Enhance your data analysis and predictive modeling skills using popular Python tools
Key Features
- Cover all fundamental libraries for operation and manipulation of Python for data analysis
- Implement real-world datasets to perform predictive analytics with Python
- Access modern data analysis techniques and detailed code with scikit-learn and SciPy
Book Description
Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.
Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.
In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.
By the end of this book, you will have hands-on experience performing data analysis with Python.
What you will learn
- Explore important Python libraries and learn to install Anaconda distribution
- Understand the basics of NumPy
- Produce informative and useful visualizations for analyzing data
- Perform common statistical calculations
- Build predictive models and understand the principles of predictive analytics
Who this book is for
Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
Table of Contents
- The Anaconda Distribution and Jupyter Notebook
- Vectorizing Operations with Numpy
- Pandas: Everyone's Favorite Data Analysis Library
- Visualization and Exploratory Data Analysis
- Statistical Computing with Python
- Introduction to Predictive Analytics Models
Alvaro Fuentes is a data scientist with an M.S. in quantitative economics and applied mathematics with more than 10 years of experience in analytical roles. He worked in the central bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as business, education, psychology, and mass media. He has taught courses to students in topics such as data science, mathematics, statistics, R programming, and Python. He also has technical skills in R programming, Spark, PostgreSQL, Microsoft Excel, machine learning, statistical analysis, econometrics, and mathematical modeling.
