Principles of Data Science OpenStax test bank cover with digital head, charts, graphs, globe, and data visualization elements. Includes practice questions and answers.
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Downloadable Test Bank – Principles of Data Science, OpenStax

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Description

📊 PRINCIPLES OF DATA SCIENCE (PRACTICE TEST BANK)

Principles of Data Science, OpenStax

Python • Statistics • Machine Learning • Visualization • Reporting

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Coverage
10 main topics
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Skill Levels
Knowledge to analysis
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Also Available
Complete Solutions Manual

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Data Science can get stressful fast. You have to go through and have to read charts, follow Python steps, understand model ideas, and explain what the data means. That is why shallow review is usually not enough before exams. Practicing with test bank has proved to be an effective way college students can rely on; it helps students pinpoints weaknesses and master the material more quickly.

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This book by Openstax is new and offers a clear start in Python, data visualization, probability, machine learning basics, and working with real datasets. If you are enrolled in Intro to Data Science, Data Analytics, Applied Statistics, or Python for Data Science, then this resource for you.

What does this resource gives you

This test bank gives you practice questions written around the book, it has questions that test knowledge, understanding, application, and analysis, so that you can evaluate your skills at every level. The questions are ranked by applicable chapter learning goals, chapter key topics, and level of difficulty.

Knowledge
Understanding
Application
Analysis
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Not only a test bank is available, the complete answers to all textbook problems are included in the Solutions Manual. The solutions manual is an effective study guides that allows you to check your work and learn faster.

Topics Covered in The Test Bank

What Are Data and Data Science?
What Is Data Science? • Data Science in Practice • Data and Datasets • Using Technology for Data Science • Data Science with Python
Collecting and Preparing Data
Overview of Data Collection Methods • Survey Design and Implementation • Web Scraping and Social Media Data Collection • Data Cleaning and Preprocessing • Handling Large Datasets
Descriptive Statistics: Statistical Measurements and Probability Distributions
Measures of Center • Measures of Variation • Measures of Position • Probability Theory • Discrete and Continuous Probability Distributions
Inferential Statistics and Regression Analysis
Statistical Inference and Confidence Intervals • Hypothesis Testing • Correlation and Linear Regression Analysis • Analysis of Variance (ANOVA)
Time Series and Forecasting
Introduction to Time Series Analysis • Components of Time Series Analysis • Time Series Forecasting Methods • Forecast Evaluation Methods
Decision-Making Using Machine Learning Basics
What Is Machine Learning? • Classification Using Machine Learning • Machine Learning in Regression Analysis • Decision Trees • Other Machine Learning Techniques
Deep Learning and AI Basics
Introduction to Neural Networks • Backpropagation • Introduction to Deep Learning • Convolutional Neural Networks • Natural Language Processing
Ethics Throughout the Data Science Cycle
Ethics in Data Collection • Ethics in Data Analysis and Modeling • Ethics in Visualization and Reporting
Visualizing Data
Encoding Univariate Data • Encoding Data That Change Over Time • Graphing Probability Distributions • Geospatial and Heatmap Data Visualization Using Python • Multivariate and Network Data Visualization Using Python
Reporting Results
Writing an Informative Report • Validating Your Model • Effective Executive Summaries

With proper preparation students can pass any exam, no matter how hard topic is.

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Good quality.Good service.The product is firmly packed.Very fast delivery.Very well worth the money.

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