Why We Should All Be Angry

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General Assembly (GA) is a community committed to diversity, equity, and inclusion. We aim to provide a welcoming environment for everyone at GA: students, staff, instructors, clients, and anyone who walks through our doors, physical or virtual. No matter what, we strive to uphold our work value to “Keep Getting Better” in our diversity journey.

In the United States, where many in our community are located, there is a long history of violence and harassment against People of Color. Now that many people carry cameras with them and have instant access to social media, these acts of violence and harassment are more likely to be swiftly and readily exposed. In recent weeks, we have experienced a shared sense of grief and horror over the untimely deaths of Ahmaud Arbery, Breonna Taylor, George Floyd, and the harassment of Christian Cooper.

We stand with Black and Brown People and are fully committed to creating physically and emotionally safe spaces for our entire GA community. Black lives matter. We do not tolerate racism or racial harassment of any kind — and we never will. In that spirit, we share this reflection by James Page, General Assembly’s Vice President for Diversity, Equity, and Inclusion:

As a Black man in America, I’ve been aware since my teen years that others’ fears are closely linked to my skin color. While I found some humor when a White woman would clutch her purse as I walked by, there was also significant frustration. I was a nerdy Catholic school kid who liked to crack a joke. However, my identity as a Black man was perceived as dangerous and threatening in a way that superseded anything else about me.

In 2016, I took a trip to the Smithsonian National Museum of African American History & Culture with my 13-year-old son. I will never forget the Emmett Till exhibit, where an open casket holds a photo of Emmett’s beaten and deformed face. I was frozen. I held my son’s hand, and without any real awareness, tears began to roll down my face. 

My son asked me what was wrong. I explained that Emmett was a 14-year-old African-American boy who was visiting family in Mississippi in 1955. A White woman accused him of whistling at her, and he was brutally beaten and murdered by two White men. The killers were found not guilty, even though they admitted to killing him one year later. They were confident that the American legal system would protect them. Sixty-two years later, Emmett’s accuser admitted she lied — he never whistled at her. Her false accusation was enough to end that young man’s life with no recourse to his accuser or his murderers. 

Fair-minded people can agree that taking another human life is wrong, and share the sense of outrage at the senseless, recent deaths of Ahmaud Arbery, Breonna Taylor, and George Floyd. However, the story of Emmett Till and its connection to the story of Amy Cooper speaks to a much deeper pattern of racism, exploitation, and injustice that is pervasive and prevalent in our society. 

Why am I angry at the justice system and our police force? Why am I angry at Amy Cooper? Why should we all be angry? Because she shared the same sense of privilege and entitlement as Emmett’s accuser when she called the police on Christian Cooper. She knew that if she called 911 and expressed fear as a White woman threatened by a Black man, she would be believed, and a Black man would be punished, regardless of what actually happened. She weaponized her racial advantage and it could have been lethal to Christian Cooper: just as it was when Carolyn Bryant lied about Emmett Till, when Eleanor Strubing accused Joseph Spell of rape, and when Tom Robinson was accused of raping Mayella Ewell in To Kill a Mockingbird.

Black men have been conditioned to fear the police, the U.S. justice system, and White women. It is well known that when the cops, or “the posse” show up, the Black man — a 14-year-old Black boy visiting family, a Black man in a consensual relationship with a White woman, a Black character in one of the greatest novels of all time, or a Black Harvard grad birdwatching in a park — can be arrested, beaten, jailed, abused, and subjected to extreme acts of violence. His Black body can be deemed disposable, be made an example of, and deemed unimportant, a piece of property for the public; another piece of “strange fruit – blood on the leaves, blood at the root.” 

While fear is closely linked to my identity, passed on from generation to generation, it is a fear that I must submit to — unbelievable in 2020. I must learn and follow the unspoken rules. I must fear the police, the justice system, bank lenders, the President of the United States, and the White woman clutching her purse — innocuous people or protectors under any other circumstance. As Ta-Nehisi Coates wrote, “It is not necessary that you believe that the officer who choked Eric Garner set out that day to destroy a body. All you need to understand is that the officer carries with him the power of the American state and the weight of an American legacy, and they necessitate that of the bodies destroyed every year, some wild and disproportionate number of them will be Black.”

The only way to end this ongoing cycle is to educate ourselves, show up for People of Color, and get involved in the political process. This is not a new moment in our nation’s history, but part of the ongoing suffering, injustice, and inhumane treatment of minorities; these acts of aggression, violence, and unequal rights we are experiencing right now create real trauma for communities of color who have to live every day in fear. All of us have a role to play in dismantling institutional racism in this country; all of us must help address — and heal — that trauma. Now is the time to stand together and say, “No. More.” 

If you are looking for ways to show up as an ally in this time, here are some places to get started — we share a handful of resources and it is by no means exhaustive: 

  • Spend time reading and learning. Read the work of James Baldwin, Ta-Nahesi Coates, Angela Davis, bell hooks, Audre Lorde, Richard Wright, and Malcolm X. More recent books like How to be Antiracist, White Fragility, Why Are All the Black Kids Sitting Together in the Cafeteria?, and White Rage provide contemporary insight on how to show up for communities of color. Purchase them from your local bookstore, and check out more resources here. They are truly eye-opening.
  • Engage with media created by People of Color. Read TheGrio, The Root, Ellen McGirt’s Race Forward, and Rachel Cargle. Listen to podcasts like Code Switch, Intersectionality Matters, and Race Forward
  • Support organizations that are moving the needle on racial justice. Color of Change, Campaign Zero, the Anti-Racism Project, the NAACP, UnidosUS, and the ACLU are but a handful of the organizations working nationally and locally for social justice issues facing communities of color. Sign up for their mailing lists, donate, respond to their calls to action, and find other ways to get involved. 
  • Stand up for People of Color. When you see wrong, stand up for what is right. Call out racist actions — explicit or implicit — when you see them. When justice is compromised, protest, and challenge it until it creates change. You can learn more about how to be an ally here and here.
  • Get involved in the political process. No matter where you fall on the political spectrum, demand accountability from your elected officials and advocate and support candidates who share your values. Most importantly, vote (register here) – and encourage others in your community to do the same. 

At General Assembly, we will never compromise on ensuring that everyone within our community gets treated with dignity and respect. In the spirit of our shared commitment to learning, we urge all of you to engage on these issues with curiosity, humility, empathy, and self-awareness in service of active dialogue, brave allyship, and the human goodness that can be brought out by all of us. 

How to Find a Job—And Change Careers—During COVID-19

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Over the years, GA’s career coaches have helped thousands of students from our full-time immersive programs land jobs with our A-list hiring partners. Now, with a transformed hiring climate, many career changers are faced with more uncertainty than ever about the likelihood of getting a new role, let alone navigating a job search remotely.

The good news is that there are reasons to be hopeful. In this recorded session, get expert advice from GA’s U.S. career coaches on how job searching has been transformed by COVID-19. Whether you’re on an active job search or curious about what the U.S. job market is like right now, you’ll gain valuable insight about how job seeking has changed and how you can stand out amongst the competition—regardless of your work experience.

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How to Run a Python Script

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As a blooming Python developer who has just written some Python code, you’re immediately faced with the important question, “how do I run it?” Before answering that question, let’s back up a little to cover one of the fundamental elements of Python.

An Interpreted Language

Python is an interpreted programming language, meaning Python code must be run using the Python interpreter.

Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code – a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it’s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.

So to run Python code, all you have to do is point the interpreter at your code.

