# 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 will 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 will explore data wrangling, descriptive statistics, and exploratory analysis, as well as creating visualizations with Matplotlib. By the end, you will 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 — Live Online - Jul 26, 2026 – Aug 23, 2026 — Live Online - Jul 27, 2026 – Jul 31, 2026 — Live Online - Aug 4, 2026 – Sep 3, 2026 — Live Online - Sep 14, 2026 – Sep 18, 2026 — Live Online - Nov 2, 2026 – Nov 6, 2026 — Live Online - Nov 17, 2026 – Dec 22, 2026 — Live Online - Nov 30, 2026 – Dec 4, 2026 — Live Online - Dec 13, 2026 – Jan 10, 2027 — Live Online ## 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.