# Python for Data Science Bootcamp Canonical URL: ## Overview This bootcamp takes you from the basics of Python programming through the foundations of data science and into the starting point of machine learning. You’ll begin by learning Python fundamentals including variables, data types, functions, and control flow before moving into essential tools like NumPy and Pandas for working with arrays and dataframes. From there, you’ll explore data wrangling, descriptive statistics, and exploratory analysis as well as creating visualizations with Matplotlib. By the end, you’ll have the skills to clean, analyze, and visualize data in Python and will be prepared to continue into machine learning with algorithms such as logistic regression, k nearest neighbors, and decision trees. ## What you'll learn - Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs - Work with complex data structures such as dictionaries and lists to efficiently organize and access data - Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration - Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights - Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots - Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows ## Curriculum ### Python Fundamentals #### Python Fundamentals: Variables & Data Types - Declare variables of basic types: integers, floats, strings, booleans - Perform input/output with print() and input() - Apply arithmetic, relational, and logical operators #### Control Flow I: Conditional Logic - Use Boolean operators ==, !=, \<, \>, \<=, \>= - Write if/else and nested conditionals - Combine conditions with and/or for complex logic #### Control Flow II: Loops & Iteration - Implement for loops over ranges and lists; understand iterables - Understand map and filter operations. - Use list comprehensions to simplify operations. #### DataFrames & Data Manipulation with Pandas - Construct DataFrames from various data formats via pd.DataFrame() - Concatenate multiple DataFrames using pd.concat() - Inspect DataFrame shape and handle missing values (NaN) - Perform Panda data analysis operations to glean insight #### Data Visualization: Charting Basics - Plot time series with plt.plot() for line charts - Create scatter plots using plt.scatter() to reveal correlations - Decide between line vs. scatter based on data context and purpose #### Trend Analysis with Regression Lines - Understand least-squares regression concept and its interpretation - Compute a best-fit line via numpy.polyfit() - Overlay regression lines on scatter plots and make predictions #### Advanced Plot Customization - Annotate charts with titles, axis labels, and legends - Highlight key data points (e.g., min/max) directly on plots - Use stacked bar charts, pie charts, and animated charts to visualize data ## Schedule - Jun 8, 2026 – Jun 12, 2026 — NYC - Jul 26, 2026 – Aug 23, 2026 — NYC - Jul 27, 2026 – Jul 31, 2026 — NYC - Aug 4, 2026 – Sep 3, 2026 — NYC - Aug 4, 2026 – Sep 3, 2026 — NYC - Sep 14, 2026 – Sep 18, 2026 — NYC - Sep 14, 2026 – Sep 18, 2026 — NYC - Nov 1, 2026 – Nov 15, 2026 — NYC - Nov 2, 2026 – Nov 6, 2026 — NYC - Nov 2, 2026 – Nov 6, 2026 — NYC - Nov 17, 2026 – Dec 22, 2026 — NYC - Nov 17, 2026 – Dec 22, 2026 — NYC - Dec 13, 2026 – Jan 10, 2027 — NYC ## Instructors ### Brian McClain — Program Director & Senior Instructor Brian McClain is an experienced instructor, curriculum developer, and web developer. Brian served as Director for a coding bootcamp before joining Noble Desktop in 2022, where he is now a lead instructor and course developer for both JavaScript and Python. He teaches Web Development, JavaScript, Python for Data Science, Machine Learning, and AI. Prior to Noble, he taught Python Data Science and Machine Learning as an Adjunct Professor of Computer Science at Westchester County College. Brian is also an active industry professional in the field of generative AI app development. His website and iOS app, Artmink, provides appraisals of art and antiques from user-uploaded images. ### Art Yudin — Program Director & Senior Instructor Art Yudin is a FinTech enthusiast who has a great passion for coding and teaching. Art is the founder and CEO of Practical Programming (a Noble Desktop partner company), a leading training company for aspiring developers and data scientists. Currently, Art develops financial services software and leads classes and workshops at Practical Programming in New York and Chicago. He is the author of several coding publications including "Building Versatile Mobile Apps with Python and REST with React and Django." ### Mourad Kattan — Program Director & Instructor Mourad Kattan is an instructor and Program Director of Business, Finance, & Excel at Noble Desktop, teaching classes and designing courses in Excel, finance, accounting, and financial modeling. Before Noble Desktop, Mourad worked as a financial analyst at Credit Suisse and H/2 Capital Partners. In those positions, he used advanced analytical and financial skills to evaluate a variety of investments. Mourad graduated from the University of Pennsylvania summa cum laude and is part of the Beta Gamma Sigma honor society. Learn more about [Mourad Kattan's](/mourad-kattan) background and expertise. ### Garfield Stinvil — Senior Instructor Garfield is an experienced software trainer with over 16 years of real-world professional experience. He started as a data analyst with a Wall Street real estate investment company & continued working in the professional development department at New York Road Runners Organization before working at Noble. He enjoys bringing humor to whatever he teaches and loves conveying ideas in novel ways that help others learn more efficiently. Since starting his professional training career in 2016, he has worked with several corporate clients including Adobe, HBO, Amazon, Yelp, Mitsubishi, WeWork, Michael Kors, Christian Dior, and Hermès. Outside of work, his hobbies include rescuing & archiving at-risk artistic online media using his database management skills. ### Colin Jaffe — Instructor Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks. Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. Colin lives in Brooklyn with his wife, two kids, and many intricate board games. ### Kash Sudhakar — Instructor Kash specializes in full-stack web development, data analysis & visualization, machine learning, artificial intelligence, and applied computer science. With over 6 years of teaching, curriculum, and leadership experience across coding boot camps and other educational institutions, combined with over 3 years of professional software engineering and data science expertise, he's driven to help shape the next generation of technologists and creative coders. ## FAQ ### How is this class structured? 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. ### How many students are in a given class? Noble's typical class ranges from 8-12 students, but we allow up to 20 students to register for our course. ### How does this class prepare me for the job market? 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. ### Why do you need to learn NumPy, Pandas, Matplotlib, and scikit-learn? 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, scikit-learn allows for regressional and predictive analysis of data. ### Is there mandatory work outside of the classroom? Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice. ### What tangible skills do students leave with after the class? Students will leave with proficiencies in both Python. Additionally, students will be proficient in various Python libraries such as NumPy, Pandas, Matplotlib, and scikit-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. ## Pricing **Tuition:** $1495 Payment options: GI Bill accepted.