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Python Bootcamps in NYC or Live Online

Become a Python Developer, Software Engineer, or Data Scientist by learning the most in-demand and versatile programming language. Learn Python with hands-on bootcamps from seasoned experts.

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Featured Python Bootcamps

Learn Python for data science, web development, machine learning, or FinTech. Choose the bootcamp that meets your learning goals.

  • Data Science Certificate

    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.

    Read more
    • $3,495
    • 84 hours
    • Weekdays, weeknights, or weekends
    • Open to beginners
    • Financing available
    Course information See upcoming dates
  • Python Developer Certificate

    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. 

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    • $4,995
    • 138 hours
    • Weekdays or weeknights
    • Open to beginners
    • Financing available
    Course information See upcoming dates
  • Python for Data Science Bootcamp

    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.

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    • $1,495
    • 30 hours
    • Weekdays, weeknights, or weekends
    • Open to beginners
    Course information See upcoming dates
  • FinTech Bootcamp

    Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this immersive FinTech course.

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    • $2,995
    • 60 hours
    • Weekdays or weeknights
    • Open to beginners
    • Financing available
    Course information See upcoming dates
  • Python Machine Learning Bootcamp

    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.

    Read more
    • $1,895
    • 30 hours
    • Weekdays, weeknights, or weekends
    • Prerequisites required
    Course information See upcoming dates
See All Python Courses
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Why Learn Python at Noble

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!

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    30 Years of Experience

    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.

Highly Reviewed by Our Alumni

From our hands-on training style to world-class instructors and proprietary curriculum, we deliver a learning experience our alumni can be proud of.

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27 Students Rated Our Python Bootcamps 5 Stars

This is a great crash course for anyone who's worked with basic Python files and wants to know what they're reading, and to start writing scripts of their own.

Gabe Scelta, United Nations

Rob was absolutely fantastic. I have done other training sessions (not with Noble) and he has been by far the best. He was entertaining, patient, and had a true mastery of the subject matter. A great teacher!

David

Excellent class. By the end of the day, we understood how to scrape a website for information and send results by email, text, or saved to the local computer. Highly useful.

Rahmaan Mwongozi

The Python for Data Science course was a mix of theory and hands on application. Probably the best value class out there.

Lorela Blaka

Was a great intro for someone looking for a Python introduction with clear use cases.

Kate Binder, FCBNY

Rob is very helpful and always ready to answer questions. He explained the content very well. The course is a great introduction to Python and programming!

Leyla Beck

Great class for someone who wants to get out of their comfort zone and challenge themselves to pick up a new skill

Tanuja Pulakhandam

Great introduction class! Excellent learning environment and teacher.

Manvi

I learned so much in just a span of two weeks. Very awesome experience.

Isaac Amar, NA

This class teaches real world uses for computer science languages

Soham Bafana

Great Class. I would highly recommend this class.

April E Cooke, JPMorgan Chase

Noble Desktop is definitely one of the best courses I've taken. Class was so informative and interactive, I will definitely come back.

Joyce Li

Course content was more than adequate and very applicable to Data Science and analysis. Learned a lot of takeaways that I will apply at work.

Melissa Manganaan

A complex subject, clearly broken out into simple steps.

James Siwicki

Having no prior knowledge or experience in computer/data science, I feel as though this course prepared me well in order to use and apply Python through a thorough, yet understandable curriculum.

Gabriel Kerstein

I learned so much in only one week. I highly recommend this class.

Arthur

Great way to start learning Python!

Nolan Young

Enrolling in the Python for Data Science course was the best decision I could have made for myself and my advancing career. I'm feeling confident enough to move beyond my traditional role using what I've learned at Noble Desktop to create a portfolio of my knowledge.

Aja Walton

Really great way to jump into a complicated topic.

Nolan Young

I highly recommend this class to anyone that is looking for an intro to machine learning.

Marcelo Zampietro

Rob is extremely knowledgeable and made learning a complicated subject matter more accessible

Jason Alter, Tapestry Inc.

"Noble is the best. Period."

Andrew Ortiz, JP Morgan Chase

I feel very comfortable with the Python syntax after taking this course...I had zero previous experience.

Philip E Camp, FEMA

Highly recommended course and the instructor is detail oriented and knowledgeable!

Yamini Y, American Express

Professional, supportive, engaging and down to earth instructors. Noble Desktop is always there for you.

Mai Amouyal

If you're truly starting with just about zero programming experience this particular Bootcamp will catapult you into intermediate within a few short weeks. Before the class was over I was already able to complete projects on Coursera's project series with relative ease. To be able to cover loops, numpy, pandas, and even data visualization in a short time is very impressive.

Tyler irwin, new york life

EXCELLENT! I was surprised by just how 'inspiring' Patrick turned out to be. He made the material interesting and provided a model of how to establish a good mind-frame necessary to mastery. I loved it. Felt like I got personal direction as well as technical skill.

