Develop Python programming through interactive projects in automation and analytics. Learn how coding fundamentals support modern problem-solving needs.
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 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 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 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.
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.
Get the skills you need for a career in finance technology with the FinTech Bootcamp. Learn Python programming, data science, financial analysis, data visualization, and machine learning to become a Financial Analyst, Data Scientist, or Data Analyst.
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 front-end development with HTML & CSS, JavaScript, and React, and back-end development with Python, Django, and Node.js. Get 1-on-1 mentoring and a structured curriculum to become a skilled Software Engineer or Full Stack Developer.
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 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.
Gain practical skills in analyzing datasets, building predictive models, and creating AI-powered applications using Python and industry-leading tools.
Since 2012, local planning, industry cooperation, and targeted investment have dramatically boosted San Diego’s profile as a high-tech leader. The city has attracted several major tech employers, including regional offices for companies like Google, Apple, Hewlett Packard, and Amazon; home offices for corporations like Qualcomm and Teradata; and successful tech startups in FinTech, artificial intelligence, and mobile app development. Support from investors, a large educational community including several universities, and regional partnerships like Tech San Diego ensures that tech startups have the funds, skilled employees, and contacts they need to grow. Software is the common bond across this tech sector, and Python is one of the most popular coding languages. Students in San Diego can choose between many Python classes, and the area even boasts a special interest group, San Diego Python, for both professional coders and Python enthusiasts.
From modest beginnings as a single programmer’s hobby project in the late 1980s, Python has grown into one of the world’s most popular programming languages. Python was first released by creator Guido van Rossum in 1991 and has been expanded since then by an active development community. Because of this origin, Python is free to use, open source, and adapted for all major computing platforms. Thanks to its valuable qualities, Python is also used in almost every industry that needs software. Nearly any kind of program, from data analysis systems to web code to video games, can be written in Python. This widespread use is related to Python’s vast code libraries: its respectable base library of pre-programmed functions plus the 500,000+ specialized libraries created by its contributors. These libraries greatly simplify programming for any task, including data analysis (NumPy, Pandas), web development (Django, Flask), machine learning (TensorFlow, Ramp), and video gaming (Pygame).
Compared to other programming languages, Python is distinguished as:
Anyone with the time and attention to spare can learn to code in Python and then use this language to write software. Independent developers and hobbyists particularly appreciate that Python is free to use and has an enthusiastic user community ready to help solve their programming problems. This utility makes Python a popular choice for personal creations like free digital media editors, simple mobile applications, websites, independent video games, virtual reality environments, and machine learning experiments. Small business owners can also write enterprise software to manage their operations, including logistics, finance, scheduling, payroll, and recordkeeping.
Python is especially helpful for automating routine computing tasks. Generally, anything that can be done manually with a computer can be controlled by a Python script instead. Users can write scripts for projects as diverse as digital image cleanup, animation, email correspondence, and security monitoring. Scientific researchers find Python useful for controlling experiments, monitoring sensors, and collecting and analyzing data. Python is also a popular choice among machine learning researchers and developers, and many artificial intelligence applications are written in Python.
Coding software is the central activity of tech-based industries that create digital products like video games, mobile apps, websites, cloud-based services, and smart devices. Python is a popular programming language across all these industries, and fluency in Python is required for many technical careers. Most other modern industries also include some role for programmers. For example, fields like finance, healthcare, and engineering use Python for their product development, internal software tools, and customer service applications.
Python classes, especially introductory classes with no prerequisites, may begin by teaching fundamental computing concepts like variables, processes, loops, and algorithms. These beginner-friendly classes are relatively common since Python is often a student’s first coding language. Python is also often included as a tool or skill in training for professions like engineering, financial analysis, and marketing, which is again used to give students their first programming experience. Practice projects in Python can illustrate computing concepts and use those concepts to expand students’ proficiency with Python. For example, object-oriented programming is a major component of Python coding, and students must understand this paradigm to use Python effectively.
Programming Logic
Alongside general computer science, Python students must understand the functional syntax of computer programs, the behaviors that emerge from combinations of smaller structures, and how to create intended behaviors with code. This capacity is required to program in any language, and Python makes its logical structure more transparent than most programming languages, but students still need to study and practice many algorithms before they can successfully create complex, sophisticated programs. Further, Python supports multiple programming paradigms, each differing in its logical structure, so students need to understand what approach best fits different coding tasks.
