Brief Introduction to Data Science and Python Basics

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Introduction to Data Science

Chapter 1!

1.1 WHAT IS DATA SCIENCE?

Data science allows us to adopt four diﬀerent strategies to explore the world using data: !

1. Probing reality. Data can be gathered by passive or by active methods. In the latter case,

data represents the response of the world to our actions. Analysis of those responses can

be extremely valuable when it comes to taking decisions about our subsequent actions.

One of the best examples of this strategy is the use of A/B testing for web development:

What is the best button size and color? The best answer can only be found by probing the

world. !

2. Pattern discovery. Divide and conquer is an old heuristic used to solve complex problems;

but it is not always easy to decide how to apply this common sense to problems. DatiÞed

problems can be analyzed automatically to discover useful patterns and natural clusters

that can greatly simplify their solutions. The use of this technique to proÞle users is a critical

ingredient today in such important Þelds as programmatic advertising or digital marketing. !

3. Predicting future events. Since the early days of statistics, one of the most important

scientiÞc questions has been how to build robust data models that are capable of

predicting future data samples. Predictive analytics allows decisions to be taken in

response to future events, not only reactively. Of course, it is not possible to predict the

future in any environment and there will always be unpredictable events; but the

identiÞcation of predictable events represents valuable knowledge. For example, predictive

analytics can be used to optimize the tasks planned for retail store staﬀ during the following

week, by analyzing data such as weather, historic sales, traﬃc conditions, etc.!

4. Understanding people and the world. This is an objective that at the moment is beyond

the scope of most companies and people, but large companies and governments are

investing considerable amounts of money in research areas such as understanding natural

language, computer vision, psychology and neuroscience. ScientiÞc understanding of

these areas is important for data science because in the end, in order to take optimal

decisions, it is necessary to know the real processes that drive peopleÕs decisions and

behavior. The development of deep learning methods for natural language understanding

and for visual object recognition is a good example of this kind of research. !

Toolboxes for Data Scientists

Chapter 2!

2.2 WHY PYTHON?

Python is a mature programming language with excellent properties for newbie programmers,

making it ideal for those who have never programmed before. Some remarkable properties of

Python include easy-to-read code, suppression of non-mandatory delimiters, dynamic typing,

and dynamic memory usage. Python is an interpreted language, allowing immediate execution

of code without the need for compilation to machine language. Python oﬀers interactive

consoles like IPython for a richer coding environment. Python is a ßexible programming

language and can be seen as a multiparadigm language, supporting object-oriented

programming and functional programming. Python has a large ecosystem of scientiÞc libraries,

making it a speciÞc platform for data scientists.!

2.3 FUNDAMENTAL PYTHON LIBRARIES FOR DATA SCIENTISTS

NumPy, SciPy, Pandas, and Scikit-Learn. !

2.3.1 NUMERIC AND SCIENTIFIC COMPUTATION: NUMPY AND SCIPY

NumPy provides support for multidimensional arrays along with basic operations on them and

useful linear algebra functions. SciPy provides a collection of numerical algorithms and

domain-speciÞc toolboxes, including signal processing, optimization, statistics, and more.!

SciPy includes the plotting library Matplotlib, which oﬀers various tools for data visualization.!

2.3.2 SCIKIT-LEARN: MACHINE LEARNING IN PYTHON

Scikit-learn is a machine learning library. It is built from NumPy, SciPy, and Matplotlib.!

Scikit-learn provides simple and eﬃcient tools for various common tasks in data analysis,

including classiÞcation, regression, clustering, dimensionality reduction, model selection, and

preprocessing.!

2.3.3 PANDAS: PYTHON DATA ANALYSIS LIBRARY

Pandas provides high-performance data structures and data analysis tools. The key feature of

Pandas is the fast and eﬃcient DataFrame object for data manipulation with integrated

indexing. The DataFrame structure can be seen as a spreadsheet, oﬀering ßexible ways of

working with data, such as reshaping, adding, or removing columns or rows. Pandas oﬀers

high-performance functions for aggregating, merging, and joining datasets. It has tools for

importing and exporting data from various formats, including CSV, text Þles, Excel, SQL

databases, and HDF5. Pandas handles missing data and intelligent data alignment in the data.

Pandas provides a convenient Matplotlib interface for data visualization.!

2.5 INTEGRATED DEVELOPMENT ENVIRONMENTS (IDE)

In general, the basic pieces of any IDE are three: the editor, the compiler, (or interpreter) and

the debugger. Some IDEs can be used in multiple programming languages, provided by

language-speciÞc plugins, such as Netbeans7 or Eclipse.8 Others are only speciÞc for one

language or even a speciÞc programming task.

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