Learn Python programming through beginner-friendly exercises in logic, automation, and data analysis. You’ll gain practical coding experience to solve real business and tech challenges.
Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn the skills needed to become a Python Developer, from Python programming fundamentals to web development with Django and Django REST. Develop in-demand skills with Django, a popular Python web framework used for back-end web programming, and build a portfolio of projects under the guidance of the lead instructor.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Learn Python programming from scratch. Master variables, data types, loops, and functions, and tackle real-world challenges with object-oriented programming. Create a GitHub portfolio to impress employers and master practical Python skills.
Learn how to use Python to extract data from websites and write loops for processing a large number of pages. This course covers topics such as HTML and CSS, Python fundamentals, web scraping exercises, storage and scheduling, and real-life examples of scraping valuable data.
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
Create, style, and enhance web apps with AI capabilities. In this advanced Python course, you'll develop a powerful sentiment analysis web application with Flask and the OpenAI API integration and learn how to take your coding projects to the next level with AI.
Python is a widely used programming language used mainly for developing web applications. It’s regarded as one of the easiest programming languages to learn, and features a versatile makeup. Its applications are numerous, making it a soft after language for web developers.
What Can You Do with Python Training?
As Python is a general-purpose language, there really isn’t much that you can’t do with it. The way things stand and are expected to stand for some years to come, you could theoretically make an entire development career using only Python. That’s perhaps an extreme case, especially as Python is often used to cobble together chunks of code in other languages.
One very popular use of Python is scientific computation, especially in tandem with the NumPy and SciPy libraries that can handle such complicated mathematical functions as arrays and imaginary numbers. The language is thus extremely practical for data science: not only can Python handle massive crunching of numbers (and crunching of massive numbers), but the simplicity of Python’s syntax makes the code accessible to data analysts and scientists who lack a strong programming background.
Python is also employed in the development of web applications. Thanks to libraries such as Django, Flask, and Pyramid, the language can be adapted to create such things as ecommerce (Django includes built-in capabilities for online catalogs and customer checkout), social media platforms, and content management systems (CMS, including DjangoCMS, Mezzanine and Cobra.) Reddit, Uber, Netflix and Facebook are all examples of web applications that employ Python.
Another, and very à la page, use for Python is the field of machine learning and artificial intelligence. Among the language’s strengths in this area are its abilities in natural language processing and its ability to automate repetitive tasks. The algorithm that governs your YouTube recommendations is Python’s machine-learning abilities at work. It’s also the thing behind ChatGPT. Google is hugely fond of the language, and trusts it to hold together the mind-numbing super-secret (way more secret than the In ‘n’ Out Burger sauce that is rumored to be coming to Georgia ere long) algorithm that tabulates its engine’s search results. The nitty-gritty of the search algorithm is still written in C++, however, which is why it can be secret despite Python’s open-source policy. The language’s scalability (its ability to handle larger and larger amounts of data) makes it extremely useful for this type of work, which you can find yourself doing without too much fear of obsolescence, at least until the bots start to program each other.
Studying Python will teach you the basics that you will need to know no matter what you intend to do with the language. After that, you’ll find yourself studying one or more of the language’s most-used libraries and frameworks. Here, what you want to do with Python will factor into the equation: if you’re studying to be a Web Developer, you’ll learn Django, while, if you want to pursue data science, you’ll probably find yourself working with NumPy, pandas, and a lot of math. There are libraries for AI and machine learning as well, including TensorFlow, and you’ll focus on those if your goal is to develop artificial intelligence applications. Finally, and no matter what path you choose, you’ll find that some of Python’s Zen will rub off on you.
Obviously, the first thing you’re going to learn in a Python class is the language itself. You always have to start at the beginning, which with Python means learning about variables (basically storage containers for data values) and operators. Among the latter, * means multiplication,—can denote a negative number or mean subtraction, == means equal, and the logical operators and, or, and not are written out in English rather than symbols. There’s also the amusingly named walrus operator, :=, which is used to assign values such as true or false in expressions. (Note that the kerfuffle over allowing the walrus operator was the crisis that ended with Guido van Rossum stepping down from his position of Benevolent Dictator for Life.) You’ll also learn Boolean, strings, lists, loops, dictionaries, and functions. The meanings of these arcane terms will become apparent as you study the language. You’ll be amazed at how quickly you’ll know enough Python to be able to set up a digital game of Rochambeau or create a random password generator.
