Our Python for Data Science Bootcamp is meant to go from the very basics of Python programming to the start of machine learning with Python. In this Bootcamp, you’ll learn how and why Python is used for data science, how to create programs, work with data in Python, create data visualizations, and use statistics to create machine learning models.
The course will start with the fundamentals of Python, including writing basic statements and expressions, creating variables, understanding different data types, working with lists, indexing and slicing lists, using functions and methods, and more. Concepts such as object-oriented programming are introduced.
Once a learning environment has been set up, we will work with different data types such as strings, lists, dictionaries, and tuples. Each data type has its own particular purpose and knowing when to use each one will be essential.
The second part of the course covers conditional statements and control flow tools. This includes the If/Else Statements, Boolean Operations, and different types of loops. These topics create a large portion of the logic in your code and this course will help you master these concepts. Learn to work with dictionaries, create functions, write for loops to iterate through data, and work with packages in Python.
The third part of the course introduces operations and tools for data science. We will learn how to import and clean data using NumPy and Pandas. You’ll learn to work with Pandas dataframes, wrangle data, and get descriptive statistics for your data.
You’ll learn to analyze and visualize data with key data science libraries including Pandas, NumPy, and Matplotlib. Learn to filter and clean data, group and pivot data, and start generating insights from your data with exploratory data analysis. Then create visualizations including bar charts, histograms, and advanced visualization for easy interpretation and sharing of your data insights.
After learning all the foundational Python programming and data analysis skills in this Bootcamp, you will be ready to dive fully into machine learning.
Our Python Machine Learning Bootcamp builds off this foundational knowledge to turn you into a full machine learning data scientist. Pick up right where the Python for Data Science Bootcamp left off with advanced statistics and create machine learning models with logistic regressions, k-nearest neighbors, and decision trees.
From our hands-on training style to world-class instructors and custom-crafted curriculum, we deliver results our alumni are proud of.
Learn the concepts and skills covered in this course or your tuition is on us. See details and terms & conditions.
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Refined over many cohorts for an optimal learning experience
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 W.
Experienced educators who are driven to help you succeed
Refresh the materials for free within one year
Attend this course in-person in NYC or Live Online via Zoom. Engage with expert instructors, ask questions, and get feedback on your exercises and projects.
Unlike other providers, these are not mass open-enrollment classes. At Noble Desktop, you’ll learn in small groups (typically 8-15 students) and receive personalized attention.
Get face-to-face interaction with an instructor and other students when you learn at our NYC campus. Courses are hands-on with a computer and software provided.
Get the same interactivity and access to the instructor as in-person students. There are no extra fees and we’ll work with you to ensure your remote setup is perfect.
Our instructors are veterans of their field and experienced educators, driven to help you succeed.
We want you to leverage the skills you’ll learn. In addition to 30 hours of hands-on, small group training, you’ll receive one 1-on-1 training session where you can:
Upon completion of this course, you’ll receive an official certificate testifying to your mastery of the curriculum. We’ll send you a link where you can download your certificate, share it online with your friends, post it to your professional network on LinkedIn, and view all your earned certificates. Congratulations on your achievement!
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We offer a single free retake of the class within a year.
You can also access recordings of each session in your student portal within one business day of the session’s end. Recordings are available for one month after the session.
Learn practical skills by working on real projects with instructor guidance and lectures to strengthen and improve your skill.
The first 12 hours of this class covers Python the language and general computer science topics. The following 18 hours covers data science topics such as descriptive statistics, data importation, graphical representation of data, and forecasting models.
Noble's typical class ranges from 8-12 students, but we allow up to 20 students to register for our course.
The classes will prepare students with proficiencies in Python and its data science libraries. This is a great starting point for any looking to pursue a career in data science and a perfect class for students looking to add complementary skills to their current job or resume.
Each library allows Python to be used for different tasks. The NumPy package is the foundational package for all of data science as it allows Python to do both mathematical and statistical operations. Pandas allow Python to work with tabular data such as data imported from CSV or Excel file. Matplotlib package is a tool that allows for Python to have graphing capabilities similar to Excel. Lastly, Sci-Kit Learn allows for regressional and predictive analysis of data.
Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice.
Students will leave with proficiencies in both Python. Additionally, students will be proficient in various Python libraries such as NumPy, Pandas, Matplotlib, and Sci-Kit learn. These libraries will allow students to automate data collection, perform analysis on the data, graph the data, and use this data to create predictive models.
Students must be comfortable using a computer. No other prior knowledge is required.
This course does not qualify for payments plans or student financing. See our Payment Plan FAQ to find related programs that qualify.
You may attend this training virtually (online) at the scheduled time the course is offered (New York, Eastern Time).
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