Python programs can be written in any text editor, but coding is much faster and less complex when using an integrated development environment (IDE), a program that combines a code editor with multiple support applications. IDEs typically include a graphical interface, search navigation, a debugger, and tools for previewing and testing code. Some IDEs are specialized for particular kinds of programming tasks and incorporate additional tools and libraries to support that work. Python classes typically use only one or two IDEs, usually free options like IDLE, PyCharm, or Visual Studio Code. Specialty Python classes may feature an IDE particular to that work such as Jupyter for data science or Adobe Dreamweaver for web development, but these classes will still familiarize students with general IDE use and features.
Among Python’s greatest assets are its ability to easily add and use libraries and the wealth of powerful libraries available to programmers. Libraries are sets of pre-written code that would otherwise have to be manually written into every program, requiring specialized knowledge and far more time and effort. Python already includes a large standard library that students need to learn and use. Python classes also typically teach one or more specific add-on libraries and also teach students how to read and use libraries generally. Some of these libraries support hardware tasks like generating graphic displays, creating user interfaces, and controlling external devices. Some offer toolkits of automated tasks like webpage formatting, sprite animations, or database structures. Yet other libraries provide high-level features like statistical analyses and learning algorithms. A class on a specific use of Python will typically feature the libraries used for that purpose such as NumPy and Pandas for data analysis or OpenCV for video and image processing.
As students progress from simple code to longer, more complex programs, they will need to organize their work: breaking large projects into logical parts, completing each part in order, and assembling those pieces correctly. Introductory Python classes may introduce these organizational methods, but advanced classes and courses for software developers will cover the development process in more detail. Students must learn how to plan a coding project from initial concept through production to final compilation and testing. They may also study and use tools that support the development process by storing component code, tracking changes (and reversing them when necessary), and assembling the final program. These methods and tools are especially necessary for developers working on large-scale industry projects, either as part of a programming team or eventually as a lead or manager.
Coders consider Python easy to learn and use when compared to other programming languages. A short introductory course gives most students enough fluency in Python to write basic programs and prepare to learn more. However, for some students, coding is a challenging subject, requiring specialized vocabulary, unfamiliar tools, and complex thought. A new coder studying Python as their first language might need extra time to get used to the structures and practices of programming. In particular, complex algorithmic structures and coding paradigms like object-oriented programming are less intuitive and may require multiple demonstrations and hours of practice to master. Within Python, students also have to learn many tools, including development environments and libraries. Finally, to program professionally, students need to learn how to apply coding lessons to specific fields like education, finance, or security. Learning enough about Python to write sophisticated software or qualify for an entry-level position as a coder usually takes several bootcamp-level courses or a longer professional training course. Beyond training, true expertise in Python comes from experience: studying sample code, writing longer and more complex programs, and collaborating with other coders.
According to experienced Python programmers, their greatest challenges when learning the language involved applying their coding knowledge to specific fields. Applying Python to industry applications involves several layers of knowledge: understanding a field well enough to encode its concepts and structures, choosing structural elements to create desired behaviors in a program, and solving the unique problems that arise when creating novel features. Professional Python users, especially those involved in software development, also need time and practice to learn how to plan large projects and collaborate alongside others. Python programmers, like other coders, are also often frustrated by the difficulties of debugging code, tracking down not only syntax errors but larger structural errors that cause unintended behaviors.
Those just starting to learn Python cite further difficulties. It takes them considerable time to learn, select between, and correctly use Python’s extensive selection of libraries. They also struggle at first when choosing between multiple possible structures for the same effect, and have difficulty writing code that is not only functional but elegant and efficient. Within Python itself, object-oriented programming is often cited as a difficult topic, requiring good familiarity with several interrelated concepts, the most confusing being metaclasses.
