# 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 make 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 comparison 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 pandas 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 - 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 - Jan 4, 2027 – Jan 8, 2027 — NYC - Jan 25, 2027 – Jan 29, 2027 — NYC - Mar 16, 2027 – Apr 15, 2027 — NYC - Mar 22, 2027 – Mar 26, 2027 — NYC - Apr 4, 2027 – May 2, 2027 — NYC - Apr 26, 2027 – May 3, 2027 — NYC - May 24, 2027 – May 28, 2027 — NYC - Jul 13, 2027 – Aug 12, 2027 — NYC - Jul 26, 2027 – Jul 30, 2027 — NYC - Aug 1, 2027 – Aug 29, 2027 — NYC - Sep 13, 2027 – Sep 17, 2027 — NYC ## FAQ ### Who is the target audience for this course? New programmers and people who have never coded before Anyone who wants to break into data science, machine learning, and the tech industry Analysts who work with Excel and are ready to upgrade with Python's ecosystem and automation capabilities Someone working on a solid foundation before diving into machine learning ### 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 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 perform mathematical and statistical operations at scale. Pandas allows 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, scikit-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 scikit-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. ### Is this course open to beginners? Yes, this course is open to beginners. Students must be comfortable using a computer. No other prior knowledge is required. ### Do you offer payment plans or student financing for this course? This course does not qualify for payment plans or student financing. See our Payment Plan FAQ to find related programs that qualify. ### Can I take this course online (remote learning)? No upcoming live online dates are currently available. Please contact us about future availability. ### What happens if I need to miss a session? You have some options: You can watch a recording of the session. We will provide any course materials. Contact us to confirm whether remote attendance is available for your session. You can use your free retake to make up any missed class time. ### What’s included with my tuition? A hands-on learning experience working on projects and exercises, which is proven to boost comprehension, retention, and engagement Expert instructors who are industry professionals and experienced educators that are driven to help you succeed Access to industry-standard software during your training Additional personalized 1-on-1 bonus training with an industry expert to reinforce concepts Top-notch curricula that have been tried and tested over many cohorts and are consistently improved for an optimal learning experience A 1-year free retake policy which allows students to retake any part of the course or program for free to refresh the materials and gain additional practice 30-day access to class recordings to review material at your own pace A certificate of completion to verify your accomplishment ### Are there any additional fees or expenses? There are no extra fees or taxes for our courses. The price you see on this page is the maximum you’ll pay us. ### Do you offer any discounts for this course? Take this course as part of a certificate program and save: Data Science Certificate Price: $3,995 | Hours: 114 Hours FinTech Bootcamp Price: $4,995 | Hours: 114 Hours Data Science Certificate Price: $995 | Hours: 18 Hours Python Data Science & Machine Learning Bootcamp Price: $3,495 | Hours: 96 Hours ## Pricing **Tuition:** $1495