Master Python Data Science 
in 10 Minutes per Day

• Master Python data science syntax in 6-8 weeks
• Master Python data science in as little as 10 minutes
  per day of practice
• Get the skills you need for 6-figure data science job
• Even if you have limited time …

I’m sure you know that data science skills are extremely valuable.

And you want to increase your earning power.

For example ….

According to Glassdoor, right now the AVERAGE salary for a U.S. data scientist is almost $150,000.

And at some companies, total compensation is over $300,000 (if you include stock, bonuses, etc).

Becoming a top-tier data scientist is worth thousands of dollars.

And even if you’re a developer or software engineer …

Then adding data skills to your skill set can increase your income by $10,000 to $20,000 per year.

Data science is extremely valuable.

Sounds great, right?

Yeah …

BUT, getting one of those data science jobs will be challenging.

And a few specific problems will stand in your way …

Let’s face it: learning data science is hard.

There’s a LOT to learn.

• data cleaning
• data wrangling and aggregation
• data visualization (including many different types of charts and graphs, along with how to modify those visualization)
• data analysis
• machine learning

And some people will even tell you to spend time learning advanced math.

It can be a little overwhelming.

Additionally, a lot of material online is either confusing or poorly structured.

So it can be very confusing to figure out exactly what to learn, and how to learn it.

Data science is hard enough for most people.

But the general difficulty is made worse by another factor.

Time.

It’s very hard to find time.

You’re busy, right?

Work, friends, social life, maybe a hobby or two.

You have a busy life and it’s hard to find time to master data science programming languages and techniques.

And to top it off, I bet you forget a lot of what you learn.

Have you ever learned some Python syntax, and the forgotten it?

It’s very common …

But forgetting the things you learn will make it even harder.

Because you’ll waste even more time re-learning things.

Finding enough time to study, practice, and master data science is a big challenge.

If you want to get a highly-paid data science job, there’s going to be several hurdles that you’ll need to get over.

But maybe the biggest hurdle is …

Companies are going to test you.

They’re going to test your ability to write data science code.

They’re going to test that you have the right skills.

For example, before I got my job at Apple, they tested me on my ability to write data science code …

And over my career, I’ve interviewed at many of the biggest companies in the USA, like Meta (Facebook), LinkedIn, Bank of America, and others.

Every single one tested me on my ability to write code.

So as a data science candidate, you need to understand, that they’re going to test you.

Like it or not, if you want a $150,000 data science job, you need to have the code memorized in order to pass your data science coding interview.

A few years ago, I was in a similar position as you.

I wanted to upskill and make more money …

But many of the skills I needed to learn were difficult.

AND I kept forgetting syntax.

AND I was really busy on top of that.

I needed to find a more effective, more efficient way to learn.

So I took a bit of a sidetrack …

I researched how to learn faster, more efficiently, and with better long term memory of things like complex data science syntax.

I read about how Navy SEALs train. I read about how chess masters train. I read about how polyglots learn new languages (polyglots are people who speak multiple languages).

Eventually, I started noticing patterns.

All of these elite performers use similar practice methods to train and learn new skills.

Finally, I “cracked the code” on learning data science.

And everything changed.

What if you could master data science in as little as 10 minutes per day?

After months (even years) of struggling to learn data science, I finally “cracked the code.”

I developed a training system to enable me to very rapidly learn new data science skills.

This system is hyper-efficient.

With as little of 10 minutes of practice per day, this system has enabled me and my students to master data science in Python.

And importantly, this system has helped me and my students memorize the code.

If you practice using our system, you’ll be able to write Python data science code “with your eyes closed” …

You’ll be able to wrangle, visualize, and analyze data for any data science coding exam.

If you practice like we show you, you’ll be able master Python data visualization in a couple of months, in as little as 10 minutes per day.

Here’s how …

Learn practical
data science skills

Python Data Mastery focuses on critical, essential data science skills. 

This course avoids unnecessary theory and BS skills that you don’t need.

