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|a Data Analysis and Visualization Using Python
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|b Analyze Data to Create Visualizations for BI Systems /
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|a Chapter 1: Introduction to data science with python -- Chapter 2: The importance of data visualization in business intelligence -- Chapter 3: Data collections structure -- Chapter 4: File I/O processing & Regular expressions -- Chapter 5: Data gathering and cleaning -- Chapter 6: Data exploring and analysis -- Chapter 7: Data visualization -- Chapter 8: Case Study.
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|a Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. You will: Use Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems.
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