Data science is the act of extracting knowledge from data. This may seem like a foreign concept at first, but you use data science all the time in your daily life. When you see a pattern a sequence of numbers, your mind has actually used data science to perform the task. What data science does is quantify what you do normally and make it possible to apply the knowledge to all sorts of different technologies. For example, robots use data science to discover objects in their surroundings.
Of course, data science is used for all sorts of applications. For example, data science is used with big data to perform tasks such as data mining or to predict trends based on various data sources. The fact that your browser predicts what you might buy based on previous purchases rests on data science. Even your doctor relies on data science to predict the outcome of a certain series of medications on a illness you might have.
Even though data science first appears easy to categorize, it’s actually huge and quite difficult to pin down. It relies on the inputs of three disciplines: computer science, mathematics, and statistics. There are all sorts of sub-disciplines used as well. Because of the depth and width of knowledge required, a data scientist often works as part of a team to tease out the meanings behind the data provided to solve a problem.
Python for Data Science for Dummies provides you with a beginning view of data science through the computer science discipline using a specific language, Python. The capabilities of Python as a language make it a perfect choice for this book. While reading this book, you’ll see these topics explained:
- Part I: Getting Started with Data Science & Python
- Chapter 1: Discovering the Match between Data Science and Python
- Chapter 2: Introducing Python Capabilities and Wonders
- Chapter 3: Setting Up Python for Data Science
- Chapter 4: Reviewing Basic Python
- Part II: Getting Your Hands Dirty with Data
- Chapter 5: Working with Real Data
- Chapter 6: Getting Your Data in Shape
- Chapter 7: Shaping Data
- Chapter 8: Putting What You Know in Action
- Part III: Visualizing the Invisible (2 Pages)
- Chapter 9: Getting a Crash Course in MatPlotLib
- Chapter 10: Visualizing the Data
- Chapter 11: Understanding Interactive Graphical and Computing Practice
- Part IV: Wrangling Data
- Chapter 12: Stretching Python’s Capabilities
- Chapter 13: Exploring Data Analysis
- Chapter 14: Reducing Dimensionality
- Chapter 15: Clustering
- Chapter 16: Detecting Outliers in Data
- Part V: Learning from Data
- Chapter 17: Exploring Four Simple and Effective Algorithms
- Chapter 18: Performing Cross Validation, Selection and Optimization
- Chapter 19: Increasing Complexity with Linear and Non-linear Tricks
- Chapter 20: Understanding the Power of the Many
- Part VI: Parts of Ten
- Chapter 21: Ten Essential Data Resources
- Chapter 22: Ten Data Challenges You Should Take
As you can see, this book is going to give you a good start in working with data science. Because of the subject matter, I really want to avoid making any errors in book, which is where you come into play. I’m looking for beta readers who use math, statistics, or computer science as part of their profession and think they might be able to benefit from the techniques that data science provides. As a beta reader, you get to see the material as Luca and I write it. Your comments will help us improve the text and make it easier to use.
In consideration of your time and effort, your name will appear in the Acknowledgements (unless you specifically request that we not provide it). You also get to read the book free of charge. Being a beta reader is both fun and educational. If you have any interest in reviewing this book, please contact me at John@JohnMuellerBooks.com and will fill in all the details for you.