Different Versions of the Python Interpreter

It’s critical to point out that there are different versions of the Python interpreter. The major versions you’ll likely see are Python 2 and Python 3, but there are sub-versions (i.e. Python 2.7, Python 3.5, Python 3.7, etc.). Sometimes these differences are subtle. Sometimes they’re dramatically different. It’s important to always know which version is compatible with your Python code.

Run a script using the Python interpreter

To run a script, we have to point the Python interpreter at our Python code…but how do we do that? There are a few different ways, and there are some differences between how Windows and Linux/Mac do things. For these examples, we’re assuming that both Python 2.7 and Python 3.5 are installed.

Our Test Script

For our examples, we’re going to start by using this simple script called test.py.

test.py
print(“Aw yeah!”)'

How to Run a Python Script on Windows

The py Command

The default Python interpreter is referenced on Windows using the command py. Using the Command Prompt, you can use the -V option to print out the version.

Command Prompt
> py -V
Python 3.5

You can also specify the version of Python you’d like to run. For Windows, you can just provide an option like -2.7 to run version 2.7.

Command Prompt
> py -2.7 -V
Python 2.7

On Windows, the .py extension is registered to run a file with that extension using the Python interpreter. However, the version of the default Python interpreter isn’t always consistent, so it’s best to always run your scripts as explicitly as possible.

To run a script, use the py command to specify the Python interpreter followed by the name of the script you want to run with the interpreter. To avoid using the full path to your script (i.e. X:\General Assembly\test.py), make sure your Command Prompt is in the same directory as your script. For example, to run our script test.py, run the following command:

Command Prompt
> py -3.5 test.py
Aw yeah!

Using a Batch File

If you don’t want to have to remember which version to use every time you run your Python program, you can also create a batch file to specify the command. For instance, create a batch file called test.bat with the contents:

test.bat
@echo off
py -3.5 test.py

This file simply runs your py command with the desired options. It includes an optional line “@echo off” that prevents the py command from being echoed to the screen when it’s run. If you find the echo helpful, just remove that line.

Now, if you want to run your Python program test.py, all you have to do is run this batch file.

Command Prompt
> test.bat
Aw yeah!

How to Run a Python Script on Linux/Mac

The py Command

Linux/Mac references the Python interpreter using the command python. Similar to the Windows py command, you can print out the version using the -V option.

Terminal
$ python -V
Python 2.7

For Linux/Mac, specifying the version of Python is a bit more complicated than Windows because the python commands are typically a bunch of symbolic links (symlinks) or shortcuts to other commands. Typically, python is a symlink to the command python2, python2 is a symlink to a command like python2.7, and python3 is a symlink to a command like python3.5. One way to view the different python commands available to you is using the following command:

Terminal
$ ls -1 $(which python)* | egrep ‘python($|[0-9])’ | egrep -v config
/usr/bin/python
/usr/bin/python2
/usr/bin/python2.7
/usr/bin/python3
/usr/bin/python3.5

To run our script, you can use the Python interpreter command and point it to the script.

Terminal
$ python3.5 test.py
Aw yeah!

However, there’s a better way of doing this.

Using a shebang

First, we’re going to modify the script so it has an additional line at the top starting with ‘#!’ and known as a shebang (shebangs, shebangs…).

test.py
#!/usr/bin/env python3.5
print(“Aw yeah!”)

This special shebang line tells the computer how to interpret the contents of the file. If you executed the file test.py without that line, it would look for special instruction bytes and be confused when all it finds is text. With that line, the computer knows that it should run the contents of the file as Python code using the Python interpreter.

You could also replace that line with the full path to the interpreter:

#!/usr/bin/python3.5

However, different versions of Linux might install the Python interpreter in different locations, so this method can cause problems. For maximum portability, I always use the line with /usr/bin/env that looks for the python3.5 command by searching the PATH environment variable, but the choice is up to you.

Next, we’re going to set the permissions of this file to be executable with this command:

Terminal
$ chmod +x test.py

Now we can run the program using the command ./test.py!

Terminal
$ ./test.py
Aw yeah!

Pretty sweet, eh?

Run the Python Interpreter Interactively

One of the awesome things about Python is that you can run the interpreter in an interactive mode. Instead of using your py or python command pointing to a file, run it by itself, and you’ll get something that looks like this:

Command Prompt
> py
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

Now you get an interactive command prompt where you can type in individual lines of Python!

Command Prompt (Python Interpreter)
>>> print(“Aw yeah!”)
Aw yeah!

What’s great about using the interpreter in interactive mode is that you can test out individual lines of Python code without writing an entire program. It also remembers what you’ve done, just like in a script, so things like functions and variables work the exact same way.

Command Prompt (Python Interpreter)
>>> x = "Still got it."
>>> print(x)
Still got it.

How to Run a Python Script from a Text Editor

Depending on your workflow, you may prefer to run your Python program directly from your text editor. Different text editors provide fancy ways of doing the same thing we’ve already done — pointing the Python interpreter at your Python code. To help you along, I’ve provided instructions on how to do this in four popular text editors.

  1. Notepad++
  2. VSCode
  3. Sublime Text
  4. Vim

1. Notepad++

Notepad++ is my favorite general purpose text editor to use on Windows. It’s also super easy to run a Python program from it.

Step 1: Press F5 to open up the Run… dialogue

Step 2: Enter the py command like you would on the command line, but instead of entering the name of your script, use the variable FULL_CURRENT_PATH like so:

py -3.5 -i "$(FULL_CURRENT_PATH)"

You’ll notice that I’ve also included a -i option to our py command to “inspect interactively after running the script”. All that means is it leaves the command prompt open after it’s finished, so instead of printing “Aw yeah!” and then immediately quitting, you get to see the Python program’s output.

Step 3: Click Run

2. VSCode

VSCode is a Windows text editor designed specifically to work with code, and I’ve recently become a big fan of it. Running a Python program from VSCode is a bit complicated to set it up, but once you’ve done that, it works quite nicely.

Step 1: Go to the Extensions section by clicking this symbol or pressing CTRL+SHIFT+X.

Step 2: Search and install the extensions named Python and Code Runner, then restart VSCode.

Step 3: Right click in the text area and click the Run Code option or press CTRL+ALT+N to run the code.

Note: Depending on how you installed Python, you might run into an error here that says ‘python’ is not recognized as an internal or external command. By default, Python only installs the py command, but VSCode is quite intent on using the python command which is not currently in your PATH. Don’t worry, we can easily fix that.

Step 3.1: Locate your Python installation binary or download another copy from www.python.org/downloads. Run it, then select Modify.

Step 3.2: Click next without modifying anything until you get to the Advanced Options, then check the box next to Add Python to environment variables. Then click Install, and let it do its thing.

Step 3.3: Go back to VSCode and try again. Hopefully, it should now look a bit more like this:

3. Sublime Text

Sublime Text is a popular text editor to use on Mac, and setting it up to run a Python program is super simple.

Step 1: In the menu, go to Tools → Build System and select Python.

Step 2: Press command +b or in the menu, go to Tools → Build.

4. Vim

Vim is my text editor of choice when it comes to developing on Linux/Mac, and it can also be used to easily run a Python program.

Step 1: Enter the command :w !python3 and hit enter.

Step 2: Profit.

Now that you can successfully run your Python code, you’re well on your way to speaking parseltongue!

– – – – –

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7 Must-Read UX Design Books

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If you search on Amazon for books using the key phrase “UX design”, more than 1,000 titles will appear. That’s a lot of titles to wade through if you’re looking to read about user experience! One of the most difficult parts of making a list of the best UX books is that there are so many awesome ones out there. I could write a must-read list that goes on forever.

