Murach's Python for Data Science (Paperback)
Scott McCoy
Sold by Grand Eagle Retail, Mason, OH, U.S.A.
AbeBooks Seller since October 12, 2005
New - Soft cover
Condition: New
Quantity: 1 available
Add to basketSold by Grand Eagle Retail, Mason, OH, U.S.A.
AbeBooks Seller since October 12, 2005
Condition: New
Quantity: 1 available
Add to basketPaperback. These days, businesses are collecting massive amounts of data. But this data isnt valuable until someone analyzes it to gain insights that can be used to make decisions. Thats why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade.Now, with Murachs Python for Data Science as your guide, youll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, youll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, youll learn how to use Scikit-learn to create regression models that you can use to make predictions.To tie everything together, this book contains four realistic analyses that use real-world data. Thats because studying analyses like these is critical to the learning process.Mike Murach & Associates has been publishing high-quality books about computer programming since 1972. Download a sample chapter from the Murach website and see for yourself. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller Inventory # 9781943873173
These days, businesses are collecting massive amounts of data. But this data isn’t valuable until someone analyzes it to gain insights that can be used to make decisions. That’s why the US Bureau of Labor Statistics (BLS) predicts that the demand for data analysts will continue to grow for the rest of the decade.
Now, with Murach’s Python for Data Science as your guide, you’ll learn how to use Python libraries to get, clean, prepare, and analyze data at a professional level. To start, you’ll learn how to use Pandas for data analysis and Seaborn for data visualization. Then, you’ll learn how to use Scikit-learn to create regression models that you can use to make predictions.
To tie everything together, this book contains four realistic analyses that use real-world data. That’s because studying analyses like these is critical to the learning process.
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