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.

Here's me. Perfectionism is unnecessary.

This post isn't for people who gag at the prospect of eating french fries. I've been ridiculed for stating you can lose weight eating carbs and six-pack abs aren't good.

My family eats frozen processed meals and quick food most weeks (sometimes more). Clean eaters may think I'm unqualified to give fitness advice. I get it.

Hear me out, though. I’m a 44-year-old raising two busy kids with a weekly-traveling husband. Tutoring, dance, and guitar classes fill weeknights. I'm also juggling my job and freelancing.

I'm as worried and tired as my clients. I wish I ate only kale smoothies and salads. I can’t. Despite my mistakes, I'm fit. I won't promise you something just because it worked for me. But here’s a look at how I manage.

What I largely get right about eating

I have a flexible diet and track my daily intake. I count protein, fat, and carbs. Only on vacation or exceptional occasions do I not track.

My protein goal is 1 g per lb. I consume a lot of chicken breasts, eggs, turkey, and lean ground beef. I also occasionally drink protein shakes.

I eat 220–240 grams of carbs daily. My carb count depends on training volume and goals. I'm trying to lose weight slowly. If I want to lose weight faster, I cut carbs to 150-180.

My carbs include white rice, Daves Killer Bread, fruit, pasta, and veggies. I don't eat enough vegetables, so I take Athletic Greens. Also, V8.

Fat grams over 50 help me control my hormones. Recently, I've reached 70-80 grams. Cooking with olive oil. I eat daily dark chocolate. Eggs, butter, milk, and cheese contribute to the rest.

Those frozen meals? What can I say? Stouffer’s lasagna is sometimes needed. I order the healthiest fast food I can find (although I can never bring myself to order the salad). That's a chicken sandwich or a kid's hamburger. I rarely order fries. I eat slowly and savor each bite to feel full.

Potato chips and sugary cereals are in the pantry, but I'm not tempted. My kids eat them because I'd rather teach them moderation than total avoidance. If I eat them, I only eat one portion.

If you're not hungry and eating enough protein and fat, you won't want to eat everything in sight.

I drink once or twice a week. As a result, I rarely overdo it.

Food tracking is tedious and frustrating for many. Taking breaks and using estimates when eating out help. Not perfect, but realistic.

I practice a prolonged fast to enhance metabolic adaptability

Metabolic flexibility is the ability to switch between fuel sources (fat and carbs) based on activity intensity and time since eating. At rest or during low to moderate exertion, your body burns fat. Your body burns carbs after eating and during intense exercise.

Our metabolic flexibility can be hampered by lack of exercise, overeating, and stress. Our bodies become lousy fat burners, making weight loss difficult.

Once a week, I skip dinner (usually around 24 hours). Long-term fasting teaches my body to burn fat. It provides me one low-calorie day a week (I break the fast with a normal-sized dinner).

Fasting day helps me maintain my weight on weekends, when I typically overeat and drink.

Try an extended fast slowly. Delay breakfast by two hours. Next week, add two hours, etc. It takes practice to go that long without biting off your arm. I also suggest consulting your doctor.

I stay active.

I've always been active. As a child, I danced many nights a week, was on the high school dance team, and ran marathons in my 20s.

Often, I feel driven by an internal engine. Working from home makes it easy to exercise. If that’s not you, I get it. Everyone can benefit from raising their baseline.

After taking the kids to school, I walk two miles around the neighborhood. When I need to think, I switch off podcasts. First thing in the morning, I go for a walk.

I lift weights Monday, Wednesday, and Friday. 45 minutes is typical. I run 45-90 minutes on Tuesday and Thursday. I'm slow but reliable. On Saturdays and Sundays, I walk and add a short spin class if I'm not too tired.

I almost never forgo sleep.

I rarely stay up past 10 p.m., much to my night-owl husband's dismay. My 7-8-hour nights help me recover from workouts and handle stress. Without it, I'm grumpy.

I suppose sleep duration matters more than bedtime. Some people just can't fall asleep early. Internal clock and genetics determine sleep and wake hours.

Prioritize sleep.