I chose the following UX titles either because they have played a significant role in the way I view my place as a UX designer or because they address foundational design topics that every UX designer should understand. I’m leaving out a fair number that have been published in all aspects of design, from usability and research to interaction design and how to present and speak to your design decisions. This reading list is intended for you to use as a starting point.

1. Badass: Making Users Awesome by Kathy Sierra

Kathy Sierra’s book is all about the strategy for creating successful products and services. Badass: Making Users Awesome looks at how to look at a product or service from the user’s perspective.

So instead of relying on marketing tactics that might be unethical, we can create products that lead users to champion them with their friends and family. A win for everyone!

The design and layout of the book is unlike that of most — it lays out the argument with a lot of visuals. And it’s an easy read. This has led to some negative reviews complaining that the book is just a PowerPoint PDF. Lay that aside, and the message is strong. It’s a great look at the point-of-view statement and how a well-written one can be influential in creating awesome products that users love.

When you read this book, it will start to make sense why some products do really well in the market and why others don’t. It will help show you how to shift your design strategy so that it can be successful too.

2. Just Enough Research by Erika Hall

Erika Hall’s book on UX research is a joyful and informative read you could probably finish in a single day. 

This slim how-to manual, published by A Book Apart, walks the reader through the basics of user research, from talking to stakeholders in an organization through analysis and reporting. Hall’s writing style makes the topic — which can be dry in other books — fun and approachable. 

She’s also realistic in her advice to readers. She recognizes the constraints in time and budget that all UX designers face in their day to day jobs, so she proposes how best to navigate these situations and what alternative methods to employ.

Just Enough Research’s current edition was updated with a new chapter on surveys and why designers must be very careful about using these often-abused metrics in their research.

Even if you aren’t a UX researcher, this book explains how you can implement research in your process and spot your own biases so you can design a better user experience.

3. The Design of Everyday Things by Don Norman

The Design of Everyday Things is a standard on most design-reading lists for a reason. This book was originally published in 1988 with the title “The Psychology of Everyday Things”. It was revised in 2013 with a major update to some of the examples to make it more relevant to today. 

Norman’s book lays the landscape for usability in human-centered design. In it, Norman lays out how human psychology affects everyday actions, why it’s natural for humans to make mistakes, and how technology can help rather than cause errors. Norman also explains human-centered design and proposes principles for good design.

I listened to this book on Audible, and a PDF accompanied the audio book so I could view the examples, which are especially helpful in understanding affordances and signifiers.

Vox produced a great video about one of the examples in the book — how doors are designed well, and how they are designed badly. If you’ve ever struggled with figuring out a door, sink, stove, switches, or other interface — the problem isn’t you. It’s the design.

Norman’s classic book explains why bad design happens, what good design is, and the constraints designers face when designing.

4. Designing with the Mind in Mind by Jeff Johnson

Sometimes designers follow a set of rules for designing user interfaces without understanding why certain patterns and methods work. This book changes that.

Jeff Johnson’s Designing with the Mind in Mind lays out the perceptual and cognitive psychology that are the foundation for intuitive interfaces.

For example, how does human perception work? How is the eye structured and how do we read? What can we do as designers to ensure that people can see the information we design?

Johnson walks through an explanation of human vision, attention, memory, and decision-making for a deep-dive into why we perceive the way we perceive. After reading this book, UX designers will have a better idea of why we have design rules so they can make educated decisions about tradeoffs between budget, time, and competing design rules.

5. About Face: The Essentials of Interaction Design 4th Edition by Alan Cooper, Robert Reiman, David Cronin, and Christopher Noessel

About Face completely changed the way I think about interaction design. Admittedly, I’ve only read sections of the book, due to its length. Still, it’s a reference when I have questions about how to approach interaction design and UI design.

This book is broken into three parts. It starts with introducing goal-directed design and how to approach digital projects. Then it moves through designing for behavior and form. Lastly, it looks at the differences in designing for desktop, mobile, and web applications.

My read of the book focused on designing for behavior, and my biggest “ah-ha” moment came when reading about optimizing for intermediate users. Much of the struggle designers have is in how to manage the different needs between beginners and experts. This chapter explains that we should focus on intermediates. We should guide beginning users to become intermediates as soon as possible, and aim to provide opportunities for advanced users to use our products without holding them back.

This book includes a number of other useful concepts to consider when designing user interfaces. At 659 pages, it might be a little too much to read in one sitting, but it should be in the designer library.

6. Don’t Make Me Think by Steve Krug

Steve Krug’s classic book introduced me to usability and usability testing, and launched me into my current career as a UX designer. Don’t Make Me Think, Revisited: A Common Sense Approach to Web Usability is now in its third edition. 

It’s short, easy to read, and a great manual for designers just getting started in usability testing. Krug wrote it based on his 30 years as a usability consultant for organizations including Apple, NPR, and the International Monetary Fund. Even if you already understand why you should do usability testing, chances are you work with people who don’t understand. This book is a great gift for those people. It explains why you should test, how to keep it simple, and how to keep it from being a budget suck. The newest edition has a new chapter about usability for mobile websites and apps, and all of the examples are updated.

If you want to take it a step further, consider Krug’s second book, Rocket Surgery Made Easy: The Do-It-Yourself Guide to Finding and Fixing Usability Problems. This book explains everything you need to know to get started with usability testing with little or no cost. It includes how to recruit, how to conduct a test session, and how to involve your team. 

7. Change by Design by Tim Brown

IDEO CEO Tim Brown explains design thinking and how it should be used at every level of a business. This isn’t a manual for designers. It’s geared towards people outside of the industry, but I included it on this list because of the examples.

IDEO is a well-known human-centered design firm, and the examples Brown provides are straight from IDEO’s project list. While sometimes it feels more like a sales pitch, the case studies are interesting examples of how design thinking is applied.

UX designers who read this book can look at design thinking from a perspective outside the industry and use the examples to explain how design thinking can be used in every industry and in every discipline — it’s not just for designers.

Conclusion

I couldn’t include all of my favorite user experience books on this list. There are just too many amazing titles, and I already have about three times as many on my “to-read” list. These must-read UX design books are a great place to get started if you’re looking for some summer reading to help you advance your design career.


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3 Major Uses for Python Programming

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Python is a popular and versatile programming language. But what is Python used for? If you’re interested in learning Python or are in the process of learning how to code in Python, your efforts will be greatly rewarded as there’s so much you can do with it. In this article, we’ll explore the top three major uses for Python.

Before we dive into the uses, let’s briefly discuss why Python has so many uses in the first place. What characteristics does Python have that allow it to be so useful? Python is:

  • General-purpose: The language was designed to be “general purpose”, meaning it doesn’t have language constructs to force it into a specific application domain. Other programming languages that are general-purpose include (but are not limited to): C++, Go, Java, JavaScript, and Ruby. 
  • Readable: Python is a high-level programming language, meaning it has a higher level of abstraction from machine language and has a simple syntax and semantics (e.g., indentation instead of curly brackets to indicate blocks), which lends to its readability. 
  • Versatile: Python has a large standard library, meaning it comes equipped with a lot of specialized code to handle different tasks. For example, instead of writing your own Python code to read and write CSV files, you can use the csv module’s reader and writer objects. In addition, there are many open-source libraries and frameworks that provide additional value for Python programmers — especially those in machine learning, deep learning, application development, and game development — and scientific computing will find an ample supply of libraries and modules.

