- 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
Data analysis allows making sense of heaps of data. Python, with its strong set of libraries, is a popular language used today to conduct various data analysis, machine learning and visualization tasks.
With this book, you will learn about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time.
What you will learn
- Install open source Python modules like NumPy, SciPy, Pandas, stasmodels, scikit-learn, theano, keras, and tensorflow on various platforms
- Prepare, 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, 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, Deep Learning and Ensembles
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