Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide
▶What You Will Learn
⦁Explore data science and its various process models
⦁Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
⦁Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
⦁Retrieve, process, and store data in a wide range of formats
⦁Understand data preprocessing and feature engineering using pandas and scikit-learn
⦁Perform time series analysis and signal processing using sunspot cycle data
⦁Analyze textual data and image data to perform advanced analysis
⦁Get up to speed with parallel computing using Dask
▶Key Features
⦁Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling
⦁Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods
⦁Get to grips with graph processing and sentiment analysis
▶Who This Book Is For
This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
▶What this book covers
⦁ Chapter 1, Getting Started with Python Libraries, explains the data analyst process and the successful installation of Python libraries and Anaconda. Also, we will discuss Jupyter Notebook and its advanced features.
⦁ Chapter 2, NumPy and Pandas, introduces NumPy and Pandas. This chapter provides a basic overview of NumPy arrays, Pandas DataFrames, and their associated functions.
⦁ Chapter 3, Statistics, gives a quick overview of descriptive and inferential statistics.
⦁ Chapter 4, Linear Algebra, gives a quick overview of linear algebra and its associated NumPy and SciPy functions.
⦁ Chapter 5, Data Visualization, introduces us to the matplotlib, seaborn, Pandas plotting, and bokeh visualization libraries.
⦁ Chapter 6, Retrieving, Processing, and Storing Data, explains how to read and write various data formats, such as CSV, Excel, JSON, HTML, and Parquet. Also, we will discuss how to acquire data from relational and NoSQL databases.
⦁ Chapter 7, Cleaning Messy Data, explains how to preprocess raw data and perform feature engineering.
⦁ Chapter 8, Signal Processing and Time Series, contains time series and signal processing examples using sales, beer production, and sunspot cycle dataset. In this chapter, we will mostly use NumPy, SciPy, and statsmodels.
⦁ Chapter 9, Supervised Learning – Regression Analysis, explains linear regression and logistic regression in detail with suitable examples using the scikit-learn library.
⦁ Chapter 10, Supervised Learning – Classification Techniques, explains various classification techniques, such as naive Bayes, decision tree, K-nearest neighbors, and SVM. Also, we will discuss model performance evaluation measures.
⦁ Chapter 11, Unsupervised Learning – PCA and Clustering, gives a detailed discussion on dimensionality reduction and clustering techniques. Also, we will evaluate the clustering performance.
⦁ Chapter 12, Analyzing Textual Data, gives a quick overview of text preprocessing, feature engineering, sentiment analysis, and text similarity. This chapter mostly uses the NLTK, SpaCy, and scikit-learn libraries.
⦁ Chapter 13, Analyzing Image Data, gives a quick overview of image processing operations using OpenCV. Also, we will discuss face detection.
⦁ Chapter 14, Parallel Computing Using Dask, explains how to perform data preprocessing and machine learning modeling in parallel using Dask.
