Learn how to use JupyterLab, Numpy, pandas, Scipy, Matplotlib, and Seaborn for Data scienceThe book will start with quick introductions to Python and its ecosystem libraries for data science such as JupyterLab, Numpy, Pandas, SciPy, Matplotlib, and Seaborn.This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. It also covers an in-depth understanding of Python data science packages where JupyterLab used as an IDE for writing, documenting, and executing the python code, Numpy used for computation of numerical operations, Pandas for cleaning and reorganizing the data, handling large datasets and merging the dataframes to get meaningful insights. You will go through the statistics to understand the relation between the variables using SciPy and building visualization charts using Matplotllib and Seaborn libraries.KEY FEATURES • Get familiar with different inbuilt Data structures, Functional programming, and Datetime objects.• Handling heavy Datasets to optimize the data types for memory management, reading files in chunks, dask, and modin pandas.• Time-series analysis to find trends, seasonality, and cyclic components.• Seaborn to build aesthetic plots with high-level interfaces and customized themes.• Exploratory data analysis with real-time datasets to maximize the insights about data.WHAT WILL YOU LEARN • Learn about Python data containers, their methods, and attributes.• Learn Numpy arrays for the computation of numerical data.• Learn Pandas data structures, DataFrames, and Series.• Learn statistics measures of central tendency, central limit theorem, confidence intervals, and hypothesis testing.• A brief understanding of visualization, control, and draw different inbuilt charts to extract important variables, detect outliers, and anomalies using Matplotlib and Seaborn. WHO THIS BOOK IS FOR This book is for anyone who wants to use Python for Data Analysis and Visualization. This book is for novices as well as experienced readers with working knowledge of the pandas library. Basic knowledge of Python is a must. AUTHOR BIO Purna Chander Rao.Kathula is a Data Science enthusiast, Data Manager, Seasoned Programmer, and a Technical trainer, with around 17+ years of experience in a vast array of languages, including Perl, C, C++, Java, and Python and wide variety set of domains like Insurance, Adtech, Storage, Gaming, Mobility, Big Data, and Analytics. He is a certified Applied Data Science with Python Specialization, from Coursera, University of Michigan. He graduated from the College of Engineering G.I.T.A.M with a degree in Mechanical Engineering. He is a frequent speaker at DataScience and Data Engineering user groups, and he regularly delivers webinars and conducts training on Hadoop, Big data, Data Analysis, and Visualization technologies.Your Blog links https://blog.imaginea.com/author/purna-chander-rao-kathula/https://www.slideshare.net/PurnaChander1https://www.slideshare.net/sriganesha/hive-and-data-analysis-using-pandasYour LinkedIn Profile: https://www.linkedin.com/in/purna-chander-rao-kathula-043852a/ Learn how to use JupyterLab, Numpy, pandas, Scipy, Matplotlib, and Seaborn for Data science The book will start with quick introductions to Python and its ecosystem libraries for data science such as JupyterLab, Numpy, Pandas, SciPy, Matplotlib, and Seaborn. This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. It also covers an in-depth understanding of Python data science packages where JupyterLab used as an IDE for writing, documenting, and executing the python code, Numpy used for computation of numerical operations, Pandas for cleaning and reorganizing the data, handling large datasets and merging the dataframes to get meaningful insights. You will go through the statistics to understand the relation between the variables using SciPy and building visualization charts using Matplotllib and Seaborn libraries.