What is Python used for? There are so many different tasks that Python can accomplish. You can use it to build recommender systems, create cool charts and graphs, build restful APIs, program robots, conduct scientific computing, manipulate text data or extract text from images; the list goes on and on. 

The best way to think about uses for Python is through the most active and popular disciplines that rely on Python programming:

  1. Artificial intelligence and machine learning
  2. Data analysis and data visualization
  3. Web development

1. Artificial Intelligence and Machine Learning

What it is: Artificial Intelligence is a concept that’s more or less the idea of machines or computers that mimic human cognitive functions such as “learning” and “problem-solving.” Activities like driving a car, playing chess, and answering a question are all structured, logic-based things that humans can do that are being implemented by computers today. At the heart of this activity is machine learning, which is the process that a computer takes to learn the relationships between variables in data so well that it can predict future outcomes (usually on unseen data). If data is the input (“knowledge”), the machines understand the relationships between variables (“learning”) and it can predict what the next step is (“outcome”) — then you have machine learning. 

How Python is used: Artificial intelligence requires a lot of data, which in turn requires appropriate storage, pre-processing, and data modeling techniques to be implemented. Deep learning is the intermediary component; it’s the use of specialized models (neural networks) that can handle “big data” at scale. Python is a programming language of choice for the machine learning, deep learning, and artificial intelligence community due to it being a minimalistic and intuitive language with a significant number of libraries dedicated to machine learning activities, which reduces the time required to implement and get results. R is another popular language used by machine learning enthusiasts and practitioners, but Python tends to be more popular because of the number of machine learning and AI-related efforts coming from the tech community, which uses Python. For example, TensorFlow is Google’s AI platform and open-source software library used for machine learning and the creation of neural networks for AI purposes.

Helpful links: Machine Learning, Python Libraries for Machine Learning

2. Data Analysis and Data Visualization

What it is: Data analysis is the specialized practice of analyzing data, both big and small, for information and insights. Results of data analyses are often visualized, for the benefit of the recipient, and the tools and techniques used to communicate results visually requires the specialization that is known as data visualization. Data analysis and data visualization are not unique to any industry. It’s better to think of them as process-focused roles than industry-specific roles. After all, every company and industry has its own data to work with. What data analysis is not is the management of data from servers and storage, although some data analysts specialize in data management.  

How Python is used: Data analysis and data visualization are specialized roles that can implement Python in ways that are integral to the mission of each role. A data analyst will use Python for data wrangling and data transformation, which is converting data from its raw format to a usable, analyzable format. Then, using open-sourced libraries like Pandas, NumPy, and SciPy, data analysts can manipulate and analyze both numerical and categorical data. In order to visualize data locally, additional libraries such as Seaborn, matplotlib, ggplot, and bokeh, can be used. Some data visualization professionals prefer using Python over business intelligence platforms like PowerBI and Tableau because it’s free, easy to learn, and reduces the need to have to use additional software to create visualizations. 

Helpful links: Data Analysis in Python, Python Libraries for Data Visualization

3. Web Development 

What it is: Web development is a catch-all term for creating web applications and application programming interfaces (APIs) for the web. Web development is a highly specialized role that can be explained by the design pattern known as model, view, and controller (MVC). These terms represent the specialized layers of code of a web application or API. The model involves the code for an application’s dynamic data structure, the view involves the code that directly interacts with the user, and the controller is the code that handles user interactions and works to facilitate input going from the view to the model.

How Python is used: Python has several MVC frameworks that can be used for web application development straight out of the box, and this includes Django, turbogears, and web2py. While a web framework is not required for web development, it’s beneficial to use them as they greatly speed up the development progress. For beginners, learning Python’s syntax and the libraries needed for building a web application or API is a high level of effort, but the alternative would involve a much greater effort, as it would require the knowledge and correct use of multiple programming languages instead of Python.

Helpful links: Full Stack Python: Web Development, Web Frameworks for Python

Conclusion

We’ve explored the major uses for Python, which include machine learning and artificial intelligence, data analysis and data visualization, and web development. If you’re currently learning Python programming, then you’re off to a good start, especially if you’re considering pursuing work in any of the aforementioned areas. For those unsure how to start learning Python, I encourage you to read some of our other posts, which provide more details and tips on how to get started.

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Beginner’s Python Cheat Sheet

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Data Science is rapidly becoming a vital discipline for all types of businesses. An ability to extract insight and meaning from a large pile of data is a skill set worth its weight in gold. Due to its versatility and ease of use, Python has become the programming language of choice for data scientists.

In this Python cheat sheet, we will walk you through a couple of examples using two of the most used data types: the list and the Pandas DataFrame. The list is self-explanatory; it’s a collection of values set in a one-dimensional array. A Pandas DataFrame is just like a tabular spreadsheet, it has data laid out in columns and rows.

Let’s take a look at a few neat things we can do with lists and DataFrames in Python!
Get the pdf here.

Python Cheat Sheet

Lists

Creating Lists

Create an empty list and use a for loop to append new values.

#add two to each value
my_list = []
for x in range(1,11):
my_list.append(x+2)

We can also do this in one step using list comprehensions:

my_list = [x + 2 for x in range(1,11)]

Creating Lists with Conditionals

As above, we will create a list, but now we will only add 2 to the value if it is even.

#add two, but only if x is even
my_list = []
for x in range(1,11):
if x % 2 == 0:
my_list.append(x+2)
else:
my_list.append(x)

Using a list comp:

my_list = [x+2 if x % 2 == 0 else x \
for x in range(1,11)]

Selecting Elements and Basic Stats

Select elements by index.

#get the first/last element
first_ele = my_list[0]
last_ele = my_list[-1]

Some basic stats on lists:

#get max/min/mean value
biggest_val = max(my_list)
smallest_val = min(my_list)avg_val = sum(my_list) / len(my_list)

DataFrames

Reading in Data to a DataFrame

We first need to import the pandas module.

import pandas as pd

Then we can read in data from csv or xlsx files:

df_from_csv = pd.read_csv(‘path/to/my_file.csv’,
sep=’,’,
nrows=10)
xlsx = pd.ExcelFile(‘path/to/excel_file.xlsx’)
df_from_xlsx = pd.read_excel(xlsx, ‘Sheet1’)

Slicing DataFrames

We can slice our DataFrame using conditionals.

df_filter = df[df[‘population’] > 1000000]
df_france = df[df[‘country’] == ‘France’]

Sorting values by a column:

df.sort_values(by=’population’,
ascending=False)

Filling Missing Values

Let’s fill in any missing values with that column’s average value.

df[‘population’] = df[‘population’].fillna(
value=df[‘population’].mean()
)

Applying Functions to Columns

Apply a custom function to every value in one of the DataFrame’s columns.

def fix_zipcode(x):
”’
make sure that zipcodes all have leading zeros
”’
return str(x).zfill(5)
df[‘clean_zip’] = df[‘zip code’].apply(fix_zipcode)

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8 Best UX Design Portfolio Examples

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The UX portfolio website has superseded the business card as a UX designer’s most essential professional networking tool. Especially these days, as the UX design industry pivots abruptly to a predominantly remote professio­­­n, UX designers communicate their professional identities virtually through their online presence to navigate the constricted job market successfully. 

In my middling work experience as a UX designer over the years, I’ve been involved in countless UX portfolio reviews on both sides of the hiring process. Having personally benefited from industry mentorship in my own career, I’m excited to share what inside intelligence I can back with the design community, to encourage emerging UX designers to represent themselves more effectively to hiring managers and potential clients.

