Learn the practical digital skills remote businesses look for — inbox and calendar management, Google Workspace, client communication tools, and more.
The Data Science program is designed for anyone who wants to learn how to work with data, uncover meaningful patterns, and turn raw information into insights that can support better decisions and real-world solutions.
You'll develop practical skills in Python, statistics, SQL, data cleaning, exploratory analysis, visualization, and introductory machine learning.
Start with the 12-week intensive or go deeper with our complete 6-month professional track.
Build a strong foundation in data science through Python, statistics, data analysis, SQL, visualization, and introductory machine learning. Work with real-world datasets and learn how to transform raw data into meaningful insights and practical solutions.
Understand the data science lifecycle, common applications, types of data, analytical workflows, and the role of a data professional.
Learn the Python programming concepts required for data analysis, including variables, data types, operators, conditions, loops, functions, and basic data structures.
Work with numerical datasets using NumPy and understand arrays, indexing, mathematical operations, and efficient numerical computation in Python.
Learn how to load, inspect, filter, transform, combine, and organize structured datasets using Pandas DataFrames.
Develop practical techniques for identifying and handling missing values, duplicates, inconsistent data, outliers, and other common data-quality issues.
Build the statistical foundation needed to understand datasets, measure relationships, interpret variation, and make informed analytical decisions.
Learn how to retrieve, filter, sort, join, and aggregate data stored in relational databases using practical SQL queries.
Learn how to investigate datasets, identify patterns and relationships, detect anomalies, and develop meaningful questions from data.
Learn how to communicate analytical findings using effective charts, graphs, and visual storytelling techniques with Python visualization libraries.
Understand the fundamentals of machine learning and how models can be trained to identify patterns and make predictions from data.
Learn how to evaluate machine learning models, understand performance metrics, identify common modeling problems, and interpret prediction results.
Complete an end-to-end data science project involving data preparation, analysis, visualization, and presentation of meaningful insights from a real-world dataset.
Go beyond the fundamentals with a deeper, project-based data science program covering advanced Python, statistics, SQL, data visualization, machine learning, model evaluation, feature engineering, and real-world data projects.
Establish a strong foundation in data science, Python programming, computational thinking, data types, functions, and the complete data science workflow.
Develop practical skills for manipulating, transforming, combining, and preparing structured datasets for analysis.
Build a stronger statistical foundation covering distributions, measures of central tendency, variability, probability, and statistical reasoning.
Work with relational databases and develop increasingly advanced SQL skills for retrieving, joining, aggregating, and analyzing structured data.
Apply professional data-cleaning and exploratory techniques to investigate complex datasets, uncover trends, and identify meaningful relationships.
Develop professional data visualizations and learn how to present analytical findings clearly through charts, dashboards, reports, and data-driven storytelling.
Explore supervised and unsupervised learning and understand how machine learning models are trained to recognize patterns and make predictions.
Learn how to prepare datasets for machine learning by transforming variables, selecting useful features, encoding data, and building effective preprocessing workflows.
Evaluate model performance, compare algorithms, identify overfitting and underfitting, and improve models using appropriate evaluation and optimization techniques.
Apply advanced analytical techniques to larger and more complex datasets while strengthening your ability to investigate problems, interpret results, and communicate findings.
Work through realistic data problems from problem definition and data preparation through analysis, visualization, modeling, and communication of results.
Organize your projects into a professional portfolio, document your analytical process, prepare a technical CV, and practice presenting your work to potential employers or clients.
Complete an end-to-end data science project involving data preparation, exploratory analysis, visualization, machine learning, evaluation, and professional presentation.
Build the skills needed to pursue opportunities across data analysis, business intelligence, analytics, and data science.
No hidden fees — choose the option that fits your budget.
Yes. Most students who join have zero prior experience. This program is built to start from first principles, so no background is assumed going in.
Payment plans are available so you're not paying the full amount upfront — see the pricing options below, or start with the free Intro to Web Development class first.
Sessions are scheduled to fit around a job, school, or other commitments rather than replace your schedule entirely.
You'll finish with a real capstone project you build yourself — something you can show in a portfolio, job application, or client pitch. We don't guarantee job placement, but we do guarantee tangible proof of what you can do.