Learning Python’s basic vocabulary, syntax, and structures takes relatively little time. A short class of two to three full-length sessions, around 12–20 hours, is usually sufficient to understand the language’s fundamentals and start writing simple programs. An introduction of this length also prepares students to learn more by showing them how to read more complex code, read and use code libraries, and use software tools like development environments. Students could also reach this same basic level with free lessons but would likely need a longer period, perhaps several weeks, due to their lack of direct guidance and less structured practice. A fuller education in Python, sufficient to write more useful programs, requires students to learn more sophisticated code structures, study several libraries, and apply the language to specific problems, especially the needs of fields like data analysis or game design. Coders can usually reach this intermediate level after three to four weeks’ study in several bootcamp-length full-time courses or a combined training program. With some prior work experience and intensive practice, this amount of study might even be enough to qualify graduates for certain entry-level positions. However, the in-depth, versatile skill that defines a professional Python developer takes additional study and experience. Most estimates recommend two to six months of study to be considered a professional Python coder, and six to twelve months of study and work experience to master Python’s more advanced aspects. True expertise, the ability to use Python flexibly in multiple environments by mastering more libraries, tools, and programming styles, takes even longer, perhaps two years.
While in-person study is the simplest learning format, it does require students to travel to a shared classroom, adding time and money to a course’s costs. Some students have no suitable school within reach. Even students who can easily reach a school have fewer classes to choose from if they limit themselves to in-person instruction. Still, some students prefer in-person study to provide the most direct interaction, allow easy access to computing hardware and software and printed materials, and avoid the difficulties of online interaction, especially for certain learning styles, sensory limitations, or attentional difficulties.
However, other students find online study quite manageable—perhaps even more productive—and appreciate the advantages of this learning format. Online classes can be accessed from any device with a good internet connection, allowing not only greater convenience and comfort but also a greatly expanded range of possible courses. Live online classes are attended via streaming video platforms like Zoom and allow real-time interaction between students and instructors. In contrast, self-paced online classes use pre-recorded videos and texts, losing this direct interaction but also removing the need to attend classes on a fixed schedule. Unfortunately, self-paced classes are less likely to have fully up-to-date information due to their pre-recorded nature. Online study has several other drawbacks: students must manage their own computer hardware and software, may encounter difficulties due to network or hardware errors, and can become distracted when studying from home. Fortunately, most Python classes have minimal hardware demands and use free, widely available software tools. Online instruction is also often supplemented by additional interaction on message boards, chatrooms, and interactive websites, especially for self-paced classes.
As no surprise given the language’s popularity, students can find many free Python classes online in a variety of formats. Video lessons are easy to find on social media sites like YouTube (either from individual creators or teaching organizations like Noble Desktop) or on general teaching websites like Udemy and Coursera. Even more sources offer tutorial texts, including the official tutorial on Python.org, text tutorials on Google and RealPython, and online textbooks like Automate the Boring Stuff With Python. Unfortunately, video and text lessons cannot interact with students, leaving them unable to address problems and uncertain about their progress.
Other free lessons, interactive web tutorials, are somewhat better in this regard. These automated teaching sites combine text, video, and graphics—allowing students to write and test sample code. Some of these interactive sites, like pychallenger and LearnPython.org, are created by Python enthusiasts. Other interactive lessons come from coding schools like Codecademy and Datacamp and teaching sites like Preply.
However, no automated lesson can match the direct interaction provided by a live instructor. Students will always progress more slowly with free resources compared to paid classes. Free resources are also limited in several other respects. On most teaching websites, free lessons are only introductions meant to prepare students for more advanced coursework and encourage them to purchase full courses. Free classes also offer no guarantee of accuracy, may vary in instructional quality, and are only as current as their creation date. Ultimately, Python students cannot rely entirely on free resources. They will need formal coursework to ensure that their skills are current and complete enough for professional employment.
While Python has broad utility across programming tasks and platforms, it is not the only programming language used in professional settings and may not always be the best language for a given project. To add more tools and appeal to more employers, students learning Python should also learn one or more additional programming languages, particularly other popular high-level, general-purpose languages like Java, Ruby, or C++; scripting languages like JavaScript or PHP; or special-purpose languages like SQL, HTML, or CSS.
Several related skills are useful for all coding-related professions such as general computer science, data science, database management, and software development. Along with software development, students should also study User Interface (UI) and User Experience (UX) design and learn different environments like mobile and cloud-based applications. Students can explore these subjects independently of Python or seek classes that teach Python’s uses within these fields. Other technical fields that often use Python and are good complements to study alongside it include web development, data analytics, machine learning, and generative AI. Finally, students seeking to use their Python coding skills within a particular industry should seek subject-specific education and classes about technology within those areas such as finance (including FinTech), engineering, project management, research science, or cybersecurity.