When you join, you’ll learn the essential foundations of data science in Python.

You’ll start by learning base Python.  Even if this is a bit of a review, you’ll get important practice exercises to help you become “fluent” in all of the important techniques of base Python.

Next, you’ll learn data manipulation.  This will include techniques from NumPy, but also techniques from Pandas.  These toolkits are essential for shaping and “wrangling” data in Python.

Finally, you’ll learn data visualization.  Data visualization is critical at every part of the data workflow, from data exploration, to data analysis, to advanced techniques.

Memorize Data Science Syntax

Forgetting data science syntax is a huge barrier to productivity, because you constantly need to stop, and search for how to do something.

That’s why we developed a unique practice system that will help you memorize syntax.

This practice system is based on key insights from cognitive psychology …

By showing you how to carefully structure your practice, we’ll help you “hack” your memory so that you remember all of the critical syntax for data science in Python, permanently.

This will dramatically increase your “fluency” with writing data science code.

… which in turn, will make you vastly more productive.

Discover How to “Put the Pieces Together”

To do real data science work …

Things like data cleaning and data analysis …

You often need to combine multiple tools together in specific ways.

In Python Data Mastery, you’ll discover how to combine together tools from Python data science packages to accomplish real tasks.

We’ll show you how to “put the pieces together” to do real work.

Become “fluent” in python

Importantly, this course is designed to help you become “fluent” in writing Python code.

When most students learn data science in Python, they “learn” techniques, but then forget almost everything.  So when they try to work on a project, they struggle and have to do Google searches for syntax every 5 minutes.

Python Data Mastery solves that problem.

Inside the course, you’ll discover a secret training system that will enable you to memorize everything you learn.

This system will enable you to become “fluent” in writing Python code.  When you eventually begin working on Python data science projects, you’ll write code quickly, effortlessly, and from memory.

Master data science within weeks

Python Data Mastery is designed to be the most efficient, most effective way to master data science in Python.

Ultimately, if you follow the system, you will be able to master critical data science skills within weeks.

The first key to learning efficiently is the structure of the course.  We’ve structured this course methodically.  You’ll learn the right material at the right time.  One topic will flows into the next … so learning will be simple, natural, and effortless.

Moreover, our unique training system is desigened for maximum efficiency.  We used insights from psychology and neuroscience to “hack” your memory.  When you follow our training system, you’ll effortlessly memorize all of the Python syntax you learn within weeks.

Master data science in 10 minutes per day

I realize that you have very limited time.

So I designed Python Data Mastery to work in as little as 10 minutes per day.

Out practice system uses insights from neuroscience to maximize your training efficiency.

With as little as 10 minutes of practice per day, you’ll be able to learn, memorize, and master how to do data wrangling, data visualization, a data analysis in Python.

It’s the system that I wish that I had years ago …

And if you enroll today, it’s all yours …

A step-by-step system for learning Python data science that’s simple, easy, and effective.

In Python Data Mastery, you’ll learn essential data science skills

Python Data Mastery will teach you several critical areas of data science: basic programming, data manipulation, and data visualization.

To do this, you’ll learn four areas within Python:

• Base Python
• NumPy
• Pandas
• Matplotlib

Specifically, here’s what you’ll learn when you join:

Base Python

In the first two modules, you’ll learn base Python.

The Python programming language forms the foundation of data science in Python, so you need to know quite a bit about data types, logic, and control structures.

With that in mind, you’ll learn:

• data types
• how to perform mathematical calculations in Python
• logical expressions
• strings
• lists and tuples
• if/else statements
• for loops
• functions

If you’re a true beginner to Python, these topics will be explained in a clear, easy to understand manner, so it will all be crystal clear.

And if you have a little experience with Python, you’ll discover a training system that will enable you to become “fluent” in all of these foundational techniques.

NumPy

After learning about base Python, you’ll learn about NumPy.

The NumPy package is a toolkit for working with numeric data. It’s widely used in a variety of tasks within data science, like statistics, data visualization, and machine learning.

