Learn Python for data science, web development, machine learning, or FinTech. Choose the bootcamp that meets your learning goals.
Master the tools to become a data scientist: Python, SQL, automation, and machine learning. Learn Python programming fundamentals and analyze data with Pandas, NumPy, and Matplotlib, and query databases with SQL. Use machine learning to apply regressions and other statistical analysis to create predictive models.
Learn the essential skills and tools to become a Python Developer. This beginner-friendly course teaches students Python for software development with Django and Django REST in addition to other developer tools such as Git and SQL.
Pick up Python fundamentals and quickly transition into analyzing real-world datasets. You will learn to how to clean and combine data, as well as generate useful statistics and visualizations. The final sessions will be focused on using linear regression to extrapolate from data and make predictions.
Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this immersive FinTech course.
Take a step beyond normal programming, into using algorithms that can independently learn patterns and make decisions. Machine learning skills are in high demand, as these algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.
Launch your career as a data scientist or analyst. Our modular approach makes learning more affordable and easy to build on. You can start with our introductory Python and SQL courses and earn a certificate over time. You only take the classes you need to increase your earning potential, gain in-demand skills, and embark on a career in a new line of work!
“Noble Desktop is far and away the most efficient way of gaining computer graphics skills. They give real-world exercises to work on, teach best practices, and inspire many an ‘aha!’ moment. Highly recommended.”
Joanne Hu
Since 1990 we have perfected the craft of teaching. If students get hung up on an issue, we tweak the class to make it better. We’re the longest running independent training center for code and design in NYC.
From our hands-on training style to world-class instructors and proprietary curriculum, we deliver a learning experience our alumni can be proud of.
You can attend training live online at the scheduled time the course is offered (New York, Eastern Time) through screen-sharing software Zoom (free for you).
Python code is very similar to English and can, therefore, be learned quickly. We are therefore witnessing an increase in start-up technology companies using Python as their preferred language.
Unlike Java or C++, Python’s syntax is very simple which allows programmers to focus on the product they are trying to build and not the syntax they need to follow. All this and more leads to products in Python being launched faster and smarter. Programmers can launch minimal viable products into the market for customer testing. The result is the creation of more technology products that have a proven and tested market. This ultimately prompts an increase in the flow of venture capital money into products built on Python.
A language is only as strong and as useful as the number of people who are using it. Python has just surpassed 35 million downloads per year and an estimated 5 million programmers worldwide are using Python as their preferred language. The massive adoption of Python by programmers is a testament to its strength and speed. This also creates a highly valuable social network of Python developers. From searching for debugging answers on Stack Overflow to finding a job in a new city, the Python social network reigns supreme.
With Python, programmers can build software for NASA, create data science models for Fortune 500 companies, and scrape data from websites and academic journals. In other words, there is an endlessly diverse group of people who use Python for very different reasons: the traditional programmers use it to build software and foster technological innovation, the data scientists will use it to build models to see which marketing strategy is most effective, and the academics use it to retrieve data autonomously using Python web scraping extension such as Beautiful Soup.
Instagram, Spotify, Amazon, Facebook are all examples of companies who currently use Python as their coding language of choice.
Instagram uses Python because it fits with their company philosophy to “do the simple thing first.” Instagram uses Django web framework which is written in Python. Another reason engineers at Instagram opted to use Python is because it is simple and effective which allows them to launch new features with little downtime.
Spotify uses Python mostly for data analysis and backend services, but programmers at Spotify said, “Python has a habit of turning up in other random places, as most of our developers are happy programming in it.” Amazon and Facebook also use Python for features including recommended friends and products.
Python's frameworks and environments all it to be used for a variety of tasks.
In this article, we'll discuss two of the most popular tools for data analytics, Excel and Python. We'll walk through how each of these tools fares across a variety of dimensions to help you with your next steps in professional development and learning.
Due to the unfortunate spread of the Coronavirus, most of us are left working remotely, social distancing, thus resulting in a lot of free time with very little to do. In this article, I want to explain how you can use your free time in the most efficient manner by learning a new skill that can progress your career to new lengths.
