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Data Analysis with Python: NumPy & Pandas Masterclass

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


. 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


  • 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


  • Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results
Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.