Because it’s so common, you really should know Numpy backwards and forwards.

In the NumPy lessons, you’ll learn everything you need to know, like:

• What NumPy arrays are
• How to think about array axes
• How to reshape NumPy arrays
• How to “split” and “combine” NumPy arrays
• How to aggregate the numbers in NumPy arrays (i.e., sum, average, min, max)
• How to generate NumPy arrays with random data

Again, everything will be explained in a clear, straightforward manner that makes it easy for you to understand.

And you get practice exercises that will enable you to master every tool and technique that you learn.

Pandas

Next, you’ll learn about Pandas, which is a toolkit for working with “dataframes.”

If you’re not familiar with them, dataframes are row-and-column data structures that are very common in data science.  They are sort of like Excel spreadsheets, and they are extremely common for storing and analyzing data.

Specifically, you’ll learn:

• What dataframes are
• How to create dataframes from raw data
• How to import csv data into a dataframe
• How to add variables to dataframes
• How to delete variables from dataframes
• How to subset and “slice” dataframes
• … and more

Pandas is extremely useful for doing data manipulation in Python. In fact, you’ll need Pandas in almost every part of the data science workflow. We’ll help you learn it so well, you’ll be “fluent” in writing Pandas code to manipulate your data.

Matplotlib

 In the Matplotlib modules, you’ll learn:

• How to create bar charts
• How to create scatter plots
• How to create histograms
• How to create line charts
• How to format your data visualizations with “style sheets”

Seaborn

Finally, you’ll also learn about Seaborn.

Seaborn is another data visualization toolkit for Python.  The advantage of Searborn is that it’s easier to use than Matplotlib, and it works better with Pandas DataFrames.

 In the Seaborn modules, you’ll learn:

• How to create Seaborn bar charts
• How to create scatter plots with Seaborn
• How to create density plots
• How to create line charts with Seaborn
• How to make “small multiple” charts (this is a powerful technique!)
• … and more

Learn how to combine syntax

Once you master the individual tools from Pandas, Numpy, and Seaborn, we’ll show you how to “put the pieces together” …

You’ll learn how to combine syntax together to do real data science work.

This is the secret to real productivity with Python.

Once you learn how to combine syntax together, you’ll be able to wrangle and visualize your data like a pro.

(If you’ve been looking for something like dplyr pipes from R, you need to learn this!)

Data Cleaning
and Dataset Preparation

After you learn how to combine syntax, you’ll learn the process for data cleaning and dataset preparation.

In Python Data Mastery, you’ll learn a step-by-step process for getting, cleaning, and reshaping your data.

You’ll learn the high-level process. But we’ll also show you a clear data preparation case study, so you can see exactly how to use Pandas and other tools to clean, reshape, and combine multiple data files into a single dataset that’s ready for visualization and analysis.

If you’re struggling with cleaning your data, then these lessons can show you exactly what to do, and how to do it.

Data Analysis

You’ll also learn a step-by-step process for analyzing your data.

So you’ll learn the high-level process of data analysis …

But you’ll also see step-by-step examples of how data analysis is actually done in Python.

We’ll show you how to use data wrangling and data visualization tools together to explore a dataset and find the insights that companies want.

Machine Learning Essentials
with Scikit Learn

After you learn data analysis, you’ll learn machine learning essentials with Scikit Learn.

You’ll learn what machine learning is, and learn about the high-level process of building ML models.

You’ll also learn the essential syntax of scikit learn.

These lessons will give you a solid foundation in machine learning essentials that will prepare you for more advanced future study.

Bonus Vaults

Python Data Mastery contains bonus vaults of extra material.

• data visualization teardowns
• examples of end-to-end projects

In the “data visualization teardown” vault, you’ll learn more about “how to think” about data visualization, and how to “tell stories with data.”

In these video lessons, I break down data visualizations and explain the good, the bad, and the ugly. These lessons will help you sharpen your understanding of how to effectively use data visualizations to communicate valuable insights.

