Principles of Data Science

MediumData ScienceStatistics10 chapters

A hands-on course covering the full data science workflow, from collecting and cleaning data to statistical analysis, machine learning, deep learning, and ethical reporting. Learn to use Python and real datasets to draw insights and build predictive models.

What This Course Covers

Principles of Data Science is structured into 10 chapters that build on each other progressively:

Chapter 1: Foundations of Data Science and the Data Science Cycle▼

Chapter 2: Data Collection, Ethics, and Preparation▼

Chapter 3: Descriptive Statistics and Probability▼

Chapter 4: Inferential Statistics and Regression Analysis▼

Chapter 5: Time Series Analysis and Forecasting▼

Chapter 6: Machine Learning Fundamentals▼

Chapter 7: Deep Learning and Artificial Intelligence▼

Chapter 8: Ethics in Data Science▼

Chapter 9: Data Visualization▼

Chapter 10: Reporting Results and Validating Models▼

Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.

How to Study Principles of Data Science on Lambdio

Lambdio's AI-powered platform adapts to how Data Science courses are best learned. Here's our recommended approach:

Learning Mode

Standard Mode — for first-time learning of each chapter

Review Modes

Standard, Quiz — for spaced repetition reviews

Learning Priority

Medium Priority — controls how often the algorithm schedules reviews

Principles of Data Science is a broad, code-and-formula-driven course that blends statistics, Python programming, machine learning, and communication skills. Standard Mode fits best because concepts such as the correlation coefficient, ARIMA parameters, backpropagation, and cross-validation are built from precise definitions and worked examples — formats where direct explanation with comprehension checks outperforms open-ended questioning. Socratic Mode is poorly suited here: guiding a learner to independently rediscover the least-squares method or the bias-variance trade-off through leading questions would be slow and error-prone compared with a structured walkthrough. The Medium difficulty and wide scope make Medium priority the appropriate default, since the spaced repetition algorithm will keep many distinct topics — statistical vocabulary, model families, and ethical frameworks — in balanced rotation without overloading the schedule. Feynman Mode is optional for reinforcing conceptual material such as bias, fairness, and the data science cycle, while Quiz Mode is ideal for frequent, low-effort check-ins on definitions, model assumptions, and evaluation metrics before exams or project work.

Interactive Quiz

Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:

Q1: Which step of the data science cycle involves cleaning and transforming raw data into a form suitable for analysis?

Q2: Which sampling method divides the population into subgroups and samples from each in proportion to its size?

Q3: Which measure of center is generally the most representative when a dataset contains strong outliers?

Q4: What does a 95% confidence interval represent?

Q5: Which forecasting model combines autoregressive, integrated, and moving-average components?

Q6: In machine learning, what describes a model that is too complex and fits the noise in its training data?

Q7: Which validation technique trains and tests a model on multiple different subsets of the data?

What You'll Be Able to Do After This Course

✓Describe the interdisciplinary foundations of data science and apply the iterative data science cycle to structure a real analysis project
✓Select appropriate data collection and sampling methods, and clean raw data by handling missing values, outliers, and noisy measurements
✓Summarize datasets using measures of center, variation, and position, and reason about uncertainty with probability theory and distributions
✓Construct confidence intervals, run hypothesis tests, and interpret correlation, linear regression, and ANOVA results correctly
✓Decompose and forecast time series data using moving averages, exponential smoothing, and ARIMA models, and evaluate forecasts with error metrics
✓Build and evaluate supervised and unsupervised machine learning models, including logistic regression, clustering, decision trees, and random forests
✓Explain the architecture and training of neural networks and describe how deep learning and NLP techniques power modern AI systems
✓Apply ethical principles to data collection, modeling, visualization, and reporting, and validate models using cross-validation, information criteria, and sensitivity analysis