Sean Kerr

Learn More About Python

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. Initially released in 1991 by Guido van Rossum, Python is open-source and emphasizes readable and efficient code, while being flexible and scalable.

Through various frameworks and libraries, Python has extensive applications in areas such as data science, software development, machine learning, and scripting. Due to its flexibility and efficiency, Python is the “most wanted” programming language for the second year running, according to the most recent survey by Stack Overflow.

Python for Data Science

Python is the most commonly used programming language in data science—with almost 70% of data scientists reporting that they use it. It has surpassed R for the number one spot and has maintained this position due to its ease of use, powerful libraries and packages, clear and user-friendly documentation, and abundant community support.

Python is easier to read and write than most other general-purpose languages, especially for analytical computing and quantitative data analysis. Data scientists are already handling complex analysis of data, so they don’t need their programming language to be complicated, too. Python is known for its simple syntax and ease of use—even for beginners.

Python is open-sourced and has numerous libraries and packages available for data science. While some other languages (like Ruby) have clean and simple syntaxes, they don’t offer the same variety of scientific computing and machine learning libraries as Python.

There are thousands of libraries in the Python Package Index. Some of the most useful libraries and packages are Pandas, NumPy, Matplotlib, and Sci-Kit Learn.

NumPy

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. NumPy has extensive documentation and can be installed quickly and easily.

NumPy works with multi-dimensional arrays in Python. Lists can be converted into arrays, random arrays can be created, and numerous operations can be performed on these arrays. This is a crucial feature because operations (addition, subtraction, multiplication, and division, for example) cannot be performed on standalone Python lists, but they can easily be performed on NumPy arrays. Since data scientists often need to perform operations on data sets, NumPy is an invaluable tool.

NumPy allows you to find the min, max, standard deviation, and variance on an array. It allows you to combine different arrays to form a single array.

Overall, NumPy arrays are faster, easier to use, and use less memory than Python lists. When working with massive data sets, convenience and ease of use are two big selling points.

Because arbitrary data types can be defined with NumPy, the package is able to connect with a variety of different databases. This adds to its versatility and makes it an important component of any data scientist’s technical repertoire.

Pandas

Pandas is an open-source library that provides high-performing, user-friendly data analysis tools for Python. It is one of the most popular libraries and, as such, has excellent documentation.

Pandas essentially takes data (from a CSV file or a SQL database, for example) and creates a Python object called a data frame. A data frame organizes data in a format that resembles a table, so it is easy to read, easy to analyze, and easy to work with.

Pandas is dependent on NumPy, and can optionally be used with Matplotlib for data plotting and visualization. Because of this, it can be installed on its own, or it can be installed through a package like Anacondas, which will install all required dependencies.

Pandas is usually used in one of three ways:

  • To convert a list, dictionary, or array into a data frame
  • To open a local CSV file or a related data file
  • To open a remote file (CSV, JSON, SQL database, etc.)

After opening the file that you’d like to work with, you can perform a number of different commands to analyze the data. You can perform statistical analysis (mean, median, standard deviation, and so on), you can retrieve specific data points, and you can file, sort, or group data as you see fit.

Another important feature is the ability to clean data by checking for null values within the data set. It is difficult to work with data that has not been cleaned; unintentional null values within data sets can skew your results or make the results difficult to analyze. Pandas addresses this concern by identifying pieces of data that might be missing, incomplete, or otherwise incorrect so that you can get the most accurate results from your analysis.

Matplotlib

Matplotlib is another 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. Research shows that people in general are very receptive to visual representations of data, making Matplotlib an invaluable resource in data science.

Matplotlib is free, easy to install, and has robust features. Data can be rendered as a histogram, a pie chart, a line graph, a box plot, and so on. There are enough features to satisfy advanced users, but even entry-level users can create powerful visualizations of data.

Consider an enormous data set that encompasses countless data points over a long period of time. While this data can be displayed in an array or in another numerical format, it would take awhile to read and analyze. There is a potential for human error when manually reading and interpreting massive lists of data. Naturally, human error is something that data scientists try to avoid.

Matplotlib allows you to choose the specific data that you’d like to work with and arrange it in any visual format that you can imagine. Data can be rendered and displayed in almost any format with a few quick commands. Because Matplotlib is so easy to use and works seamlessly with other Python libraries and packages, it is a top choice for data scientists who use Python.

Sci-Kit Learn

Many data scientists begin their analysis and evaluation of data with Pandas before moving over to Sci-Kit Learn for machine learning. Sci-Kit 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).

Some modeling options include clustering, data sets, parameter tuning, and cross-validation. Sci-Kit Learn comes with standard data sets (for classification and regression of data, for example). Sci-Kit Learn is used in conjunction with stats and linear regression to make predictions based on data sets.