In the project vault, you’ll see an example of an end-to-end project that involves getting data (from Wikipedia), cleaning the data, and visualizing it. This video lesson integrates many of the skills learned in the course, and shows you another example of how they are actually used, in practice.

The code that you see in this vault will help you understand how everything fits together, and will give you ideas for how you can put your new data skills to use.

BONUS:
5 Visualization Mistakes
That Make You Look Like an Amateur

Data visualization is as much about good design as it is about writing syntax to create visualizations.

And a big part of good design is avoiding simple design mistakes.

So in this bonus, I’ll show you 5 of the most common data visualization mistakes that amateurs make.

These are things that you NEED to know, so you know what to avoid doing.

You’ll learn:

• 3 Major Plot Title Mistakes
• The #1 Worst Visualization That You Need to AVOID (hint: it’s extremely common)
• The Reason that 3D Charts are Terrible (and what to do instead)

… and more

This bonus will further sharpen your intuition about how to create great visualizations by telling you 5 things that you absolutely need to avoid.

(You really need to see this, because you’re probably doing at least 1 or 2 of them …)

BONUS:
5 Ways To Improve
Your Data Visualizations

Again … data visualization is about more than just writing syntax.

A lot of visualization is knowing how to use different elements of a plot to communicate to an audience visually.

Have you ever heard someone say the phrase “telling stories with data”?

Visual design is a big part of that.

You have to know how to structure your visualizations.

That’s why in this bonus, I’ll show you 5 simple ways to improve the design of your visualizations.

You’ll learn:

• The RIGHT Way to Use the Plot Title
• How to Use the Plot Subtitle
• Why Plot Annotations are Useful (And what to Use Them For)

… and more

In this bonus, you’ll learn more tips and strategies to improve your visualizations, so you create your charts and graphs like a high-paid pro.

BONUS:
Data Science Cheat Sheets

Even though this course will help you memorize critical Python data science syntax, there might be some times when you just need a quick reminder.

So included with the course, you’ll get a …

• Numpy Cheat Sheet
• Pandas Cheat Sheet
• Seaborn Cheat Sheet

You can keep the electronic versions on your computer’s desktop for easy reference, or even print out a hard copy to put nearby.

BONUS:
SQL Essentials

Pandas, Numpy, and Seaborn are obviously necessary for data science in Python.

But as a data scientist, SQL is also very important for retrieving data from databases (particularly in large corporate environments).

This bonus will show you the essentials of SQL, as a compliment to your Python data science skills.

When I worked at Apple and Bank of America, SQL was a big part of the job.

So this bonus will probably be very important for you.  

If you want a data science job, these lessons along could be worth the price of the course.

Python Data Mastery has been designed to help you memorize Python data science syntax.

Like it or not, you need to know Pandas syntax.

For example, when you interview for a data job, they’re going to test you to make sure you know the syntax. They’re going to check that you know what you’re doing.

So imagine…

You’re in your data science coding interview, and they ask you to do some data inspection and visualization on a dataset …

They ask you to load a dataset, inspect it, and then visualize it.

You smile briefly, and then effortlessly type the code ….

Imagine how satisfying it will be to ace your coding interview …

How exciting it will be to get the job offer …

How great it would be to make thousands more than you’re already making.

It’s all possible.

When you join Python Data Mastery, you’ll discover a training system that will help you master all of the critical Python data science syntax you’ll need.

Our training system will enable you to memorize the syntax for cleaning, wrangling, visualizing, and analyzing your data.

This is the key to helping you pass your data science coding interview …

And it’s the key to making you a productive data scientist that companies will pay a lot of money to.

About Your Instructor

I’ve been doing data science and analytics since 2004 (literally since before we called it data science).

I’ve done data science and analytics at some of the biggest and best companies in the world, including Apple, Bank of America, HP, Ogilvy, Kraft, and many others.