Any industry that uses software can use programs written with Python. Python coding is a valuable skill not only in technology-based industries but also manufacturing and service businesses, healthcare, education, and government. This article will concentrate on the industries that use Python the most, employ the most Python programmers, and most often provide the digital tools used by other industries. Software development, for example, covers not only standalone applications but also the code used to control smart devices and systems used by other digital creators. San Diego is home to several large corporations that provide such software solutions, including Qualcomm, Teradata, and ServiceNow. Web development creates websites and web-based applications that deliver services online. Data science and data analytics, fields often taught using Python, are useful in any industry that needs to manage large datasets. One industry in particular, finance, uses Python widely for its data management, web development, and software tools; supports multiple industries; and employs many Python coders through the intersecting field of FinTech.
The software development industry includes companies and independent developers who create applications for any platform. These applications support personal activities like scheduling, health tracking, or gaming; provide technical solutions to business challenges like organization, communications, or finance; support industries’ internal needs as enterprise software; or control external devices like sensors or robotics. While not the only language used for these purposes, Python is a popular choice for software creation. Most software developers in San Diego, including WebcentriQ, Codebay, SynergyTop, and EffectiveSoft, provide custom software solutions for business clients. Python programmers in San Diego can also work for software providers focused on specific industries such as Drata (cybersecurity and compliance) and Altium (electronics).
The central task of web development is creating websites, which includes webpage design and content, website structure, and web server programming. Python contributes to each of these components, creating animated and interactive web components, web-based applications, custom-generated webpages, AI features like smart search and chat systems, and server code. Thus, Python programmers can work in all parts of web development, although most web developers will need to know several other languages and systems beyond Python. Multiple web development companies of varying sizes are based in San Diego, including Alliance Innovations, Achieve internet, Frogmouth Digital, and Dextersol. Most local custom software development companies such as Asymm also offer web development. Some marketing companies, especially those focusing on digital marketing like WISE Digital Partners, also include web development in their offerings.
Data analytics describes multiple related industries that gather, analyze, and explain information for various other fields such as finance, healthcare, transportation, or scientific research. Python’s value for data analysis is well established, and the language is often taught specifically for this purpose. Data analysts can quickly build custom tools in Python, automating tasks and expanding the scope of their investigations. Python has also found wide use in machine learning systems that improve data analytics by identifying unexpected patterns and refining analyses without exterior guidance. In San Diego, data analytics employers include companies like Modern Analytics and OpAnalytx LLC that create custom data systems for various business clients plus specialized data analysis services like Measurabl (commercial real estate), Grasp Technologies, Inc. (travel and expense), and PrediqTank (product design).
The finance industry includes several sectors with common roots: banking, lending, investment, commodity and stock trading, currency exchange, valuation, and economic policy. Financial technologies, or FinTech, support these fields by automating and protecting financial services, collecting and analyzing financial information, and generating explanations and advice. As in other technology fields, Python plays a significant part in FinTech, being used to code applications, data systems, and other software tools for financial institutions and industries that support finance. FinTech companies in San Diego include Axos Bank (commercial banking), ATM Together (ATM services), Applied Data Science (financial analysis), Reliant Funding (small business financing), and Guild Mortgage (mortgage lending). San Diego also hosts regional offices for several large financial service providers that incorporate FinTech, including Wells Fargo, CSC, and Intuit.
Both Software Developers and Software Engineers create applications for desktop, mobile, web, and cloud use. However, a Software Developer may be an independent creator, while a Software Engineer usually works as part of a team and has more varied training and duties such as software design, project management, testing, and deployment. Fluency with programming languages like Python is a central skill for both Software Developers and Software Engineers. Software Engineers might also need to know Python to create quick, flexible code for business needs like enterprise software, hardware automation, or data servers; in this role, a Software Engineer might also be called a Systems Engineer. Software Engineers in San Diego earn an average of $111,000 per year across all positions.
Python programmers can fill multiple roles as Web Developers: coding web components, applications, scripts, and servers. A Back-end Developer uses Python most frequently, to write web server code for data management, custom page creation, and other server-side applications. However, Front-End Developers also use Python, particularly as an alternative to JavaScript when adding interactive and animated components to webpages. Full Stack Developers, who handle all aspects of web development, learn Python as an option for multiple tasks. Across all specialties and experience levels, the average income for a Web Developer in San Diego is around $116,000 per year. A Front-end Developer in San Diego averages about $115,000 per year. Due to their greater technical expertise, a Back-end Developer in San Diego earns almost $158,000 per year, while a Full Stack Developer in San Diego averages around $137,000 annually.
