Practical Python Programming for Data Scientists Data science plays a very vital role in shaping up the process of transitioning data into information and into knowledge. As business enterprises, organizations, governments, IT companies, and service providers are keenly becoming data-driven, the role and responsibility of data scientists are bound to go up significantly. Python is emerging as the leading programming language for data science projects. The aim of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation data science applications
Data science is a thriving and rapidly expanding field, as you probably already know. People are starting to come to a consensus that everyone should have some basic data science skills, sometimes called “data literacy.” This book is intended to get you up to speed with the basics of data science using the most popular programming language for doing data science today: Python. In this first chapter, we will cover:
Data science is used in a variety of ways. Some data scientists focus on the analytics side of things, pulling out hidden patterns and insights from data, then communicating these results with visualizations and statistics. Others work on creating predictive models in order to predict future events, such as predicting whether someone will put solar panels on their house. Yet others work on models for classification; for example, classifying the make and model of a car in an image. One thing ties all applications of data science together: the data. Anywhere you have enough data, you can use data science to accomplish things that seem like magic to the casual observer.
There’s a saying in the data science community that’s been around for a while, and it goes: “A data scientist is better than any computer scientist at statistics, and better than any statistician at computer programming.” This encapsulates the general skills of most data scientists, as well as the history of the field.
Data science combines computer programming with statistics, and some even call data science applied statistics. Conversely, some statisticians think data science is only statistics. So, while we might say data science dates back to the roots of statistics in the 19th century, the roots of modern data science actually begin around the year 2000. At this time, the internet was beginning to bloom, and with it, the advent of big data. The amount of data generated from the web resulted in the new field of data science being born.
A brief timeline of key historical data science events is as follows:
We can make a few observations from this timeline. For one, the idea of data science was around for several decades before it became wildly popular. People foresaw that future society would need something like data science, but it wasn’t until the amount of digital data became so widespread and easily accessible that data science could actually be used productively. We also note that the two most widely used programming languages in data science, Python and R, existed for 15 years before the field of data science existed in earnest, after which they rapidly took off in use as data science languages.
There is another trend happening in data science, which is the rise of data science competitions. The first online data science competition organization was Kaggle.com in 2010. Since then, they have been acquired by Google and continue to grow. Kaggle offers cash prizes for machine learning competitions (often 10k USD or more), and also has a large community of data science practitioners and learners. Several other websites have appeared and run data science competitions, often with cash prizes as well. Looking at other people’s code (especially the winners’ code if available) can be a good way to learn new data science techniques and tricks. Here are most of the current websites with data science competitions:
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About this book
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You’ll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.ntroduction to Data Science
Data science is a thriving and rapidly expanding field, as you probably already know. People are starting to come to a consensus that everyone should have some basic data science skills, sometimes called “data literacy.” This book is intended to get you up to speed with the basics of data science using the most popular programming language for doing data science today: Python. In this first chapter, we will cover:
- The history of data science
- The top tools and skills used in data science, and why these are used
- Specializations within and related to data science
- Best practices for managing a data science project
Data science is used in a variety of ways. Some data scientists focus on the analytics side of things, pulling out hidden patterns and insights from data, then communicating these results with visualizations and statistics. Others work on creating predictive models in order to predict future events, such as predicting whether someone will put solar panels on their house. Yet others work on models for classification; for example, classifying the make and model of a car in an image. One thing ties all applications of data science together: the data. Anywhere you have enough data, you can use data science to accomplish things that seem like magic to the casual observer.
The data science origin story
There’s a saying in the data science community that’s been around for a while, and it goes: “A data scientist is better than any computer scientist at statistics, and better than any statistician at computer programming.” This encapsulates the general skills of most data scientists, as well as the history of the field.
Data science combines computer programming with statistics, and some even call data science applied statistics. Conversely, some statisticians think data science is only statistics. So, while we might say data science dates back to the roots of statistics in the 19th century, the roots of modern data science actually begin around the year 2000. At this time, the internet was beginning to bloom, and with it, the advent of big data. The amount of data generated from the web resulted in the new field of data science being born.
A brief timeline of key historical data science events is as follows:
- 1962: John Tukey writes The Future of Data Analysis, where he envisions a new field for learning insights from data
- 1977: Tukey publishes the book Exploratory Data Analysis, which is a key part of data science today
- 1991: Guido Van Rossum publishes the Python programming language online for the first time, which goes on to become the top data science language used at the time of writing
- 1993: The R programming language is publicly released, which goes on to become the second most-used data science general-purpose language
- 1996: The International Federation of Classification Societies holds a conference titled “Data Science, Classification and Related Methods” – possibly the first time “data science” was used to refer to something similar to modern data science
- 1997: Jeff Wu proposes renaming statistics “data science” in an inauguration lecture at the University of Michigan
- 2001: William Cleveland publishes a paper describing a new field, “data science,” which expands on data analysis
- 2008: Jeff Hammerbacher and DJ Patil use the term “data scientist” in job postings after trying to come up with a good job title for their work
- 2010: Kaggle.com launches as an online data science community and data science competition website
- 2010s: Universities begin offering masters and bachelor’s degrees in data science; data science job postings explode to new heights year after year; big breakthroughs are made in deep learning; the number of data science software libraries and publications burgeons.
- 2012: Harvard Business Review publishes the notorious article entitled Data Scientist: The Sexiest Job of the 21st Century, which adds fuel to the data science fire.
- 2015: DJ Patil becomes the chief data scientist of the US for two years.
- 2015: TensorFlow (a deep learning and machine learning library) is released.
- 2018: Google releases cloud AutoML, democratizing a new automatic technique for machine learning and data science.
- 2020: Amazon SageMaker Studio is released, which is a cloud tool for building, training, deploying, and analyzing machine learning models.
We can make a few observations from this timeline. For one, the idea of data science was around for several decades before it became wildly popular. People foresaw that future society would need something like data science, but it wasn’t until the amount of digital data became so widespread and easily accessible that data science could actually be used productively. We also note that the two most widely used programming languages in data science, Python and R, existed for 15 years before the field of data science existed in earnest, after which they rapidly took off in use as data science languages.
There is another trend happening in data science, which is the rise of data science competitions. The first online data science competition organization was Kaggle.com in 2010. Since then, they have been acquired by Google and continue to grow. Kaggle offers cash prizes for machine learning competitions (often 10k USD or more), and also has a large community of data science practitioners and learners. Several other websites have appeared and run data science competitions, often with cash prizes as well. Looking at other people’s code (especially the winners’ code if available) can be a good way to learn new data science techniques and tricks. Here are most of the current websites with data science competitions:
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