Last thoughts

Fitness and diet advice is often useless. Some of the advice is inaccurate, dangerous, or difficult to follow if you have a life. I want to throw a shoe at my screen when I see headlines promising to speed up my metabolism or help me lose fat.

I studied exercise physiology for years. No shortcuts exist. No medications or cleanses reset metabolism. I play the hand I'm dealt. I realize that just because something works for me, it won't for you.

If I wanted 15% body fat and ripped abs, I'd have to be stricter. I occasionally think I’d like to get there. But then I remember I’m happy with my life. I like fast food and beer. Pizza and margaritas are favorites (not every day).

You can get it mostly right and live a healthy life.

Quitting your job for a side gig isn't smart.

A few years ago, I would have laughed at the idea of starting a side business.

I never thought a side gig could earn more than my 9-to-5. My side gig pays more than my main job now.

You may then tell me to leave your job.  But I don't want to gamble, and my side gig is important. Programming and web development help me write better because of my job.

Yes, I share work-related knowledge. Web development, web3, programming, money, investment, and side hustles are key.

Let me now show you how to make one.

Create a side business based on your profession or your interests.

I'd be direct.

Most people don't know where to start or which side business to pursue.

You can make money by taking online surveys, starting a YouTube channel, or playing web3 games, according to several blogs.

You won't make enough money and will waste time.

Nitin directs our efforts. My friend, you've worked and have talent. Profit from your talent.

Example:

College taught me web development. I soon created websites, freelanced, and made money. First year was hardest for me financially and personally.

As I worked, I became more skilled. Soon after, I got more work, wrote about web development on Medium, and started selling products.

I've built multiple income streams from web development. It wasn't easy. Web development skills got me a 9-to-5 job.

Focus on a specific skill and earn money in many ways. Most people start with something they hate or are bad at; the rest is predictable.

Result? They give up, frustrated.

Quietly focus for a year.

I started my side business in college and never told anyone. My parents didn't know what I did for fun.

The only motivation is time constraints. So I focused.

As I've said, I focused on my strengths (learned skills) and made money. Yes, I was among Medium's top 500 authors in a year and got a bonus.

How did I succeed? Since I know success takes time, I never imagined making enough money in a month. I spent a year concentrating.

I became wealthy. Now that I have multiple income sources, some businesses pay me based on my skill.

I recommend learning skills and working quietly for a year. You can do anything with this.

The hardest part will always be the beginning.

When someone says you can make more money working four hours a week. Leave that, it's bad advice.

If someone recommends a paid course to help you succeed, think twice.

The beginning is always the hardest.

I made many mistakes learning web development. When I started my technical content side gig, it was tough. I made mistakes and changed how I create content, which helped.

And it’s applicable everywhere.

Don't worry if you face problems at first. Time and effort heal all wounds.

Quitting your job to work a side job is not a good idea.

Some honest opinions.

Most online gurus encourage side businesses. It takes time to start and grow a side business.

Suppose you quit and started a side business.

After six months, what happens? Your side business won't provide enough money to survive.

Indeed. Later, you'll become demotivated and tense and look for work.

Instead, work 9-5, and start a side business. You decide. Stop watching Netflix and focus on your side business.

I know you're busy, but do it.

Next? It'll succeed or fail in six months. You can continue your side gig for another six months because you have a job and have tried it.

You'll probably make money, but you may need to change your side gig.

That’s it.

You've created a new revenue stream.

Remember.

Starting a side business, a company, or finding work is difficult. There's no free money in a competitive world. You'll only succeed with skill.

Read it again.

Focusing silently for a year can help you succeed.

I studied web development and wrote about it. First year was tough. I went viral, hit the top 500, and other firms asked me to write for them. So, my life changed.

Yours can too. One year of silence is required.

Enjoy!

Recently, I saw an interesting social media post. In an entrepreneurship forum. A blogger asked for help because he/she couldn't find customers. I now suspect that the writer’s occupation is being disrupted by A.I.

Introduction

Artificial Intelligence (A.I.) has been a hot topic since the 1950s. With recent advances in machine learning, A.I. will touch almost every aspect of our lives. This article will discuss A.I. technology and its social and economic implications.