Programmers who specialize in Python may be called Python Developers regardless of their employer. They may work as part of a team to provide coding expertise to a larger project or may write custom code for their employer’s needs. Python Developers can also be experts who improve and expand Python itself, often by creating new Python libraries or development tools. Given their varied range of possible duties, the average salary for Python Developers in San Diego is a substantial $142,000 per year.
Data Scientists are experts in data, studying its collection, structure, storage, analysis, and visualization. As employees, Data Scientists aim to better understand, manage, and use information to guide an organization’s decision-making. They are usually employed in industries involving particularly large datasets such as finance, sales, healthcare, engineering, and sciences. Data Analysts perform similar duties but are more focused on identifying and representing the patterns in data. Data Analysts work in a wide range of industries and are often described by their specialties: Financial Analysts, Marketing Analysts, and Insurance Analysts, for example. Data Engineers, by comparison, are more concerned with large-scale data storage and retrieval, tasked with building systems for data management. Data Engineers may work in concert with Data Analysts or may build analytic systems as part of their work. Python is frequently used in all these jobs, particularly for automating repeated and complex analyses but also for data collection, cleaning, and visualization programs. Entry-level Data Scientists in San Diego can earn an average of $94,000 per year, increasing to an average of $125,000 per year for Data Scientists in San Diego at all levels of experience. Data Analysts in San Diego earn an average of $83,000 per year, and Data Engineers in San Diego earn an average of $126,000 per year.
Combining data science with artificial intelligence approaches, Machine Learning Engineers create systems capable of collecting and examining data, identifying its patterns, and then using their discoveries to improve future processes, guide behavior, and answer questions. Machine learning applications are playing an increasing role in many industries such as finance, healthcare, and education. Python has long been a favored tool to code machine learning algorithms and build their interfaces. Machine Learning Engineers in San Diego earn an average of $138,000 per year, although this figure includes senior experts and scholars with graduate degrees. An estimated starting salary for entry-level Machine Learning Engineers in San Diego is around $74,000 annually.
In partnership with Fullstack Academy, the University of San Diego offers a live online AI & ||CPN633||, a 26-week, part-time program teaching AI programming in Python. Although students are encouraged to have prior experience with Python, the course starts with a “refresher” programming unit before progressing to specific applications. Subsequent units cover data analysis, machine learning, deep learning, natural language processing, and generative AI. Students will create example projects for each subject before finishing the course with a capstone project applying these techniques to a real-world problem. The course also covers a wide range of Python libraries including Pandas, Matplotlib, TensorFlow, and Keras; other Python tools like PyTorch, Jupyter, and NLTK; and AI systems like ChatGPT, OpenAI, and DALL·E. Graduates receive a certificate of completion and can opt into a guidance program for career coaching and support for up to one year.
Data Scientists are experts in data, studying its collection, structure, storage, analysis, and display. As employees, Data Scientists seek to better understand, manage, and use information to guide an organization’s decision-making. They are usually employed in industries involving especially large datasets such as finance, sales, healthcare, engineering, and sciences. Data Analysts perform similar duties but focus more specifically on identifying and representing the patterns in data. Data Analysts work in a wide range of industries and are often described by their specialty: Financial Analysts, Marketing Analysts, and Insurance Analysts, for example. Data Engineers, by comparison, are more concerned with large-scale data storage and retrieval, tasked with building systems for data management. Data Engineers may work in concert with Data Analysts or may also build analytic systems as part of their work. Python is frequently used in all these jobs, particularly for automating repeated and complex analyses but also for data collection, cleanup, and visualization programs. Entry-level Data Scientists in San Diego can earn an average of $94,000 per year, increasing to an average of $125,000 per year for Data Scientists in San Diego at all levels of experience. Data Analysts in San Diego earn less on average, $83,000 per year, and Data Engineers in San Diego earn more, $126,000 per year, on average.