Over the course of my career, I’ve written 10s of thousands of lines of data science code for data wrangling, visualization, and analysis.

I believe that data science can be simple and easy, IF you know what you’re doing.

And to know what you’re doing, you need great mentorship.

Therefore, I designed this to teach you what I know, in clear, simple language.

I want to turn you into a skilled data science pro, as quickly and efficiently as possible, using our step-by-step training system.

Read What our Students are saying about  our courses and training method

 I get it …

You’re probably skeptical.

Becoming “fluent” in Python in a few weeks sounds too good to be true.

Mastering essential data science in Python in a few weeks doesn’t seem possible …

But it IS possible. 

You can become “fluent” in Python and master data science skills in only a few weeks.

Our past students have had amazing results.

Here’s what our past students have had to say about our courses and training methodology:

Frequently Asked Questions

This could be worth hundreds of
thousands of dollars

According to Glassdoor, the average salary of a data scientist is almost $150,000 per year. 

That’s roughly DOUBLE the median household income in the US.

Do the math.

Mastering data science skills could be worth tens of thousands of dollars in a single year.

And over the course of your career, data science skills could be worth hundreds of thousands of dollars.

Python Data Mastery is specifically structured to help you learn and master essential data science skills as fast as possible.

Don’t hesitate.

The return on investment could easily be 10x what you spend on the course.

Invest in your future by enrolling now.

You could save yourself
years of time

Time is your most valuable resource.

It’s a terrible waste of your time to try to figure data science out by yourself. Some data science students waste years learning useless skills, or struggling to learn anything at all, because they don’t have any plan.

Python Data Mastery will save you valuable time …

The course is highly structured and extremely efficient.

It’s designed to enable you to learn data science in Python as fast as possible, by teaching you the right things, in the right order, and giving you a high-performance practice system for mastering what you learn.

You could save yourself hundreds of hours of unnecessary work.

Don’t try to figure it out alone.

If you join, you’ll get a step-by-step plan.  You’ll also get a high performance practice system that will enable you to master data science skills in a matter of weeks.

Don’t try to figure it out alone. Save yourself your valuable time.

Do the math. If you value an hour of your time at $50 dollars, and this course saves you just 100 hours, that’s $5000 saved.

And personally, I value my time a lot higher than $50 an hour.

If you do too … if you value your time at more than $50/hour, then you could save thousands of dollars.

Do the math.

This course could save you thousands of dollars, just by saving you time.

Invest in a system that works

If you’re like most data science students, you’ve tried a lot of methods to learn data science.

… books, free blog posts, YouTube videos, searches on Google and Stack Overflow.

You might have even taken a few online courses.

I probably don’t need to tell you this, but most methods simply don’t work.

Let me ask you: have you read a data science book and “learned” a few things, but then found that you forgot almost all of it a few weeks later?

I’d be willing to bet that you have.

I personally know one data science student who spent $30,000 on a prestigious data science masters degree.

He learned a few things, but a few weeks after the program ended, he realized that he forgot most of it.

Think about that.  $30K.  Gone.

There’s a happy ending to this story though.

He enrolled in one of our data science courses, and became highly proficient in writing data science code within a few weeks.

And there are many other students who have had great results.

You can get great results too …

…. imagine if you were fluent in writing Python code.

…. imagine if you could memorize data science syntax.

…. imagine that you could do these things in a matter of weeks.

You can, if you invest yourself and enroll in Python Data Mastery.

Learn data science
from home

If you find yourself at home more often right now, Python Data Mastery is perfect for you.

This course is 100% online.

The lecture lessons are taught by video, and you can watch them whenever you like. And you can watch the videos as many times as you like.

We provide you with practice code that you can download and run on your home computer, along with a set of “code walkthrough” videos that explain the code line by line.

And the course will give you a set of practice exercises that you can use to master all of the techniques. You can do this practice at home, whenever it’s convenient for you.

With Python Data Mastery, you can master data science in Python from the comfort of your couch or home office.