With the basics of the language in hand, you can proceed to learn how to use Python for more compelling pursuits than just giving you someone with whom to play rock/paper/scissors. One of the most frequent follow-ups to basic Python is the Django framework, which greatly aids developers trying to create web applications. In particular, Django is designed to help with the server sides of web applications, and provides developers with a highly developed open-source framework that allows them to bypass having to create their applications from the ground up. Django can set up forms and user identification, and even has a password hashing system module available. If you want to use Python and make a career in web development, Django will rapidly become one of your best friends.
Another direction in which your Python education can lead is the world of data science. That field requires the processing of sometimes enormous quantities of data, followed by the Data Analyst’s interpretation of past performance or the Data Scientist’s prognostications for an organization’s future performance. Python enters into this picture by virtue of its computational capabilities, which are enhanced by the existence of libraries such as NumPy (it helps to create large arrays and matrices and then perform calculations with them) and pandas (a tool for data analysis and manipulation.) One of Python’s selling points for data science is that the intelligibility of the language to Data Scientists who aren’t coders makes it possible for the latter to look at and, if need be, get their hands dirty with the code they’re using. Another Python strong point for data science is its vaunted scalability, which, in this case, means that it can handle the kinds of really Big Data companies use today to power their crystal balls.
A further application for Python is the currently much-discussed field of machine learning. An offshoot of advanced statistics, machine learning is a means of letting the computer do the gruntwork when it comes to data processing. Python has an evolved ecosystem of libraries for machine learning, and you can teach the language far more easily to a statistician than you can teach five years’ worth of statistics classes to a programmer. There’s even a Pybiotics library for robotics, although whether that includes a module for Asimov’s Three Rules of Robotics that ensure the benignity of its creations is harder to establish.
Python will teach you to code, possibly more efficiently than any other programming language will. What takes up ten lines of code in C can probably be done with five lines of code in Python. In addition to the mechanics of writing programs for a computer to execute, you’re almost sure to pick up another skill that’s built into the language itself, and which formed one of Guido van Rossum’s prime directives when he constructed the language over that Christmas holiday when he had nothing better to do. The skill is simplicity. Python is not a language for bells and whistles: its code is readable (“readability counts” is one of the aphorisms in “The Zen of Python”) and intrinsically simple (“simple is better than complex” is a further aphorism), and some of that is bound to rub off on you as you learn to translate your computational needs into computer commands. By having Python as your first computer language, you’ll likely learn to bring that simplicity to your programming in other languages when you learn them. That doesn’t mean your C++ code is going to look as elegant on the screen as all those handsome Python indentations, but you’ll have been trained to think in concise terms. That’s a considerable virtue, and not only in programming: the ability to think in simple terms is a valuable rarity in an overcomplicated world.
The received wisdom about learning Python is that it is easy to learn and hard to master. Its readability means that you can quickly pick up basic operators and commands and start constructing simple applications in a matter of days.
Even if you don’t have a clue as to the difference between an operator and an object when you start out, basic Python should come quickly to you. To be able to work with some security and range in the language, you should think in terms of months. With considerable application, that can mean as short a time as two months. On the other hand, quite a bit more time is required if you’re to master, not just the syntax, but also many of the vast number of libraries that go with the language. Your knack for syntactical simplicity is also going to have to develop if you’re to be a true expert in Python capable of constructing elaborate applications or artificial intelligence models.
Longer than the 30-minute Penguin Encounter at the Georgia Aquarium. Not as long as it would take to learn Russian so that you can read War and Peace in the original. You can get a comfortable grasp on the language (Python, not Russian) in about the time it would take you to read Tolstoy’s hefty masterpiece of historical fiction (in English) three times. That comes out to a bit over 100 hours of reading or studying time. Working at Python full-time, you should be reasonably conversant in the language within the space of a month. On the other hand, you’ll need a great deal more time to really master Python, but that will involve not only class time, but also time spent actually programming in the language. For that, you need to figure at least a year.