Combining data science with artificial intelligence approaches, Machine Learning Engineers create systems capable of collecting and examining data, identifying its patterns, and then using their discoveries to improve later processes, guide behavior, and answer questions. Machine learning applications are playing an increasing role in many industries such as finance, healthcare, and education. Python has long been a favored tool to code machine learning algorithms and build their interfaces. Machine Learning Engineers in San Diego earn an average of $138,000 per year, but this amount includes senior experts and scholars with graduate degrees. A more likely salary for entry-level Machine Learning Engineers in San Diego is around $74,000 annually.
In partnership with Fullstack Academy, the University of San Diego offers a live online AI & ||CPN633||, a 26-week, part-time program teaching AI programming in Python. Although students are encouraged to have prior experience with Python, the course starts with a “refresher” programming unit before progressing to specific applications. Subsequent units cover data analysis, machine learning, deep learning, natural language processing, and generative AI. Students will create example projects for each subject before finishing the course with a capstone project applying these techniques to a real-world problem. The course also covers a wide range of Python libraries including Pandas, Matplotlib, TensorFlow, and Keras; other Python tools like PyTorch, Jupyter, and NLTK; and AI systems like ChatGPT, OpenAI, and DALL·E. Graduates receive a certificate of completion and can opt into a guidance program for career coaching and support for up to one year.
ONLC Training Centers presents a wealth of technical training both live online and at facilities across the United States, including San Diego. Their Python classes cover three levels of instruction: Python Programming Level 1: Introduction, Python Programming Level 2: Advanced Programming Techniques, and Python Programming Level 3: Data Analysis Using Python. There are two versions of their introductory course, one for experienced programmers and one for novices.
General Assembly hosts coding classes both live online and in-person at classroom sites in multiple U.S. cities including San Diego. Their Python Programming Short Course offers an intensive introduction to coding with Python that takes only one week on a full-time schedule or ten weeks of part-time evening classes. This course teaches the fundamentals of Python programming with a focus on object-oriented programming, then addresses several applications of Python, including data science and web development. Students complete a full, real-world project in one of these application areas, suitable for their professional portfolio, and receive a certificate upon completion. General Assembly graduates also receive discounts on additional courses and access to exclusive networking events and career workshops.
AcademyX trains a wide range of technical skills, both live online and in-person in San Diego. Their three-day Python Training class covers basic Python programming in significant detail but expects some previous computer science and programming experience. After addressing Python fundamentals like development environments, interfaces, and functions, the class teaches multiple algorithmic elements and structures, including object-oriented programming. Throughout the class, instructors familiarize students with several Python libraries and demonstrate concepts through practical programming exercises.
Noble Desktop's live online courses include short classes, intensive bootcamp courses, and professional training programs. For Python, Noble Desktop offers a Python Developer Certificate program, an immersive ||CPN774||, and several targeted bootcamps addressing Python’s applications in specific fields. The Python Developer Certificate course is a complete professional training program, lasting about three weeks on a full-time schedule, incorporating multiple bootcamp-length courses. The first of these units is equivalent to the ||CPN774||, a one-week course covering the fundamental elements of Python, computer science basics, programming structure and algorithms, object-oriented programming, and the application development process. Both this bootcamp and the certificate program require no prior programming knowledge. The second standard unit in the certificate program is another bootcamp course, Python Web Development with Django, which teaches students how to code for the web using Python’s Django web development framework. Finally, students may choose two of four possible electives to complete their certificate program, each being another one-week bootcamp focused on an applied use of Python: Python for Data Science, Python for AI: Create AI Apps with Flask & OpenAI, Python Data Visualization & Interactive Dashboards, or Python Machine Learning. In addition to live class sessions, students can access video recordings after each class, receive supplemental study materials, and schedule four 1-on-1 mentoring sessions to address difficult topics or improve their career preparations. The course’s applied projects are also suitable for a starting portfolio, and the certificate awarded upon completion is endorsed by the New York State Department of Education.
Noble Desktop offers both live online and onsite Python training for businesses and other organizations, including general coding classes as well as applications like data analysis and machine learning. Companies can book any of Noble Desktop’s existing Python courses or design custom courses for their unique needs. Course length and scheduling can also be arranged to fit a client’s availability. As another option, companies can purchase vouchers for existing live online classes, with a discount for bulk purchases, and distribute these to their employees, who can then schedule sessions as available.
Contact corporate@nobledesktop.com to purchase course vouchers or to arrange a free consultation where you can ask questions, design a custom course, and schedule classes.