Other Libraries and Packages

These libraries and packages, among others, are one of the main reasons that Python is so popular in data science. The options to import, manipulate, operate on, clean up, visualize, and model data are unmatched by any other programming language’s libraries.

In our Python for Data Science Bootcamp, we cover Python in depth, and we hone in on NumPy, Pandas, Matplotlib, and Sci-Kit Learn to help you make the most of your data.

Python for Machine Learning

One of the most powerful tools of Python is its machine learning capabilities. Machine learning is a subsection of artificial intelligence that creates programs to automate data analysis and learn from the data. This is a remarkably powerful tool because as data continues to grow and become more complex, machine learning algorithms will be able to produce full-scale automated models that are efficient and reliable.

Python is the number one language used in machine learning projects due to its simplicity, wide-usage, and open source packages, like scikit-learn. Scikit-learn is a machine learning library built for Python that allows programmers to cluster data and run various forms of modeling algorithms on the data.

Sci-kit learn has much to offer when it comes to machine learning due to its simplicity and flexibility. In as few as two-lines of code, an analyst can run a decision tree model on a massive data frame in seconds!

However, the main reason scikit-learn is the gold standard for machine learning is that it’s built on top of several common Python libraries, which allows programmers to input Numpy arrays and Pandas data frames into scikit-learn. Additionally, scikit-learn provides programmers with a full suite of data modeling tools such as Regression, Decision trees, Neural Networks, SVMs, and Naive Bayes.

Python for Software Development

As the number of websites, daily active users, and applications grows, programmers are increasingly turning to Python for software development. Python was designed for server-side web applications for its easy integration with other languages and its flexible frameworks,

One of Python’s benefits is its easy integration with other web languages. Python has third-party packages that enable collaboration with other languages such as C, Java, Ruby, and Objective-C. This allows for quick development and deployment of particular parts of tools and applications.

Python’s web frameworks, namely Django and Flask, allow programmers to create and scale projects efficiently.

Flask is a relatively new framework, and is now the most commonly used web framework for new Python coders. Flask is simple and easy to learn due to its lack of syntax and need for boilerplate code. This minimalistic language allows for a great deal of control and is the ideal choice for websites that provide live updates, for example, a stock ticker, due to its speed and live data fetching abilities.

Django is a “batteries included” framework, which means that Django makes it easy for Python developers to dive into web applications without worrying about the infrastructure upfront. Django is a well-established platform that supports many plug-ins, but it unfortunately has a steep learning curve and can feel overwhelming for new programmers.

Web Development is another vertical that displays Python’s flexibility and power. Whether you want to create software, websites, web applications or just learn how to code, Python is the perfect language to choose!

Python for Automation

Python’s power extends beyond data science, machine learning, and web development, and makes its way into automation. With the power of Python, programmers can automate tasks that, for decades, had to be manually completed. Python scripts are capable of automating countless types of tasks due to the extensive libraries that are available in the language.

One of the most popular automated tasks in Python is called web scraping. Using Beautifulsoup, programmers are able to write a Python script that will “scrape” the data off a webpage and store it into a CSV file. This allows researchers to gain all the information they are seeking in a clean, easy-to-analyze format within seconds!

Python can be used to automate hundreds of additional tasks such as inputting data into a form, searching for files, updating data in Excel, and much more. Through the power of automation, Python enables programmers to complete tens of hours’ worth of tedious tasks in just a few seconds.

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Learn Python for a New Career

  • Python Developer

    Indeed Avg. Salary

    $119K / year

    indeed.com

    Glassdoor Avg. Salary

    $76K / year

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    Python Developers typically choose to focus on back end web development, data science or analysis, scripting, or product development. They build the server side of websites, processes for data analysis, and create automation scripts.

    Read more
  • Data Scientist

    Indeed Avg. Salary

    $121K / year

    indeed.com

    Glassdoor Avg. Salary

    $113K / year

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    Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.

    Read more
  • Web Developer

    Indeed Avg. Salary

    $77K / year

    indeed.com

    Glassdoor Avg. Salary

    $68K / year

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    Web developers build webpages using coding languages such as HTML, CSS, and JavaScript. They program functionality and identify/troubleshoot errors in code. Web developers can work on front-end development (the part of the website you see in a web browser), or on back-end development (the logic and database functionality that runs on the web server). Others work as full-stack developers, providing end-to-end (front to back) expertise.

    Read more
  • Full Stack Developer

    Indeed Avg. Salary

    $112K / year

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    Glassdoor Avg. Salary

    $105K / year

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    Full Stack Developers build web applications for both the visible front end that users see and the back end that powers the applications.

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  • Back End Developer

    Indeed Avg. Salary

    $127K / year

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    Glassdoor Avg. Salary

    $101K / year

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    A Back End Developer builds the server-side of a web application and integrates front end development components.