You may be surprised to discover that, today, the overwhelming majority of classes in Python are given online. The days of being able to find several computer schools in every major city have yielded to those schools embracing the virtual classroom and teaching their classes across the internet. The live class hasn’t entirely bitten the proverbial dust as yet, but, if the trend continues, you won’t have much choice in the matter. You may nonetheless find yourself wishing that there were in-person classes like the ones to which you were accustomed you when you were growing up. The paradigm of teacher, students, and a blackboard all in the same room dates as far back as the invention of the chalkboard in 1801, and obviously (minus the blackboard) before that, as far back as prehistoric education. That means in-person learning has a few thousand years of human history behind it, so it’s no surprise that you might find it comfortable.
The live online class is, however, remarkably similar to the live in-person class, at least once you get past the inescapable difference that all the participants aren’t occupying the same physical space. While you and your instructor won’t be sharing identical geographical coordinates, you will be occupying the same position in time, which means that you can interact with each other just as you might if you were stationed in the same room. You can ask questions through the time-honored mechanism of raising your hand, and you can even invite the teacher to take a look at your screen to get you through a particularly knotty and unpythonic bit of code. On the other hand, you get to study in a comfortable spot of your own choosing, without needing to worry about whether your outfit goes together or having to force your way through rush hour traffic from Downtown to Buckhead. You’re spared the concerns of not getting there in time to secure a good seat, and you won’t have to put up with inclement climate control, gum-cracking neighbors, or being seated downwind of someone doused in toxic cologne. Yes, you will have to get used to the new paradigm of following a class on your computer, but, without being overly fatalistic about it, that’s the wave of the future today already, and, like most waves of the future that are here already, this one is inevitable.
An alternative to the live online class is available from many internet-based schools. Termed self-paced, on-demand, or asynchronous, these classes have participants as passive observers of pre-recorded video tutorials. It’s a lecture format, essentially, but it affords no chance for asking questions of the instructor. On the other hand, the convenience of an on-demand class is terrific: you study when and where you want, assuming that there’s a way to link to the internet from where you happen to be. (You can try out the wifi at Read Shop up in Paces; it’s a bookstore as well as a café, and, as such, worth exploring in itself.) Before you get too excited at being able to learn Python with absolute freedom, there are some drawbacks to self-paced learning, probably the weightiest of which is that tutorials go past their expiry dates very quickly, and not all providers of self-paced Python courses are as careful as they should be when it comes to updating their content. You can thus rack your brains trying to learn something that may turn out to be sorely out-of-date. In addition to that, you’re going to need quite a bit of determination to finish a self-paced class without a teacher to guide and encourage you. It can be done, of course, and some people make terrific autodidacts, but, if you’re not good at sticking with things, self-paced online learning probably isn’t the pedagogical method for you.
If you don’t have a background in front-end development languages, your Python studies will benefit immensely if you learn the holy trinity of, not celery, onion, and green pepper, but HTML, CSS, and JavaScript. These will give you the ability to create the front ends of websites, and, if you throw in Python’s Flask library, you’ll be able to whip up full-stack web applications all by yourself. If you’re after a career in web development, you can’t go wrong with these fundamental front-end tools.
If you already know your HTML, CSS, and JavaScript, you might consider learning another language, preferably a compiled one, that will give you an understanding of what other, more involved, languages are like. Starting two languages at the same time is never a good idea, so get a firm grasp on Python before branching out into something else. What that something else should be is a matter of debate, with the primary choices being C# and Java. If there is a consensus on the issue, it would be in favor of C#, which is easier to read than Java (although nowhere as easy to read as Python) and comes complete with libraries and documentation courtesy of the language’s originator, Microsoft. Java, being open source, is a little messier than C#, but it arguably poses a comparable challenge to Python speakers.
You’ll find plenty of ways to use your Python skills in Altana. The bustling Southern metropolis boasts industry growth and job creation throughout many different sectors. Companies like Goji Labs, Bellwood, and SOLTECH are only a few examples of places in need of Python pros, but the demand spans across many sectors throughout the city.
Perhaps Python’s most pertinent application in the business world is data science, without which today’s businesses wouldn’t know what to do next. That applies to Atlanta’s enormous corporate entities like Home Depot, UPS, and Delta Air Lines, which have entire departments devoted to nothing but this processing of data, down to small companies and even single proprietors who pore over their Google Analytics as though it were a crystal ball. There’s a lot more to data science than Google Analytics, and a combination of Python’s scalability and libraries like NumPy and pandas can turn Big Data into big information.
