4…”

etc.

This reinforces the day you planned the night before. This makes following your plan easy.

Set the timer.

It's the best iPhone productivity app. A timer is incredible for increasing productivity.

Set a timer for an hour or 40 minutes before starting work. Your call. I don't believe in techniques like the Pomodoro because I can focus for varied amounts of time depending on the time of day, how fatigued I am, and how cognitively demanding the activity is.

I work with a timer. A timer keeps you focused and prevents distractions. Your mind stays concentrated because of the timer. Timers generate accountability.

To pee, I'll pause my timer. When I sit down, I'll continue. Same goes for bottle refills. To use Twitter, I must pause the timer. This creates accountability and focuses work.

Connecting everything

If you do all 4, you won't be disappointed. Here's how:

• Plan out your day's schedule the night before.

• Next, envision in your mind's eye the same timetable in the morning.

• Speak aloud 54321 when it's time to work: Eat the frog! In the morning, devour the largest frog.

• Then set a timer to ensure that you remain focused on the task at hand.

We love round numbers, don't we? $100,000 is a frequent freelancing milestone. You feel like six figures means you're doing something properly.

You've most likely already conquered initial freelancing challenges like finding clients, setting fair pricing, coping with criticism, getting through dry spells, managing funds, etc.

You think I must be doing well. Last month, my freelance income topped $100,000.

That may not sound impressive considering I've been freelancing for 2.75 years, but I made 30% of that in the previous four months, which is crazy.

Here are the things I wish I'd known during the early days of self-employment that would have helped me hit $100,000 faster.

1. The Volatility of Freelancing Will Stabilize.

Freelancing is risky. No surprise.

Here's an example.

October 2020 was my best month, earning $7,150. Between $4,004 in September and $1,730 in November. Unsteady.

Freelancing is regrettably like that. Moving clients. Content requirements change. Allocating so much time to personal pursuits wasn't smart, but yet.

Stabilizing income takes time. Consider my rolling three-month average income since I started freelancing. My three-month average monthly income. In February, this metric topped $5,000. Now, it's in the mid-$7,000s, but it took a while to get there.

Finding freelance gigs that provide high pay, high volume, and recurring revenue is difficult. But it's not impossible.

TLDR: Don't expect a steady income increase at first. Be patient.

2. You Have More Value Than You Realize.

Writing is difficult. Assembling words, communicating a message, and provoking action are a puzzle.

People are willing to pay you for it because they can't do what you do or don't have enough time.

Keeping that in mind can have huge commercial repercussions.

When talking to clients, don't tiptoe. You can ignore ridiculous deadlines. You don't have to take unmanageable work.

You solve an issue, so make sure you get rightly paid.

TLDR: Frame services as problem-solutions. This will let you charge more and set boundaries.

3. Increase Your Prices.

I studied hard before freelancing. I read articles and watched videos about writing businesses.

I didn't want to work for pennies. Despite this clarity, I had no real strategy to raise my rates.

I then luckily stumbled into higher-paying work. We discussed fees and hours with a friend who launched a consulting business. It's subjective and speculative because value isn't standardized. One company may laugh at your charges. If your solution helps them create a solid ROI, another client may pay $200 per hour.

When he told me he charged his first client $125 per hour, I thought, Why not?

A new-ish client wanted to discuss a huge forthcoming project, so I raised my rates. They knew my worth, so they didn't blink when I handed them my new number.

TLDR: Increase rates periodically (e.g., every 6 or 12 months). Writing skill develops with practice. You'll gain value over time.

4. Remember Your Limits.

If you can squeeze additional time into a day, let me know. I can't manipulate time yet.

We all have time and economic limits. You could theoretically keep boosting rates, but your prospect pool diminishes. Outsourcing and establishing extra revenue sources might boost monthly revenues.

I've devoted a lot of time to side projects (hopefully extra cash sources), but I've only just started outsourcing. I wish I'd tried this earlier.

If you can discover good freelancers, you can grow your firm without sacrificing time.

TLDR: Expand your writing network immediately. You'll meet freelancers who understand your daily grind and locate reference sources.

5. Every Action You Take Involves an Investment. Be Certain to Select Correctly.

Investing in stocks or crypto requires paying money, right?

In business, time is your currency (and maybe money too). Your daily habits define your future. If you spend time collecting software customers and compiling content in the space, you'll end up with both. So be sure.

I only spend around 50% of my time on client work, therefore it's taken me nearly three years to earn $100,000. I spend the remainder of my time on personal projects including a freelance book, an investment newsletter, and this blog.

Why? I don't want to rely on client work forever. So, I'm working on projects that could pay off later and help me live a more fulfilling life.

TLDR: Consider the long-term impact of your time commitments, and don't overextend. You can only make so many "investments" in a given time.