What's AI?

A computer program or machine with A.I. can think and learn. In general, it's a way to make a computer smart. Able to understand and execute complex tasks. Machine learning, NLP, and robotics are common types of A.I.

AI's global impact

AI will change the world, but probably faster than you think. A.I. already affects our daily lives. It improves our decision-making, efficiency, and productivity.

A.I. is transforming our lives and the global economy. It will create new business and job opportunities but eliminate others. Affected workers may face financial hardship.

AI examples:

OpenAI's GPT-3 text-generation

Developers can train, deploy, and manage models on GPT-3. It handles data preparation, model training, deployment, and inference for machine learning workloads. GPT-3 is easy to use for both experienced and new data scientists.

My team conducted an experiment. We needed to generate some blog posts for a website. We hired a blogger on Upwork. OpenAI created a blog post. The A.I.-generated blog post was of higher quality and lower cost.

MidjourneyAI's Art Contests

AI already affects artists. Artists use A.I. to create realistic 3D images and videos for digital art. A.I. is also used to generate new art ideas and methods.

MidjourneyAI and GigapixelAI won a contest last month. It's AI. created a beautiful piece of art that captured the contest's spirit. AI triumphs. It could open future doors.

After the art contest win, I registered to try out these new image generating A.I.s. In the MidjourneyAI chat forum, I noticed an artist's plea. The artist begged others to stop flooding RedBubble with AI-generated art.

Shutterstock and Getty Images have halted user uploads. AI-generated images flooded online marketplaces.

Imagining Videos with Meta

Meta released Make-a-Video this week. It's an A.I. app that creates videos from text. What you type creates a video.

This technology will impact TV, movies, and video games greatly. Imagine a movie or game that's personalized to your tastes. It's closer than you think.

Uses and Abuses of Deepfakes

Deepfake videos are computer-generated images of people. AI creates realistic images and videos of people.

Deepfakes are entertaining but have social implications. Porn introduced deepfakes in 2017. People put famous faces on porn actors and actresses without permission.

Soon, deepfakes were used to show dead actors/actresses or make them look younger. Carrie Fischer was included in films after her death using deepfake technology.

Deepfakes can be used to create fake news or manipulate public opinion, according to an AI.

Voices for Darth Vader and Iceman

James Earl Jones, who voiced Darth Vader, sold his voice rights this week. Aged actor won't be in those movies. Respeecher will use AI to mimic Jones's voice. This technology could change the entertainment industry. One actor can now voice many characters.

AI can generate realistic voice audio from text. Top Gun 2 actor Val Kilmer can't speak for medical reasons. Sonantic created Kilmer's voice from the movie script. This entertaining technology has social implications. It blurs authentic recordings and fake media.

Medical A.I. fights viruses

A team of Chinese scientists used machine learning to predict effective antiviral drugs last year. They started with a large dataset of virus-drug interactions. Researchers combined that with medication and virus information. Finally, they used machine learning to predict effective anti-virus medicines. This technology could solve medical problems.

AI ideas AI-generated Itself

OpenAI's GPT-3 predicted future A.I. uses. Here's what it told me:

AI will affect the economy. Businesses can operate more efficiently and reinvest resources with A.I.-enabled automation. AI can automate customer service tasks, reducing costs and improving satisfaction.

A.I. makes better pricing, inventory, and marketing decisions. AI automates tasks and makes decisions. A.I.-powered robots could help the elderly or disabled. Self-driving cars could reduce accidents.

A.I. predictive analytics can predict stock market or consumer behavior trends and patterns. A.I. also personalizes recommendations. sways. A.I. recommends products and movies. AI can generate new ideas based on data analysis.

Conclusion

A.I. will change business as it becomes more common. It will change how we live and work by creating growth and prosperity.

Exciting times,  but also one which should give us all pause. Technology can be good or evil. We must use new technologies ethically, fairly, and honestly.

“The author generated some sentences in this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication. The text of this post was further edited using HemingWayApp. Many of the images used were generated using A.I. as described in the captions.”