Although there’s been a post-COVID falling off of startups starting up in Atlanta, the city is nonetheless considered one of the up-and-coming tech hubs in the world. That’s garnered the city the not overly creative moniker of Silicon Peach, and definitely has put it on the radar for the all-important funding that makes a startup ecosystem viable. Among the grown-in-Atlanta companies worth noting are Calendly (business scheduling software), Hermeus (hypersonic aircraft), and Patientory (giving patients more comprehensive access to their medical charts.) These show how broad the options are for software developers in the city. All these companies use Python to varying degrees, although its strengths and current popularity make the language an essential component of just about any software project, even if it is only to interpose stickily between portions of programs constructed in other languages such as C and C++.
Atlanta is a financial capital on a global scale. Known in some quarters as Transaction Alley, some 70% of all financial transactions in the world pass through Atlanta’s computer networks. That makes Atlanta a major center for FinTech, which is to say the technology of finance. Python is well-suited for financial uses such as the development of payment systems, financial management tools, or blockchain. All the virtues of Python—its scalability, simplicity, and popularity—apply to all these cases, but perhaps most of all to blockchain, the revolutionary distributed-ledger technology that makes possible cryptocurrencies such as Bitcoin (blockchain was invented as part of Bitcoin’s birth process) and Ether (Python is an excellent choice for the development of smart contracts.) Python’s adaptability to blockchain benefits from more than 500 ready-to-use code packages that can assist in the development of blockchains for cryptocurrency purposes and beyond. One such library even enables the developer to construct virtual blockchains within a cryptocurrency’s blockchain. Python is thus an essential component of FinTech, which, in turn, is an essential component of Atlanta’s overall thriving tech sector.
Python is used in several ways by nonprofit organizations. The language’s machine-learning capabilities can be harnessed to automate repetitive tasks such as donor management, email campaigns, and event registration. Not-for-profit organizations and NGOs stand to gain immensely from data science, and, as yet, aren’t squeezing anything like the full amount of conclusions from the data they collect so diligently. Python, as one of the principal languages for data analysis, can come to the rescue here, as, for example, predictive modeling can be used to determine where a nonprofit should target its fundraising efforts. Atlanta is also home to several software companies designed to service nonprofits such as GiveSmart (bidding technology for charity silent auctions), Double the Donation (matching gifts with companies that have gift-matching programs), and TeamWorks (automation for fundraising campaigns). Also worth noting is that Python itself is a not-for-profit venture, thanks to the Python Software Federation. Those who use the language for profit sometimes remember that they learned it for free, essentially, and make efforts to give back to the Python community through pro bono programming projects and similar ventures. Thus, an open-source software project is able to keep giving forward. As would befit not only the most popular language of the moment but also an open-source one, Python has distinct value in the not-for-profit sphere.
Although Python wasn’t created for any special purpose, and is endowed with formidable number-crunching capabilities, it can also be used as a language for web development. Python Developers work on creating web applications, usually concentrating on the server-side of things with a leg-up from Django. The average salary for Python developers in Atlanta is approximately $117,000, which is a bit below the national average of $124,000. Note that U.S. government figures mention the anomaly that, while Computer and Mathematical Operations show employment ahead of the national average for the whole sector, there is a distinct shortfall of people employed in the Web Developer rubric, which can be explained either by Atlanta not being a center for Web Development, despite its title of Silicon Peach, or because there are more jobs than qualified candidates in the Atlanta/Sandy Springs/Roswell statistical area.
One of the best-known uses of Python is the field of data analysis. Thanks to libraries such as pandas and NumPy and the language’s adaptability to complex mathematical calculations, Python can be used to digest enormous amounts of data to shed light on a company’s (or an investment’s) past performance, and to cast the results into all manner of visualizations that even the most computer-illiterate of managers can understand. Atlanta’s corps of Data Analysts earns an average salary of roughly $81,000 per annum, which outpaces the national average by a small measure.
Although the terms Data Analyst and Data Scientist (Data Engineer can be tossed into the mix as well) get understandably confused, Data Scientists, strictly speaking, keep their eyes fixed on the future, whereas Data Analysts look back to an organization’s past. Their prognostic abilities account for Data Scientists’ proportionally larger salaries, which, in the Atlanta area, fall out for the most part between $105,000 and $110,000.
