Learn today’s most powerful and versatile programming language for data science, machine learning, automation, and more. Noble Desktop offers in-person and live online Python courses and certificates, as well as best-in-class corporate training programs to upskill your team.
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.
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.
Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this immersive FinTech course.
Learn the skills to become a Software Engineer or Full Stack Developer in this immersive course. Master the front-end using HTML, CSS, JavaScript, and React; build the back-end with Python, Django, and Node.js
Learn to code in this beginner Python programming course. This hands-on course is designed to give you a comprehensive understanding of Python programming fundamentals and get you coding on your own. You'll learn how to professionally structure your programs and solve real-world challenges using Object-Oriented Programming.
Get started learning Python in our introductory courses (Python in a Day and Intro to Python Programming), or learn advanced uses of Python such as Machine Learning and Automation (see course prerequisites).
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.
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.
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.
Learn Python to extract data from websites. Along the way, you’ll learn how to write loops so that your web scraping code can process a large number of pages.
Work on projects proven to boost retention
Refined over many cohorts for an optimal learning experience
Experienced educators who are driven to help you succeed
Refresh the materials and gain additional practice
Learn the concepts and skills covered in this program or your tuition is on us. See details and terms & conditions.
The teaching method at Noble Desktop is perfect and the classes provide you with infinite knowledge that makes you eager to take everything they offer. I love Noble!
—Ivonne Ackerman
Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this immersive FinTech course.
Upskill and take your finance skills to the next level with this Python for Finance class. You'll learn to analyze large amounts of financial data using Python, create visualizations, and start using statistics for predictive modeling.
This course will begin with advanced Python and statistic topics such as object-orientated programming and regression models. Students will learn how to apply these concepts using real-world financial data by building a predictive returns model using regression. We will then introduce students to important financial statements and ratios, and how to pull data from these statements and compute these important financial ratios using Python.
As people get busier and busier, we want to automate as much as we can day to day including our investments. Using Python, this class will teach you how to build robust and automated trading strategies without needing to spend hours a day overseeing their portfolio. Students learn how to connect their Python scripts with an online trading brokerage, how to place and query stock orders, and are introduced to Machine learning as it applies to properly value an Option.
Train your team in Python’s versatile applications in financial services, technology, health care, and more. Purchase group class vouchers at a discount for our regularly-scheduled group classes in New York, or create a custom training program at your offices.
Request more info:
Analyze, visualize, and uncover trends in your data.
Create machine learning algorithms and models to predict outcomes.
Create dynamic charts and graphs from complex data.
Write scripts to automate everyday tasks.
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!
Python is a programming language that uses the English language, making it very easy to read and understand. It is now the most used language in introductory courses in universities across the globe. However, there is no trade-off between power and ease; Python is one of the most versatile and powerful languages.
If you still have doubts about whether should learn Python, a motivating factor may be salary. Python developers are the highest paid developers in the market, and there’s a growing need for Python developers in the industry. According to ZipRecruiter, the average salary for a Python developer in the United States is over $110,000.
In our Python classes, students will dive into coding within the first 10 minutes of class. First, the instructor will demonstrate an exercise, then you’ll practice on projects which include college choice algorithm, temperature converter, stock data analysis, back-end web building, and more.
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).
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.
Learning how to manipulate your dataset using Pandas is a critical skill and this tutorial will cover some essential Pandas operations
Learn what machine learning is, the various types of machine learning models, and walk through building a machine learning model in Python with this step-by-step guide.
Learn to wrangle data with Python in this tutorial guide. We'll walk you through step-by-step to wrangle a Jeopardy dataset.
Learn data visualization in Python using Matplotlib and Seaborn in this data visualization guide.
In this guide, we'll walk through the 5 phases of your data science journey with Python from the basics of Python to building machine learning algorithms.
Python is the most valuable skill you can have when it comes to data-focused careers, but it’s also incredibly versatile for Web Developers and Software Engineers. Learning Python is the best place to start for some of the most lucrative and fulfilling careers. If you’re a complete beginner, know some data science, or want to add a back end language to your stack, then Python is for you.
One of the biggest advantages of using Python for both data professionals and developers is its massive collection of libraries and frameworks, collectively known as packages. Python packages enable more efficient coding, building secure and complex applications, and make Python versatile enough to be used for everything from building social media platforms to predicting the stock market.
Data Scientists and Data Analysts use Python and its packages to find useful insights in big data. Python is the most popular programming language among data professionals. If you want to work in data science, you should take a Data Science Bootcamp that includes Python.
A data science bootcamp will teach you the fundamentals of data science and Python, how to create automations, and how to write complex queries. You’ll get hands-on training from industry experts so you won’t have to rely on forums to answer your questions. Some bootcamps even offer payment plans and job assistance so you can maximize your learning while you prepare for your new career.
If you want to change careers, a comprehensive bootcamp like this is what you need. But if you just want to upskill, you can take a Python course that is specifically designed for data science. There are Python courses specific for finance professionals and web developers too.
Python is quickly becoming the most popular language in back end web development. Web developers are using Python’s most popular frameworks like Django for creating large scale platforms for business websites, news, and social media.
You can take a Python web development course to add Python and Django to your stack. This course is perfect for someone who already knows front end development or wants to focus only on back end development.
Along with learning Python, a Python course or bootcamp will teach you how to learn properly. You’ll find out where developers and data scientists go to get answers to their questions and you’ll have direct access to someone who can answer your questions right away instead of having to scour forums for hours.
A live online or in-person Python course is the most efficient route to mastering Python. While Python is one of the most beginner-friendly languages, its application to different professions varies. Data, web, and finance professionals each use Python differently, so it’s important to learn what you need to know from industry experts who have teaching experience.
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).