6. LinkedIn Is an Endless Mine of Gold. Use It.

Why didn't I use LinkedIn earlier?

I designed a LinkedIn inbound lead strategy that generates 12 leads a month and a few high-quality offers. As a result, I've turned down good gigs. Wish I'd begun earlier.

If you want to create a freelance business, prioritize LinkedIn. Too many freelancers ignore this site, missing out on high-paying clients. Build your profile, post often, and interact.

TLDR: Study LinkedIn's top creators. Once you understand their audiences, start posting and participating daily.

For 99% of People, Freelancing is Not a Get-Rich-Quick Scheme.

Here's a list of things I wish I'd known when I started freelancing.

1. Although it is erratic, freelancing eventually becomes stable.

2. You deserve respect and discretion over how you conduct business because you have solved an issue.

3. Increase your charges rather than undervaluing yourself. If necessary, add a reminder to your calendar. Your worth grows with time.

4. In order to grow your firm, outsource jobs. After that, you can work on the things that are most important to you.

5. Take into account how your present time commitments may affect the future. It will assist in putting things into perspective and determining whether what you are doing is indeed worthwhile.

6. Participate on LinkedIn. You'll get better jobs as a result.

If I could give my old self (and other freelancers) one bit of advice, it's this:

Despite appearances, you're making progress.

Each job. Tweets. Newsletters. Progress. It's simpler to see retroactively than in the moment.

Consistent, intentional work pays off. No good comes from doing nothing. You must set goals, divide them into time-based targets, and then optimize your calendar.

Then you'll understand you're doing well.

Want to learn more? I’ll teach you.

10 biases in 10 visuals

Cognitive biases are crucial for UX research, design, and daily life. Our biases distort reality.

After learning about biases at my UX Research bootcamp, I studied Erika Hall's Just Enough Research and used the Nielsen Norman Group's wealth of information. 10 images show my findings.

1. Bias in sampling

Misselection of target population members causes sampling bias. For example, you are building an app to help people with food intolerances log their meals and are targeting adult males (years 20-30), adult females (ages 20-30), and teenage males and females (ages 15-19) with food intolerances. However, a sample of only adult males and teenage females is biased and unrepresentative.

2. Sponsor Disparity

Sponsor bias occurs when a study's findings favor an organization's goals. Beware if X organization promises to drive you to their HQ, compensate you for your time, provide food, beverages, discounts, and warmth. Participants may endeavor to be neutral, but incentives and prizes may bias their evaluations and responses in favor of X organization.

In Just Enough Research, Erika Hall suggests describing the company's aims without naming it.

Third, False-Consensus Bias

False-consensus bias is when a person thinks others think and act the same way. For instance, if a start-up designs an app without researching end users' needs, it could fail since end users may have different wants. https://www.nngroup.com/videos/false-consensus-effect/

Working directly with the end user and employing many research methodologies to improve validity helps lessen this prejudice. When analyzing data, triangulation can boost believability.

Bias of the interviewer

I struggled with this bias during my UX research bootcamp interviews. Interviewing neutrally takes practice and patience. Avoid leading questions that structure the story since the interviewee must interpret them. Nodding or smiling throughout the interview may subconsciously influence the interviewee's responses.

The Curse of Knowledge

The curse of knowledge occurs when someone expects others understand a subject as well as they do. UX research interviews and surveys should reduce this bias because technical language might confuse participants and harm the research. Interviewing participants as though you are new to the topic may help them expand on their replies without being influenced by the researcher's knowledge.

Confirmation Bias

Most prevalent bias. People highlight evidence that supports their ideas and ignore data that doesn't. The echo chamber of social media creates polarization by promoting similar perspectives.

A researcher with confirmation bias may dismiss data that contradicts their research goals. Thus, the research or product may not serve end users.

Design biases

UX Research design bias pertains to study construction and execution. Design bias occurs when data is excluded or magnified based on human aims, assumptions, and preferences.

The Hawthorne Impact

Remember when you behaved differently while the teacher wasn't looking? When you behaved differently without your parents watching? A UX research study's Hawthorne Effect occurs when people modify their behavior because you're watching. To escape judgment, participants may act and speak differently.

To avoid this, researchers should blend into the background and urge subjects to act alone.

The bias against social desire

People want to belong to escape rejection and hatred. Research interviewees may mislead or slant their answers to avoid embarrassment. Researchers should encourage honesty and confidentiality in studies to address this. Observational research may reduce bias better than interviews because participants behave more organically.

Relative Time Bias

Humans tend to appreciate recent experiences more. Consider school. Say you failed a recent exam but did well in the previous 7 exams. Instead, you may vividly recall the last terrible exam outcome.

If a UX researcher relies their conclusions on the most recent findings instead of all the data and results, recency bias might occur.

I hope you liked learning about UX design, research, and real-world biases.