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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

2 Working with Data and Data Types

Learn how to structure, import, inspect, and prepare tabular data in pandas while checking data types, missing values, and inconsistent entries.
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5 Data Visualization

Learn how to choose charts for comparisons, distributions, trends, and relationships, and how to design them so data is communicated clearly and accurately.
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3 Data Cleaning and Preparation

Learn how to identify and address missing, duplicated, inconsistent, implausible, and incorrectly formatted data while preserving meaning and documenting decisions.
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8 End-to-End Data Analysis Project

Learn how to plan, clean, explore, model, evaluate, and document a reproducible data analysis project, using a synthetic customer dataset as a practical example.
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4 Descriptive Statistics

Learn how to summarize distributions and relationships with appropriate statistics, graphs, and careful interpretation.
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6 Exploratory Data Analysis

Learn how to inspect, summarize, visualize, and interpret data during exploratory data analysis without confusing exploratory patterns with proof.
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7 Introduction to Machine Learning

Learn how machine learning uses labeled and unlabeled data, how models are trained and evaluated, and why evaluation must account for leakage, overfitting, and bias.
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1 Programming Foundations for Data Science

Learn how Python values, collections, conditions, loops, and functions work together to inspect and transform data.
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Quizzes

8

2 Working with Data and Data Types

A medium-difficulty quiz on interpreting tabular data, choosing appropriate data types, and inspecting data quality with pandas.
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8 End-to-End Data Analysis Project

A medium-difficulty quiz on planning, cleaning, exploring, modeling, and evaluating an end-to-end data analysis project.
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4 Descriptive Statistics

A medium-difficulty quiz on interpreting descriptive statistics, choosing appropriate summaries, and applying basic measures of center, spread, and position.
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5 Data Visualization

A medium-difficulty quiz on choosing and interpreting charts, checking data, and avoiding misleading visual encodings.
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1 Programming Foundations for Data Science

A medium-difficulty quiz on Python programming foundations for data science, covering values, collections, conditions, loops, and functions.
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6 Exploratory Data Analysis

A medium-difficulty quiz on applying exploratory data analysis methods to inspect data, choose summaries and displays, assess patterns cautiously, and communicate findings.
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7 Introduction to Machine Learning

A medium-difficulty quiz on applying core machine learning concepts, evaluating models, and recognizing limitations.
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3 Data Cleaning and Preparation

A medium-difficulty quiz on applying data-cleaning principles while preserving meaning, checking evidence, and documenting decisions.
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Flashcards

8

5 Data Visualization

Learn how to choose charts for comparisons, distributions, trends, and relationships, and how to present data clearly and accurately.
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6 Exploratory Data Analysis

Learn how to inspect, summarize, and visualize data, choose displays for different variable types, and interpret exploratory findings cautiously.
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8 End-to-End Data Analysis Project

Review the end-to-end data analysis workflow, from planning and cleaning through exploration, model evaluation, and reproducibility.
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1 Programming Foundations for Data Science

Review Python variables, data types, collections, conditional logic, loops, and functions used to organize and transform data.
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7 Introduction to Machine Learning

Learn core machine learning approaches, evaluation practices, and limitations, including overfitting and bias.
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4 Descriptive Statistics

Review how descriptive statistics summarize distributions and relationships, and how to interpret their results responsibly.
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3 Data Cleaning and Preparation

Learn how to prepare reliable data by assessing missing values, duplicates, inconsistent categories, outliers, and format problems while documenting decisions.
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2 Working with Data and Data Types

Review how tabular data is structured, imported, inspected, and checked for appropriate types and data-quality issues in pandas.
Flashcards · Updated