As with any other good user experience, a UX portfolio website should consider the user’s mindset during a visit. Bear in mind, companies typically automate their hiring processes using HR software, with workflows designed to evaluate as many qualified applicants as quickly as possible. Conciseness is merciful to reviewers digging through a pile of applications. The reviewer expects immediate access to all the information they need to accomplish their evaluation. Within twenty seconds, they should understand your pitch, get a sense for your work, and have your contact information at their fingertips, ready to take the next step in their hiring workflow.

For full disclosure, I’ve pulled all of these examples from my own personal orbit, and included friends and colleagues who I respect and want to uplift. Let’s take a look at how each designer’s site uniquely succeeds, and look for patterns to model a great UX design portfolio.

1. Total class: Liya Xu

Liya Xu is an accomplished UX designer and Amazon alum, now returning to graduate school to study design management at Pratt. She leverages her technical know-how combined with her visual sensibility to craft all-around excellent applications. Really, check out her work.

This online design portfolio has the character of a fashion spread, with well-selected attributes and succinctly written content. She allows the viewer plenty of breathing room in the empty space of the layout, to process the impact of her UX portfolio content. The case studies fall in reverse chronological order, most recent and impressive work at the top. A visitor gains immediate access to an example of work “above the fold,” peeking up from the bottom of the home screen. The experience conveys an overall modern, professional effect.

2. Authenticity: Seka Sekanwagi

Seka Sekanwagi works at Cash App as a UX researcher and comes from a well-rounded background in product design, interaction design, UX, and UI. His degree is actually in industrial design, the crafting of objects and tools, and he brings that same human-centered mindset to his work. A genuine empathetic interest in other people drives his user research, questioning the meaning behind core user needs and translating them into tangible quality improvements.

The imagery and copywriting of Seka’s design portfolio establish his credibility while expressing his individuality. Selectively-edited messaging demonstrates the level of thoughtfulness that goes into his work output. He formats his work qualifications in simple typesetting, reducing the cognitive load on the visitor, and inviting them to review his qualifications at their leisure.

3. Perfect Pitch: Roochita Chachra

Roochita Chachra is an Austin-based UX designer and recent General Assembly immersive graduate who is highly active in the local creative community. Roochita enters UX design from the adjacent worlds of graphic design and digital marketing and is transitioning her career focus to allow her more opportunity to conduct user research, prototype, and problem-solve.

Whenever repositioning for a new avenue of design, it takes self-restraint to hide old projects which don’t reflect your updated professional image. A UX design portfolio needs to represent the type of work you’re looking for, not just what you’ve done. Roochita focuses hers on the UX design process, and supports it with plenty of explanations and artifacts to show the output.

4. Pure Enthusiasm: Ljupcho Sulev

Ljupcho Sulev approaches his design work with a passion and a positive attitude. Originally from Macedonia, he works for SoftServe out of Sofia, Bulgaria. I had the opportunity to collaborate with Ljupcho on a project, conducting user interviews and analyzing research side-by-side for weeks. His sunny disposition brightens the spirits of his team members and elevates the work.

Ljupcho’s profile is sparse and direct. He highlights his career achievements by pairing photography with bold infographics, letting his enthusiasm pop off the screen. The minimal design aesthetic allows the content to take priority over the visuals. 

5. Scannability: Aimen Awan

Aimen Awan is a UX designer with a background in software engineering and information experience design. Aimen optimizes her case studies for the viewer to scan quickly, with summaries at the top denoting her role and responsibilities on the project. Scrolling down the page, project artifacts illustrate the design process, increasing the fidelity successively up to the final product.

When developing a UX portfolio for a job search, take a lean approach like Aimen — gather feedback, and iterate on your design. We designers are all susceptible to over-designing our work, nitpicking well past diminishing returns. The most useful design portfolio feedback comes from submitting actual job applications and gauging the response, so the earlier you have something ready to share, the better. Think of it as a user test — submitting a batch of applications and fishing for feedback from hiring leads. Every response is a valuable piece of data and should help you refine your messaging and presentation.

6. Approachability: Ke Wang

Ke Wang writes his UX portfolio with a tone of casual levity, with bonus points for rhyming, and his About section reads like a social media status update. He pulls it off because his case studies scroll through examples of his overwhelming talent and work.

Website design covers some crucially important goals which require some entirely human skills. Relating to the site visitor in an approachable way is the hallmark of intuitive user experience and a good heuristic of success.

7. Clear Storytelling: Phill Abraham

Phill Abraham is a graduate of General Assembly’s User Experience Design Immersive course. Like many other UX designers, Phill arrived through a circuitous career path, with a background in psychology and experience in documentary film. He is actively involved in the local design scene, building out his book of projects.

Each case study shapes a compelling narrative of Phill’s design process. A project from his experience as a documentary filmmaker bolsters his UX portfolio and speaks to his capability to perform as a professional. Documentary is, after all, a quintessential form of user research. Phill applies his storytelling sensibility in presenting the case studies, outlining his thorough process step-by-step. As the visitor scrolls down the page, they experience a neat narrative arch outlining the scenario, the design process, and the final product.

8. The Resume Homepage: Samantha Li

Samantha is a Design Manager at Capital One and an all-around UX champion. An active organizer within the design community, she mentors students and early-career UX designers working to break into the industry. Her own UX portfolio website outlines her career journey in the form of an extended resume, dense as a novel. An evaluator doesn’t even have to click to find all of the relevant information.

The resume homepage is a great design pattern for more established professionals with a long list of accomplishments. As a best practice, scrutinize what you publish diligently. Password-protecting case studies helps avoid any disputes over showing sensitive client work, and you may need to censor any personal data that may appear in your photographs and artifacts.

Conclusion

Job hunting poses challenges even for design professionals with advanced experience. Candidates need to squeeze their credentials into a digestible size to communicate their entire work history to reviewers in a short window of attention. The importance of every element of the online UX design portfolio becomes amplified, and dialing in the nuances of messaging makes a difference in getting noticed.Emerging UX designers face an uphill challenge as they’re fleshing out their portfolio projects. UX professionals in the job market are judged by their list of accomplished projects, a frustrating situation for early-career UX designers who may be struggling to get their foot in the door with shorter resumes. The only course of action is bootstrapping through some initial projects — side projects, student projects, volunteer work, and ultimately paid UX design jobs — to demonstrate applied skills. A great UX portfolio effectively communicates your ability and value to potential clients.

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5 Tips for Starting a Career in UX Design

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Are you curious about how to get into UX design? With so many jargon-filled UX designer job descriptions, chatter about software tools, and contrasting perspectives on the future of UX, it can be challenging knowing where to start your career path in UX design

It may be helpful to understand that UX design is a vast field with many opportunities and ultimately benefits from your contributions. In the following article, we’ll help you navigate some of these uncertainties so that you can find your voice amid an ocean of options.

Identify Your Passion

Due to the scale and complexity of what it takes to create successful products and services, becoming a successful UX designer requires perspectives from all walks of life. From research to development to management, UX design is a multifaceted field. 

Because of this, it’s important to start with you and your passions versus responding to what’s outlined in a job description. It’s critical to self-reflect and ask yourself: What inspires me most? What are my strengths? Where would I like to grow?

Another reason you want to start with your passion is that achieving your goal is going to require sustained energy. For example, not everything you try is going to work as you expect. Nor are you going to get everything “right” the first time. This means you need to stay the course as you learn and that drive comes from within you, not from the outside. 

For example, I started in Industrial Design because I had experience making furniture and was passionate about design. I followed popular designers for the time such as Philipe Stark, Philip Johnson, and Jasper Morrison. I saw them as my mentors and did everything I could to emulate some of their thinking.