A different direction in which Python can be taken in the workplace is the development of all manner of machine learning models and applications that can significantly reduce the time programmers, developers, and scientists spend on computing drudgery. Machine Learning Engineers work in a branch of the mushrooming field of artificial intelligence (AI), and their work ties together Python programming, data science, libraries such as Jupyter and scikit-learn, and data visualization. Salaries for this breed can run quite high, with the United States annual average running to $165,000. In Atlanta, salaries for these workers fall for the most part in the $130,000 to $150,000 range.
Noble Desktop offers the Python Developer Certificate program, one of the most in-depth offerings in the language available, which, in under a month of full-time study, teaches the language’s coding basics before turning to teaching the Django framework to show you how to program web applications. Suppose you’d prefer to study Python for data science. In that case, Noble also offers a Data Science Certificate program that covers using Python together with NumPy and pandas for number-crunching on a grand scale, SQL for querying databases, Python for automation, an extended look at Python for data visualization and dashboards, and, finally, a stop at Python’s machine learning capabilities as they relate to data science. Both these courses include multiple 1-to-1 sessions with a dedicated mentor that may be used however you choose, be it for assistance with classroom topics or job market preparation. Also included is a free retake option (good for one year from the time you complete your class) and access to recordings of your classroom sessions, should you ever want to go over anything a second time.
If you’re in the market for something shorter and more targeted, Noble Desktop offers a Python Programming Bootcamp, an immersive learning experience that will have you programming in the language in under a week of full-time study. The course includes a 1-to-1 bonus training session, along with the retake option and classroom recordings described above.
Noble Desktop can help make your Python wishes come true by bringing one of its expert instructors to your company premises for a live in-person class or by bridging unlimited miles with a teleconferencing platform such as Zoom for a live online class. Noble will work with you to set up a tailor-made curriculum that will improve your team’s Python abilities in precisely the way you desire. If you’d rather take advantage of Noble’s regular roster of Python classes, the school has a voucher program that will allow you to do just that, with discounts for multiple purchases. Feel free to address any questions you might have to Noble’s corporate sales department.
Atlanta’s professional scene is diverse and powerful, making it perfect for anyone looking to expand their skills in tech, design, business, or data. As one of the Southeast’s most important hubs for companies, the city brings together industries like finance, media, technology, and logistics. Through Noble Desktop’s network of experienced instructors, Atlanta-based professionals can access training from experts who truly understand the local job market.
These instructors have spent years in their fields and have developed a sense of pride in imparting their knowledge to others. This is a surefire way for students to turn their interests into real-world, applicable skills. Noble Desktop's Atlanta Python instructors bring years of programming and data experience to the classroom, helping students learn practical coding techniques and real-world problem-solving skills. Their guidance makes complex topics like automation and data science easier to master.
Based in Atlanta, Georgia, Willie Morris is a certified Project Management Professional (PMP) with over 30 years of experience leading initiatives across government, aviation, and transportation industries. Willie, a retired U.S. Air Force veteran, has also held roles with the Department of Homeland Security and the Metropolitan Atlanta Rapid Transit Authority. He has served as a Registered Educational Provider with the Project Management Institute and an Authorized Training Partner with Rita Mulcahy Learning Solutions. Drawing on his background of teaching PMP Exam Prep Certification courses, Willie now instructs professionals in Atlanta and nationwide, helping teams in federal industries strengthen their leadership and project management skills.
Kimberly Peppers dedicated 37 years to federal service, building her career through a range of audit, budget, and program analysis roles. Over the course of those decades, she rose to senior leadership positions, including Regional Inspector General and Audit Director across different federal agencies. She earned her Doctorate in Business Administration while simultaneously working on audit and investigative assignments in the Middle East. After retiring from federal service, Kim continued work in the public sector by moving into the federal consulting field. She is also an instructor at Graduate School USA, teaching mainly finance and accounting classes.
With more than 20 years of both personal and professional involvement in government HR, Ashley is an experienced human resources professional and educator. She currently works as an instructor at Graduate School USA, where she earned a reputation for delivering comprehensive marketing, project management, and HR concepts in a clear, understandable way. Drawing on her extensive experience, Ashley brings a unique twist to her courses through real-life, practical examples that help students bridge the gap between theory and actual application of the skills. Ashley holds a Master’s in Public Administration and remains dedicated to strengthening the public service sector through professional development and advanced education.