Analytics with Pandas Complete Guide
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Follow along with data science expert Rohit Kumar and apply data analysis and analytics using the Python Pandas library.

The following ten topics will be covered through a combination of lecture and hands-on to maximize your understanding of using Pandas for data analysis:

Introducing Data Science and Data Analytics. As a prerequisite to learning Pandas, become equipped to explain the fields of data science and data analysis in this first topic in the Analytics with Pandas Complete Guide. Learn why data analysis is so important and become familiar with data analysis tools. Know why Python is ideal for data analytics. Become comfortable with the various types of analyses: Text Analysis, Statistical Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis. Become aware of the steps in the data analytics process: Data Requirements Gathering, Data Collection, Data Analysis, Data Interpretation, and Data Visualization.
Introducing the Pandas Library. Receive a broad overview to Pandas and install the Pandas Python library in second topic in the Analytics with Pandas Complete Guide. Learn the key features of Pandas and follow along with Rohit on a detailed Pandas example. Install the Pandas library and the related packages of Numpy and Matplotlib.
Practicing Pandas Functionality Part 1. Practice Pandas functionality in this third topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and master the three main Python Pandas structures of Series, Data Frame, and Panel.
Practicing Pandas Functionality Part 2. Continue practicing Pandas functionality in this fourth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and dive deeper into Pandas panels.
Applying Statistics using Pandas. Apply statistics using Pandas in this fifth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and practice using these Pandas functions: Count, Sum, Mean, Median, Mode, Std, Min, Max, Abs, and prod.
Working with Text Data using Pandas. Work with text data using Pandas in this sixth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and practice using these Pandas functions: Lower, Upper, Len, Strip, Split, Cat, Contains, and Replace.
Working with Date and Time using Pandas. Work with date and time using Pandas in this seventh topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and practice using time-series and date functionality Pandas commands.
Importing Data from and Exporting Data to Pandas. Import data from and export data to Pandas in this eighth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and practice using a number of Input/Output (I/O) tools with Pandas in various formats including CSV, Excel, and JSON.
Applying Data Visualization using Pandas. Apply data visualization using Pandas in this ninth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and practice creating these data visualization diagrams: plots, bar plots, histograms, box plots, area plots, scatter plots, and pie charts.
Using SQL-like Statements with Pandas. Use SQL-like statements with Pandas in this tenth topic in the Analytics with Pandas Complete Guide. Follow along with Rohit and see how Pandas compares with SQL, and apply SQL-like statements in Pandas including Select, Where, and Group By.

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