However, over time, I came to the realization that while I loved their work, they were able to work in a way that was impossible for me. And as I wrestled with this realization, I came to a deeper understanding about myself. What excited me most as a designer was thinking about how people interacted with the product or service. I wanted to understand what was driving people’s behaviors and expectations more than the object itself. The thought of influencing what’s in the world based on people’s feedback became my new interest and has been for the past 15 years. 

OK, so I’ve identified my passion but how do I know where it might fit with user experience design? We can learn a lot by breaking down some of the roles within a typical UX design engagement: 

  • User Research
    • Spends time understanding a product or service user, their needs, and expectations.
    • Creates a foundation of understanding for other teams (e.g. Interaction Design, Front-End Development) to build upon.
  • Interaction Design 
    • Spends time detailing the functionality of a product or service for every user scenario.
    • Creates site maps, user flows, wireframes, prototypes, and navigation paradigms to illustrate a potential solution.
  • UI Design and Visual Design 
    • Spends time organizing and creating visual elements for interfaces, considering the visual details of a UI design such as color, imagery, typography, brand guidelines, and visual hierarchy, similar to graphic design. 
    • Creates illustrations and UI design mockups to illustrate potential solutions.
  • Development 
    • Spends time understanding what it will take, front-end and back-end, to have a product or service built to function the way it’s intended.
    • Creates proof-of-concepts and functioning prototypes making ideas tangible.
  • Product Management 
    • Spends time understanding a specific product, its market, and ways to improve it.
    • Creates product portfolios, roadmaps, return on investment estimates, and continuous improvement plans.
  • Project Management 
    • Spends time aligning teams based on established goals, tracks time and budget.
    • Creates status reports for stakeholders, project timelines, and project wikis.

Stand on Shoulders 

“If I have seen further it is by standing on the shoulders of giants.”

– Sir Isaac Newton 

The phrase “standing on the shoulders of giants” originated from Sir Isaac Newton when asked how he had been so prolific in discovering a wealth of governing laws defining how physical objects interact. The same mentality applies to UX designers as well. You’ll want to soak up as much as you can about how others before you have dealt with and defined their challenges. 

In my case, this meant putting away the glossy design magazines and engrossing myself in the social sciences: sociology, psychology, behavioral economics, etc. Doing so has allowed me to build on the brilliance of many before me in an effort to stay relevant. 

“Stand on shoulders” means coming to the realization there have been many great minds that have impacted the way things are today and by understanding their contributions, you can be effective in how you spend your time building off of their work instead of repeating the same mistakes. 

Keep in mind, this does not relegate you to spending hours in a library. There’s a lot that can be learned from a mentor, for example — a UX designer who’s been working in UX design for some time and is willing to offer their insights based on your needs. This too will help you hit the ground running. 

Get to Know Your Toolbox

Now that you’ve identified your passion and a UX designer to be your mentor, it’s a good idea to begin experimenting with some of the tools you may have been hearing about. Remember, your goal is to become acquainted and perhaps proficient, but not a master. That will come later as you learn further, gain experience, and the tools of your discipline mature. 

When we say tools, that doesn’t necessarily mean software only. There will be many aspects of your practice you’ll need to learn in order to be an effective UX designer. For me, that meant learning different interviewing techniques, fundamentals of body language, practicing active listening, studying storytelling, and presenting to others — all of which have proven to be timeless and fundamental in my career. A short UX design course can provide a good introduction to essential tools, methods, and strategies.

Experiment and Reflect

“Everything is an experiment.” 

– Tibor Kalman

When it comes to creating impactful products and services, it’s critical to keep in mind that we learn by trying things out and reflecting on what happened. In fact, the process of experimentation and reflection is a core tenet of UX design. 

Remember: Words and actions are not the same. You need to put in the work. 

As a UX designer, the more you can demonstrate your thinking by creating concepts and putting them in front of others, the more you will capture the interest of a potential employer. Consider taking a UX design course that helps you create projects for a professional UX portfolio. Don’t worry about making it perfect. Seeing the evidence of your thought process not only helps them see your strengths, but also your potential. 

So, be bold! Try things out. Experiment.

Rinse and Repeat

As the saying goes, the more you learn, the more you realize how much you don’t. The same is true here. UX design is a vast field with many roads. The more you keep at it, the better you’ll get, and becoming an expert UX designer will take time. 

Stay curious, experiment, and have fun!

Remember: 

  • UX design is a vast and multi-faceted field, not to mention ever-changing.
  • Don’t let current titles, tools, and job descriptions intimidate you from taking the first step.
  • Careers require an internal commitment in order to hone your experience and perspective.
  • Embrace the soft skills of your practice because tools are not limited to screens. 
  • Demonstrating your thought process increases your chances of landing your first UX job, and a project-based UX design course can help.
  • Since it is impossible to know what’s going to happen, it’s important to take the first step.
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What is Python: A Beginner’s Guide

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WHAT IS PYTHON?: AN INTRODUCTION

Python is one of the most popular and user-friendly programming languages out there. As a developer who’s learned a number of programming languages, Python is one of my favorites due to its simplicity and power. Whether I’m rapidly prototyping a new idea or developing a robust piece of software to run in production, Python is usually my language of choice.

The Python programming language is ideal for folks first learning to program. It abstracts away many of the more complicated elements of computer programming that can trip up beginners, and this simplicity gets you up and running much more quickly!

For instance, the classic “Hello world” program (it just prints out the words “Hello World!”) looks like this in C:

However, to understand everything that’s going on, you need to understand what #include means (am I excluding anyone?), how to declare a function, why there’s an “f” appended to the word “print,” etc., etc.

In Python, the same program looks like this:

Not only is this an easier starting point, but as the complexity of your Python programming grows, this simplicity will make sure you’re spending more time writing awesome code and less time tracking down bugs! 

Since Python is popular and open source, there’s a thriving community of Python developers online with extensive forums and documentation for whenever you need help. No matter what your issue is, the answer is usually only a quick Google search away.

If you’re new to programming or just looking to add another language to your arsenal, I would highly encourage you to join our community.

What is Python?

Named after the classic British comedy troupe Monty Python, Python is a general-purpose, interpreted, object-oriented, high-level programming language with dynamic semantics. That’s a bit of a mouthful, so let’s break it down.

General-Purpose

Python is a general-purpose language which means it can be used for a wide variety of development tasks. Unlike a domain-specific language that can only be used for specific types of applications (think JavaScript and HTML/CSS for web development), a general-purpose language like Python can be used for:

Web applications – Popular frameworks like Django and Flask are written in Python.

Desktop applications – The Dropbox client is written in Python.

Scientific and numeric computing – Python is the top choice for data science and machine learning.

Cybersecurity – Python is excellent for data analysis, writing system scripts that interact with an operating system, and communicating over network sockets.

Interpreted

Python is an interpreted language, meaning Python code must be run using the Python interpreter.

Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code — a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it’s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.

In other words, instead of having to go through the sometimes complicated and lengthy process of compiling your code before running it, you just point the Python interpreter at your code, and you’re off!

Part of what makes an interpreted language great is how portable it is. Compiled languages must be compiled for the specific type of computer they’re run on (i.e. think your phone vs. your laptop). For Python, as long as you’ve installed the interpreter for your computer, the exact same code will run almost anywhere!

Object-Oriented

Python is an Object-Oriented Programming (OOP) language which means that all of its elements are broken down into things called objects. Objects are very useful for software architecture and often make it simpler to write large, complicated applications. 

