Elements of Data Science

“Elements of Data Science” delves into the fundamental components of the data science field. It explores topics such as machine learning, data visualization, and statistical analysis. The document discusses the importance of data preprocessing, model evaluation, and the application of algorithms in real-world scenarios. Additionally, it covers the significance of data augmentation for enhancing machine learning models. The content emphasizes the role of data in decision-making processes and the utilization of various tools and techniques to extract valuable insights from data. Overall, “Elements of Data Science” provides a comprehensive overview of key concepts and practices in the data science domain.

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Variables and values

Click here to run this notebook on Colab or click here to download it . Data Science is the use of data to answers questions and guide decision making. For example, a topic of current debate is wheth...

Lists and arrays

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In the previous chapter we used tuples to represent latitude and longitude. In this chapter, you’ll see how to use tuples more ...

Times and places

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In the previous chapter, you learned about variables and two kinds of values: integers and floating-point numbers. In this chap...

Loops and files

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . This chapter presents loops, which are used to express repeated computation, and files, which are used to store data. As an exa...

Dictionaries

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In the previous chapter we used a for loop to read a file and count the words. In this chapter, you’ll learn about a new type c...

Elements of Data Science

Elements of Data Science is an introduction to data science for people with no programming experience. My goal is to present a small, powerful subset of Python that allows you to do real work in data...

DataFrames and Series

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . This chapter introduces Pandas, which is a powerful library for working with data. Pandas provides functions for reading and wr...

Plotting

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . This chapter presents ways to create figures and graphs, more generally called data visualizations . As examples, we’ll generat...

Regression

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In the previous chapter we used simple regression to quantify the relationship between two variables. In this chapter we’ll get...

Distributions

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In this chapter we’ll see three ways to describe a set of values: A probability mass function (PMF), which represents a set of ...

Resampling

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . This chapter introduces resampling methods , which are used to quantify the precision of an estimate. As examples, we’ll use re...

Bootstrap Sampling

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In the previous chapter we used resampling to compute standard errors and confidence intervals, which quantify the variability ...