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Introduction to Data Science - DATA 101

Covers data types, data cleaning, visualization, descriptive statistics, basic programming, exploratory analysis, and introductory machine learning concepts.

Study Tools

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Study guides

8

5 Descriptive Statistics

Learn how to summarize categorical and numerical data with appropriate measures of center, spread, position, and graphical displays.
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6 Data Visualization

Learn to select charts for different questions, interpret their labels, scales, patterns, and uncertainty, and communicate findings without overstating what the data show.
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8 Introduction to Machine Learning

Learn how machine learning uses data, how to train and evaluate models, and how to interpret their performance and limitations.
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7 Exploratory Data Analysis

Learn how exploratory data analysis uses plots and numerical summaries to reveal patterns, investigate unusual observations, and develop useful follow-up questions without treating patterns as proof of causation.
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4 Data Cleaning and Preparation

Learn how to prepare trustworthy data by handling missing values, investigating duplicates, standardizing formats, validating rules, and following a reproducible workflow.
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3 Data Types and Data Structures

Learn how variable meaning, measurement scales, and table structure determine which data summaries and comparisons are appropriate.
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2 Programming Foundations for Data Analysis

Learn how Python variables, collections, conditionals, loops, and functions work together to make data analysis steps repeatable and easier to inspect.
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1 Introduction to Data Science

Learn how to plan and carry out an iterative data science project, choose formats for different data needs, and handle data responsibly.
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Quizzes

8

7 Exploratory Data Analysis

A medium-difficulty quiz on using exploratory data analysis to choose visualizations, interpret patterns, investigate anomalies, and develop useful follow-up questions.
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5 Descriptive Statistics

A medium-difficulty quiz on choosing, calculating, and interpreting descriptive statistics for categorical and numerical data.
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4 Data Cleaning and Preparation

A 14-question medium-difficulty quiz on applying data cleaning and preparation principles.
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6 Data Visualization

Practice selecting, reading, and communicating data visualizations. Apply chart-choice principles, check how scales and uncertainty affect interpretation, and describe patterns without claiming more than the data support.
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8 Introduction to Machine Learning

A medium-difficulty quiz on core machine-learning concepts, data splitting, model evaluation, and interpreting results.
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3 Data Types and Data Structures

A medium-difficulty quiz on interpreting variable types, measurement scales, and data structures.
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2 Programming Foundations for Data Analysis

A medium-difficulty quiz on applying Python programming foundations to data analysis tasks.
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1 Introduction to Data Science

A medium-difficulty quiz on data science workflows, data formats, and responsible data practice.
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Flashcards

8

5 Descriptive Statistics

Review how to choose, calculate, and interpret key summaries of categorical and numerical data.
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6 Data Visualization

Learn how to choose, read, and communicate charts accurately, including how to interpret scales, uncertainty, and visual accessibility.
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8 Introduction to Machine Learning

Review machine-learning data concepts, learning tasks, model evaluation, and the limits of test results.
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7 Exploratory Data Analysis

Explore how exploratory data analysis uses plots and summaries to reveal patterns, investigate unusual observations, and guide follow-up questions.
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4 Data Cleaning and Preparation

Learn how to prepare trustworthy data by handling missing values, duplicates, inconsistent formats, and validation while preserving meaning and reproducibility.
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3 Data Types and Data Structures

Learn how variable types, measurement scales, and table structures guide valid data summaries and comparisons.
Flashcards · Updated

2 Programming Foundations for Data Analysis

Review Python variables, collections, conditionals, loops, and functions used to organize and process data.
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1 Introduction to Data Science

Review the data science workflow, common data formats, and responsible practices for managing and interpreting data.
Flashcards · Updated