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(UPI) Chapter 17: Key Concepts in Data Analysis: Indexing, Slicing, Missing Data, and Visualization

80-minute College Credit Course

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About this Course

This course is part of our College Credit Program, designed to help you earn college credit while mastering valuable skills. If you're interested in pursuing college credit, click here to learn more.

Data Science provides the ability to derive insights and make informed decisions from data. It plays a crucial role in various disciplines, including:

  • Healthcare
  • Business
  • Education
  • Politics
  • Environmental Science
  • Social Sciences

This chapter aims to provide an introduction to the field of data science and the data science life cycle. The resources provided in this chapter are meant to guide readers using Python to further explore data science.

Introduction to Data Science Life Cycle and Tools

Data science is a multidisciplinary field that combines collecting, processing, and analyzing large volumes of data to extract insights and drive informed decision-making.

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Introduction to NumPy Library and Its Operations

NumPy is a Python library designed for efficient numerical operations on large, multi-dimensional arrays. It enables high-performance data analysis and manipulation with tools for creating arrays, performing mathematical operations, and conducting linear algebra tasks.

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Introduction to the Pandas Library for Data Analysis

Pandas is a Python library designed for efficient data manipulation and analysis. It provides key data structures like Series and DataFrame, allowing for streamlined data processing, exploration, and cleaning, and integrates well with other data analysis libraries.

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Exploratory Data Analysis (EDA)

Learn the fundamentals of Exploratory Data Analysis (EDA) with Pandas, including techniques for data indexing, slicing, filtering, and handling missing values to uncover insights from your datasets.

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Essential Concepts and Tools for Data Visualization in Data Science

Data visualization plays a pivotal role in data science, enabling us to understand and interpret data more effectively. It aids in exploring data, identifying anomalies, understanding relationships and trends, and communicating findings clearly.

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