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  • Product Manager

    Indeed Avg. Salary

    $107K / year

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    Glassdoor Avg. Salary

    $108K / year

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    Product managers guide product development from ideation to market. Starting with consumer and market research, they use their understanding of customer wants and needs to inform product development and go-to-market strategy. They will work closely with engineering, marketing, sales, and other teams to launch products. After launch, they solicit and analyze feedback on the product to inform future iterations.

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Attend Training Live Online

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).

  • Classes are activity-based and taught by a live instructor.
  • You can hear the instruction, ask questions, and share your screen—all in real-time.

Learn more about how our live online training works

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Python Resources

Why Learn Python

Easy to Build & Test

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.

The Language of the People

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.

Diverse Network

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.  

Prominent Companies Are Using Python

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.

Frameworks & Environments & Libraries

Python's frameworks and environments all it to be used for a variety of tasks. 

  • Django is a full-stack Python web framework that is open source and free to all. Django is widely popular amongst developers because it provides programmers with templates that simplify complex code.
  • Flask is a Python web framework that allows the use of Python in web development.
  • Beautiful Soup is a library for pulling data off of the internet.
  • Jupyter is an open-source web application that allows programmers to input, analyze, and visualize data.
  • Python vs. Excel for Data Analytics

    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.

    Thumbnail image for Python vs. Excel for Data Analytics
  • Why Python is a perfect skill to learn remotely

    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.

    Thumbnail image for Why Python is a perfect skill to learn remotely
  • Learning the Math used in Data Science: Introduction

    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. 

    Thumbnail image for Learning the Math used in Data Science: Introduction
  • Why is Everyone Using Python?

    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...

    Thumbnail image for Why is Everyone Using Python?
  • Finding the Mean Using Python

    In this series of posts, we'll cover various applications of statistics in Python. This first post talks about calculating the mean using Python.

    Thumbnail image for Finding the Mean Using Python
  • Calculating Median in 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.

    Thumbnail image for Calculating Median in Python
  • 4 Reasons Why Developers Should Learn Python

    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: ...

  • Getting Started with Python: A Survival Guide

    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...

    Thumbnail image for Getting Started with Python: A Survival Guide
  • Python and Pandas: A Bigger Data Solution to Excel

    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...

    Thumbnail image for Python and Pandas: A Bigger Data Solution to Excel
  • Python Versus: A Look at the Fastest Growing Language

    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...

  • 5 Apps You Use Everyday Built with Python

    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

  • Harmful Myths about Learning Data Science

    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...

  • Understanding the Math of Data Science

    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...

  • 4 Surprising Things You Can Do With Python

    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...

  • Standard Deviation & Variance in Python

    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.

    Thumbnail image for Standard Deviation & Variance in Python
  • Best Python Classes Online

    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. 

    Thumbnail image for Best Python Classes Online
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Key Terms for Python

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

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

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

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

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

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

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).

Upcoming Python Classes in NYC or Live Online

All times are listed in Eastern Time unless otherwise specified.

January 2021
Data Science Certificate
January 19–February 8
Weekdays 10–5pm
Online Only
$3,495
84 Hours
NYC or Live Online

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.

Add to cart
Python for Data Science Bootcamp
January 25–29
Monday to Friday 10–5pm
Registration closing soon
$1,495
30 Hours
NYC or Live Online

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.

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Python Machine Learning Bootcamp
January 26–February 25
Tuesdays & Thursdays 6–9pm
$1,895
30 Hours
NYC or Live Online

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.

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February 2021
Python Machine Learning Bootcamp
February 1–5
Monday to Friday 10–5pm
$1,895
30 Hours
NYC or Live Online

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.

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Python Developer Certificate
February 22–March 24
Monday to Friday 10–5pm
$4,995
138 Hours
NYC or Live Online

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. 

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FinTech Bootcamp
February 22–April 28
Weeknights 6–9pm
$2,995
60 Hours
NYC or Live Online

Learn Python for financial analysis, machine learning, and algorithmic trading from experienced finance & engineering professionals in this immersive FinTech course.

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Python for Data Science Bootcamp
February 23–March 25
Tuesdays & Thursdays 6–9pm
$1,495
30 Hours
NYC or Live Online

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.

Add to cart
Data Science Certificate
February 23–May 27
Weeknights 6–9pm
$3,495
84 Hours
NYC or Live Online

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.

Add to cart
March 2021
Python for Data Science Bootcamp
March 29–April 2
Monday to Friday 10–5pm
$1,495
30 Hours
NYC or Live Online

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.

Add to cart
Data Science Certificate
March 29–April 15
Weekdays 10–5pm
$3,495
84 Hours
NYC or Live Online

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.

Add to cart
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