High-Level

Python is a high-level language which really just means that it’s simpler and more intuitive for a human to use. Low-level languages such as C/C++ require a much more detailed understanding of how a computer works. With a high-level language, many of these details are abstracted away to make your life easier.

For instance, say you have a list of three numbers — 1, 2, and 3 — and you want to append the number 4 to that list. In C, you have to worry about how the computer uses memory, understands different types of variables (i.e. an integer vs. a string), and keeps track of what you’re doing.

Implementing this in C code is rather complicated:

However, implementing this in Python code is much simpler:

Since a list in Python is an object, you don’t need to specifically define what the data structure looks like or explain to the computer what it means to append the number 4. You just say “list.append(4)”, and you’re good.

Under the hood, the computer is still doing all of those complicated things, but as a developer, you don’t have to worry about them! Not only does that make your code easier to read, understand, and debug, but it means you can develop more complicated programs much faster.

Dynamic Semantics

Python uses dynamic semantics, meaning that its variables are dynamic objects. Essentially, it’s just another aspect of Python being a high-level programming language.

In the list example above, a low-level language like C requires you to statically define the type of a variable. So if you define an integer x, set x = 3, and then set x = “pants”, the computer will get very confused. However, if you use Python to set x = 3, Python knows x is an integer. If you then set x = “pants”, Python knows that x is now a string.

In other words, Python lets you assign variables in a way that makes more sense to you than it does to the computer. It’s just another way that Python programming is intuitive.

It also gives you the ability to do something like create a list where different elements have different types like the list [1, 2, “three”, “four”]. Defining that in a language like C would be a nightmare, but in Python, that’s all there is to it.

It’s Popular. Like, Super Popular.

Being so powerful, flexible, and user-friendly, the Python language has become incredibly popular. This popularity is important for a few reasons.

Python Programming is in Demand

If you’re looking for a new skill to help you land your next job, learning Python is a great move. Because of its versatility, Python is used by many top tech companies. Netflix, Uber, Pinterest, Instagram, and Spotify all build their applications using Python. It’s also a favorite programming language of folks in data science and machine learning, so if you’re interested in going into those fields, learning Python is a good first step. With all of the folks using Python, it’s a programming language that will still be just as relevant years from now.

Dedicated Community

Python developers have tons of support online. It’s open source with extensive documentation, and there are tons of articles and forum posts dedicated to it. As a professional developer, I rely on this community everyday to get my code up and running as quickly and easily as possible.

There are also numerous Python libraries readily available online! If you ever need more functionality, someone on the internet has likely already written a library to do just that. All you have to do is download it, write the line “import <library>”, and off you go. Part of Python’s popularity in data science and machine learning is the widespread use of its libraries such as NumPy, Pandas, SciPy, and TensorFlow.

Conclusion

Python is a great way to start programming and a great tool for experienced developers. It’s powerful, user-friendly, and enables you to spend more time writing badass code and less time debugging it. With all of the libraries available, it will do almost anything you want it to.

Final answer to the question “What is Python”? Awesome. Python is awesome.

8 Tips for Learning Python Fast

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It’s possible to learn Python fast. How fast depends on what you’d like to accomplish with it, and how much time you can allocate to study and practice Python on a regular basis. Before we dive in further, I’d like to establish some assumptions I’ve made about you and your reasons for reading this article:

  • You have little to no prior experience learning Python. 
  • You want to know how long it’s going to take to learn Python.
  • You’re interested in resources and strategies for learning Python.

First, I’ll address how quickly you should be able to learn Python. If you’re interested in learning the fundamentals of Python programming, it could take you as little as two weeks to learn, with routine practice. If you’re interested in mastering Python in order to complete complex tasks or projects, or to spur a career change, it’s going to take much longer. In this article, I’ll provide tips and resources geared toward helping you gain Python programming knowledge in a short timeframe.

If you’re wondering how much it’s going to cost to learn Python, the answer there is also, “it depends”. There is a large selection of free resources available online, not to mention the various books, courses, and platforms that have been published for beginners. More on that in a moment.

Another question you might have is, “how hard is it going to be to learn Python?” That also depends. If you have any experience programming in another language such as R, Java, or C++, it’ll probably be easier to learn Python fast than someone who hasn’t programmed before. But learning a programming language like Python is similar to learning a natural language, and everyone’s done that before. You’ll start by memorizing basic vocabulary and learning the rules of the language. Over time, you’ll add new words to your repertoire and test out new ways to use them. Learning Python is no different.

By now you’re thinking, “OK, this is great. I can learn Python fast, cheap, and easily. Just tell me what to read and point me on my way.” Not so fast. There’s a fourth thing you need to consider, and that’s how to learn Python. Research on learning has identified that not all people learn the same way. Some learn best by reading, while others learn best by seeing and hearing. Some people enjoy learning through games rather than courses or lectures. As you review the curated list of resources below, consider your own learning preferences as you evaluate options.

Now let’s dig in. Below are my eight tips to help you learn Python fast.

1. Cover the following Python fundamentals.

At a bare minimum, you (and your resource) must cover the fundamentals. Without understanding them, you’ll have a hard time working through complex problems, projects or use cases. Examples of Python fundamentals include:

  • Variables and Types
  • Lists, Dictionaries, and Sets
  • Basic Operators
  • String Formatting
  • Basic String Operations
  • Conditions
  • Loops
  • Functions
  • List Comprehensions
  • Classes and Objects

If you’re really pressed for time, all of these fundamentals can be quickly explored on a number of different websites: docs.python.org, RealPython.org, stavros.io, developers.google.com, pythonforbeginners.org. See the section below on “Websites” for more details.

2. Establish a goal for your study.

Before you start learning Python, establish a goal for your study. The challenges you face as you start learning will be easier to overcome when you keep your goal in mind. Additionally, you’ll know what learning material to focus on or skim through as it pertains to your goals. For example, if you’re interested in learning Python for data analysis, you’re going to want to complete exercises, write functions, and learn Python libraries that facilitate data analysis. The following are typical examples of goals for Python that might pertain to you:

  • Data analysis
  • Data science and machine learning
  • Mobile apps
  • Website development
  • Work automation

3. Select a resource (or resources) for learning Python fast.

Python resources can be grouped into three main categories: interactive resources, non-interactive resources, and video resources. In-person courses are also an option, but won’t be covered in this post.

Interactive resources have become common in recent years through the popularization of interactive online courses that provide practical coding challenges and explanations. If it feels like you’re coding, that’s because you actually are. Interactive resources are typically available for free or a nominal fee, or you can sign up for a free trial before you buy. 

Non-interactive resources are your most traditional and time-tested; they’re books (digital and paperback) and websites (“online tutorials”). Many first-time Python learners prefer them due to the familiar and convenient nature of these mediums. As you’ll see, there are many non-interactive resources for you to choose from, and most are free.

Video resources were popularized over the past 10 years by MOOCs (massive online open courses) and resembled university lectures captured on video. In fact, they were often supported or promoted by leading universities. Now, there’s an abundance of video resources for various subjects, including programming in Python. Some of these video resources are pre-recorded courses hosted on learning platforms, and others are live-streamed courses provided by online education providers. General Assembly produces a live course in Python that covers Python fundamentals in one week

Below I’ve compiled a list of resources to help you get a jumpstart on learning Python fast. They fall into the categories laid out above, and at a bare minimum they cover Python basics. Throughout the list, I’ve indicated with an asterisk (*) which resources are free, to the best of my knowledge.