Data Science is truly comprised of two main topics: math and programming. However, one does not need to be a computer scientist or mathematician, one does not even need to have taken algebra or a basic programming class to start.
Any way you measure it, Python is booming in popularity. According to the most recent survey by Stack Overflow, Python is the “most wanted” programming languag...
In this series of posts, we'll cover various applications of statistics in Python. This first post talks about calculating the mean using Python.
In this tutorial, we'll cover the cental tendency statistic, the median. The median is the middle value in a dataset when ordered from largest to smallest or smallest to largest.
For the last few years, Python has been the most popular coding language learned at educational institutes. Statistics reveal that Python has become popular in a number of other settings as well as reflected by the following: ...
While Python is designed to be beginner-friendly, there are some common pitfalls that hold newcomers back. Experienced Python developers know how to avoid traps like these, but for beginners they can mean hours of frustration. If you’ve t...
Excel spreadsheets have been the standard in the business world, allowing people to leverage spreadsheets for everything from accounting to managing schedules. As one of the world’s most popular software programs, Excel is used in all fac...
In recent years, Python has exploded to become one of the fastest-growing languages. Traditional object-oriented programming languages have many rigid rules, and Python often breaks the convention of these languages, offering simplicity to coun...
Since established almost 30 years ago, Python has become one of the most popular programming languages. Current tech giants like Instagram and Spotify use Python to create smooth functioning and better
The internet is full of noise about what counts as “real” data science. This genre is generally a waste of time, but for beginners it can be particularly pernicious. If you’re already feeling like a fish out of water...
Inevitably, everyone who has any interest in Data Science faces a wall, and that wall’s name is Mathematics. As much as we would love to avoid the intricacies of math in data science, when a stakeholder asks, “Can you explain the an...
Before taking an introductory class to Python, you may be tempted to know what you can really do with Python. Many students who take any coding class essentially finish the course asking themselves what’s next. It’s good to know tha...
The standard deviation or variance, the standard deviation is just the variance square rooted or raised to ½. The standard deviation is more commonly used, and it is a measure of the dispersion of the data. This is very different than the mean, median which gives us the “middle” of our data, also known as the average.
It has never been easier to learn Python from the comfort of your home, and in this article, we’ll go through various platforms and courses for you to master the skills you need in Python for data science.
Data science is the discipline of using data and statistics to make predictions. In many industries, data science is used to make strategic decisions and solve challenging analytical problems. Data science can be used to predict stock prices, create revenue projections, or create any sort of predictive model based on data.
Python is a general-purpose programming language that can be used to develop applications, analyze and visualize data, create machine learning algorithms, automate tasks, and much more. Python is open-source and offers a ton of support and packages to make data science a lot simpler. Python is the most commonly used programming language for data science and Python was also voted the "most wanted" programming language by a survey from Stack Overflow.
Machine learning is the process of teaching computers to make decisions based on data. Machine learning is a subset of artificial intelligence and creates programs to automate analysis and learn from data for predictive modeling. Using Python and machine learning, you can train a computer to understand a dataset and predict future outcomes based on it.
NumPy is a powerful linear algebra package for Python. It is primarily used for scientific computing. Many other libraries (Pandas, Matplotlib, and Sci-Kit Learn, for example) are dependent on NumPy.
Pandas is an open-source library that provides high-performing, user-friendly data analysis tools for Python. Pandas stands for "Python Data Analysis Library" and it is a key building block for data science. Pandas allows you to take data and convert it to a Python object called a dataframe. Pandas makes importing and working with data from csv's or SQL databases easy.
Matplotlib is a popular library that allows data scientists to visualize data. Data visualization is a crucial step in making data accessible. It allows you to identify outliers and patterns quickly while making data interpretation easier overall. The package is completely free and easy to install so you can start creating histograms, pie charts, line graphs, and more with Python.
Scikit-learn is a machine learning library for Python that works with NumPy arrays and focuses on modeling data, not operating on it (NumPy and Pandas handle this).
