This Python for Data Science Bootcamp takes you from the core fundamentals of Python programming through the beginning of machine learning, giving you everything you need to begin working confidently as a data scientist or analyst. You will learn why Python has become the dominant language for data science, how to write programs that work with real data, how to create compelling visualizations, and how to apply statistical thinking to build your first machine learning models.
Python Fundamentals
The course opens with the building blocks of Python: writing statements and expressions, declaring variables, working with different data types, using lists, indexing and slicing sequences, calling functions and methods, and more. Key concepts such as object-oriented programming and working in the IDLE development environment are introduced early to establish strong, professional habits from the start.
Once the environment is configured, you will work extensively with Python's core data types, including strings, lists, dictionaries, and tuples. Understanding the specific strengths and appropriate uses of each data type is fundamental to writing effective, readable Python code.
Program Structure and Control Flow
The second section covers the logical structures that power most Python programs: if-else statements, Boolean operations, and various types of loops. You will learn to write reusable functions, iterate through data with for loops, work with nested data structures, and import and use external packages to extend Python's capabilities.
Arrays and DataFrames
This section introduces the core tools of Python-based data science. You will learn to import, inspect, and clean data using NumPy and Pandas, work with Pandas DataFrames, reshape and wrangle messy data into usable formats, and generate descriptive statistics that give you a first look at your data.
Data Analysis and Visualization
You will develop the ability to analyze datasets and communicate findings visually using Pandas, NumPy, and Matplotlib. Topics include filtering and cleaning data, grouping and pivoting, exploratory data analysis (EDA), and building visualizations such as bar charts, histograms, scatter plots, and more advanced graphical representations designed for sharing insights with others.
Linear Regression and Introductory Machine Learning
The final section covers data science workflows and the fundamental statistics needed to ensure your data and models are sound before you build with them. You will construct your first machine learning models, evaluate their performance, and develop the intuition to move into more advanced modeling work.
Next Steps After This Bootcamp
After completing this course, you will be prepared to move into the Python Machine Learning Bootcamp, which picks up directly where this program leaves off. There, you will tackle advanced statistics and build machine learning models using logistic regression, k-nearest neighbors, and decision trees on your way to becoming a fully equipped data scientist.
Flexible Training Solutions for Your Team
Group Vouchers and Scheduled Classes
Group vouchers are available so employees can attend sessions at their convenience, with options on weekdays, weeknights, and weekends. Courses run live online and in person at our Midtown Manhattan offices. Larger voucher packages include a custom registration tracking portal.
With a wide variety of courses on the calendar, employees can choose sessions that match their current skill level, schedule, and preferred format.
Practical Skills from Working Industry Professionals
Participants leave training ready to apply what they have learned right away, with all course materials to keep for ongoing reference and daily use.
Our curriculum is hands-on and built around real data science workflows and challenges. Training is available on-site at your office, at our Midtown Manhattan location, or through live virtual instruction, and can be fully customized to your organization's specific learning needs and goals.
Who Should Attend
This course is well-suited for business analysts, data analysts, data scientists, developers, and anyone whose work involves querying or reporting from databases. It is equally valuable for professionals in non-technical roles who want to build the SQL skills to pull their own data rather than relying on others. No prior database experience is required. If you work with data regularly and want more control over how you access and analyze it, this course is the right starting point.