Interactive Resources: Tools and Lessons

  • CodeAcademy: One the more popular online interactive platforms for learning Python fast. I know many Python programmers, myself included, who have taken CodeAcademy’s Python fundamentals course. It’s great for beginners, and you can knock it out in a week. It will get you excited about programming in Python. 
  • DataCamp: Short expert videos with immediate hands-on-keyboard exercises. It’s on-par with the CodeAcademy courses. 
  • *PythonTutor.com: A tool that helps you write and visualize code step by step. I recommend pairing this tool with another learning resource. This tool makes learning Python fundamentals a lot easier because you can visualize what your code is doing. 

Non-Interactive Resources

Non-interactive resources fall into two sub-categories: books and websites.

Books

In researching books, I noticed a majority of them were actually catered to existing programmers interested in learning Python, or experienced Python programmers looking for reliable reference material (“cookbooks”) or specialized literature. Below, I’ve listed only the books I think are helpful for beginners.

  • Introducing Python, 2nd Edition: This book mixes tutorials with cookbook-style code recipes to explain fundamental concepts in Python 3.
  • Learn Python 3 The Hard Way: 52 well-developed exercises for beginners to learn Python. 
  • Python Basics: A Practical Introduction to Python 3: The website says it all — this book is designed to take you from “beginner to intermediate.” 
  • Python Crash Course, 2nd Edition: This book provides a foundation in general programming concepts, Python fundamentals, and problem solving through real-world projects.

Websites

At first, my list started off with over 20 examples of websites covering Python fundamentals. Instead of sharing them all, I decided to only include ones that had a clear advantage in terms of convenience or curriculum. All of these resources are free.

  • *Google’s Python Class: Tutorials, videos, and programming exercises in Python for beginners, from a Python-friendly company. 
  • *Hitchhiker’s Guide to Python: This guide helps you learn and improve your Python code and also teaches you how to set up your coding environment. The site search is incredibly effective at helping you find what you need. I can’t recommend this site enough. 
  • *Python for Everybody: An online book that provides Python learning instruction for those interested in solving data analysis problems. Available in PDF format in Spanish, Italian, Portuguese, and Chinese. 
  • *Python For You and Me: An online book that covers beginner and advanced topics in Python, in addition to introducing a popular Python framework for web applications.
  • *Python.org: The official Python documentation. The site also provides a beginner’s guide, a Python glossary, setup guides, and how-tos.
  • *Programiz in Python: Programiz has a lengthy tutorial on Python fundamentals that’s really well done. It shouldn’t be free, but it is.
  • *RealPython.com: A large collection of specialized Python tutorials, most come with video demonstrations. 
  • *Sololearn: 92 chapters and 275 related quizzes and several projects covering Python fundamentals that can also be accessed through a mobile app.
  • *Tutorialspoint.com: A no-frills tutorial covering Python basics. 
  • *W3Schools for Python: Another no-nonsense tutorial from a respected web-developer resource. 

Video Resources

Video resources have become increasingly popular, and with good reason: they’re convenient. Why read a textbook or tutorial when you can cover the same material in video format on your computer or mobile device? They fall into two sub-categories, pre-recorded video-courses, and live video courses.

Pre-Recorded Courses

  • Coursera: A large catalog of popular courses in Python for all levels. Most courses can be taken free, and paid courses come with certifications. You can also view courses on their mobile app.
  • EdX: Hosts university courses that focus on specific use cases for Python (data science, game development, AI) but also cover programming basics. EdX also has a mobile app.
  • Pluralsight: A catalog of videos covering Python fundamentals, as well as specialized topics like machine learning in Python.
  • RealyPython.com: A collection of pre-recorded videos on Python fundamentals for beginners.
  • *TreeHouse: A library of videos of Python basics and intermediate material.
  • EvantoTutsPlus: 7.6 hours of pre-recorded videos on Python fundamentals, plus some intermediate content.  
  • *Udacity: Provides a 5-week course on Python basics. Also covers popular modules in the Python Standard Library and other third-party libraries. 
  • Udemy: A library of popular Python courses for learners of all levels. It’s hard to single out a specific course. I recommend previewing multiple beginner Python courses until you find the one you like most. You can also view courses on their mobile app.

Live Courses

  • General Assembly: This live online course from General Assembly takes all of the guesswork out of learning Python. With General Assembly, you have a curated and comprehensive Python curriculum, a live instructor and TA, and a network of peers and alumni you can connect with during and after the course.

4. Consider learning a Python library.

In addition to learning Python, it’s beneficial to learn one or two Python libraries. Libraries are collections of specialized functions that serve as “accelerators.” Without them, you’d have to write your own code to complete specialized tasks. For example, Pandas is a very popular library for manipulating tabular data. Numpy helps in performing mathematical and logical operations on arrays. Covering libraries would require another post — for now, review this Python.org page on standard Python libraries, and this GitHub page on additional libraries.

5. Speed up the Python installation process with Anaconda.

You can go through the trouble of downloading the Python installer from the Python Software Foundation website, and then sourcing and downloading additional libraries, or you can download the Anaconda installer, which already comes with many of the packages you’ll routinely use, especially if you plan on using Python for data analysis or data science

6. Select and install an IDE.

You’ll want to install an integrated development environment (IDE), which is an application that lets you script, test, and run code in Python. 

When it comes to IDEs, the right one is the one that you enjoy using the most. According to various sources, the most popular Python IDEs/text editors are PyCharm, Spyder, Jupyter Notebook, Visual Studio, Atom, and Sublime. First, the good news: they’re all free, so try out a couple before you settle on one. Next, the “bad” news: each IDE/text editor has a slightly different user interface and set of features, so it will take a bit of time to learn how to use each one.

For Python first-timers, I recommend coding in Jupyter Notebook. It has a simple design and a streamlined set of capabilities that won’t distract and will make it easy to practice and prototype in Python. It also comes with a dedicated display for dataframes and plots. If you download Anaconda, Jupyter Notebook comes pre-installed. Over time, I encourage you to try other IDEs that are better suited for development (Pycharm) or data science (Rodeo) and allow integrations (Sublime). 

Additionally, consider installing an error-handler or autocompleter to complement your IDE, especially if you end up working on lengthy projects. It will point out mistakes and help you write code quicker. Kite is a good option, plus it’s free and integrates with most IDEs.

7. When in doubt, use Google to troubleshoot code.

As you work on Python exercises, examples, and projects, one of the simplest ways to troubleshoot errors will be to learn from other Python developers. Just run a quick internet search and include keywords about your error. For example, “how to combine two lists in Python” or “Python how to convert to datetime” are perfectly acceptable searches to run, and will lead you to a few popular community-based forums such as StackOverFlow, Stack Exchange, Quora, Programiz, and GeeksforGeeks.

8. Schedule your Python learning and stick to it.

This is the part that most people skip, which results in setbacks or delays. Now, all you have left is to set up a schedule. I recommend that you establish a two-week schedule at a minimum to space out your studying and ensure you give yourself enough time to adequately review the Python fundamentals, practice coding in your IDE, and troubleshooting code. Part of the challenge (and fun) of learning Python or any programming language is troubleshooting errors. After your first two weeks, you’ll be amazed at how far you’ve come, and you’ll have enough practice under your belt to continue learning the more advanced material provided by your chosen resource. 

Concluding thoughts

By this point, we’ve established a minimum learning timeline, you know to select a goal for your study, you have a list of learning resources to choose from, and you know what other coding considerations you’ll need to make. We hope you make the most of these tips to accelerate your Python learning!

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