# Python for Data Science Immersive Canonical URL: ## Overview From suggesting your next binge-watching spree to predicting the stock market, Python is used for analyzing data and automation tasks in practically every high-growth industry. In this fast-paced course, you’ll learn practical applications of Python so that you can start contributing valuable insights to your company or to land your first job in data science. Python careers are projected to grow more than four times faster than most US vocations. Python’s easy to read syntax and versatile practical applications makes it the best programming language to learn right now. ## What you'll learn - Programming foundations including objects, loops, and functions - The object-oriented programming paradigm - How to work with different types of data such as strings, lists, and integers - Selectively alter the control flow of your programming with conditional statements - Analyze tabular data using Python libraries NumPy and Pandas - Create data visualizations with Matplotlib - Predict outcomes using linear regression with Scikit-Learn ## 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 - Sep 14, 2026 – Sep 18, 2026 — NYC - Nov 2, 2026 – Nov 6, 2026 — NYC - Nov 2, 2026 – Nov 6, 2026 — NYC - Nov 17, 2026 – Dec 22, 2026 — NYC - Nov 30, 2026 – Dec 4, 2026 — NYC - Dec 13, 2026 – Jan 10, 2027 — NYC ## 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? Typical class size 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 class will prepare students with proficiencies in Python and its data science libraries. This is a great starting point for anyone 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 Sci-Kit 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 perform mathematical and statistical operations at scale. Pandas allow Python to work with tabular data such as data imported from CSV or Excel files. Matplotlib is a tool that allows for Python to have graphing capabilities similar to Excel. Lastly, Sci-Kit Learn allows for regression 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 Python proficiency. Additionally, students will gain substantial practice with various Python libraries such as NumPy, Pandas, Matplotlib, and Sci-Kit learn. These libraries 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.