Key Features
Find, manipulate, and analyze your data using the Python 3.5 libraries
Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.
Book Description
Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.
With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.
The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
What you will learn
Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms
Prepare and clean your data, and use it for exploratory analysis
Manipulate your data with Pandas
Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
Understand signal processing and time series data analysis
Get to grips with graph processing and social network analysis
About the Author
Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.
Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.
Table of Contents
Getting Started with Python Libraries
NumPy Arrays
The Pandas Primer
Statistics and Linear Algebra
Retrieving, Processing, and Storing Data
Data Visualization
Signal Processing and Time Series
Working with Databases
Analyzing Textual Data and Social Media
Predictive Analytics and Machine Learning
Environments Outside the Python Ecosystem and Cloud Computing
Performance Tuning, Profiling, and Concurrency
Key Concepts
Useful Functions
Online Resources
Find, manipulate, and analyze your data using the Python 3.5 libraries
Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.
Book Description
Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.
With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.
The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
What you will learn
Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms
Prepare and clean your data, and use it for exploratory analysis
Manipulate your data with Pandas
Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
Understand signal processing and time series data analysis
Get to grips with graph processing and social network analysis
About the Author
Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.
Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.
Table of Contents
Getting Started with Python Libraries
NumPy Arrays
The Pandas Primer
Statistics and Linear Algebra
Retrieving, Processing, and Storing Data
Data Visualization
Signal Processing and Time Series
Working with Databases
Analyzing Textual Data and Social Media
Predictive Analytics and Machine Learning
Environments Outside the Python Ecosystem and Cloud Computing
Performance Tuning, Profiling, and Concurrency
Key Concepts
Useful Functions
Online Resources
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