Data Analysis with Python: NumPy & Pandas Masterclass
Learn NumPy & Pandas for data science, data analysis & business intelligence, with practical, hands-on Python projects!
What you'll learn
- Master the essentials of NumPy and Pandas, two of Python's most powerful data analysis packages
- Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
- Analyze and manipulate dates and times for time intelligence and time-series analysis
- Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms
- Import and export flat files, Excel workbooks and SQL database tables using Pandas
- Build powerful, practical skills for modern analytics and business intelligence
About The Course : Data Analysis with Python: NumPy & Pandas Masterclass
OURSE OUTLINE:
. Intro to NumPy & Pandas
- Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis
. Pandas Series
- Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis
. Intro to DataFrames
- Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently
. Manipulating DataFrames
- Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data
. Basic Data Visualization
- Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms
MID-COURSE PROJECT
- Put your skills to the test with a brand new dataset, and use your Python skills to analyze and evaluate a new retailer as a potential acquisition target for Maven MegaMart
. Analyzing Dates & Times
- Learn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averages
. Importing & Exporting Data
- Read in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its source
. Joining DataFrames
- Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows
. FINAL COURSE PROJECT
- Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results