Python is an ideal first language for data science. It's easy to learn, you can use it on almost any platform, and it has a wide variety of libraries that support machine learning and other aspects of data science. In this post, we'll discuss why python is great for beginners who want to get started with data science before moving on to more advanced topics like machine learning algorithms and deep learning frameworks.
Why Python?
Python for data science is a popular programming language widely used. It is easy to learn, easy to read and write, fast, and offers a large collection of libraries for data analysis. Python has a large community of users which makes it possible to get help with any problems you might encounter.
Python is also known as a general purpose language, meaning that it can be used in many different tasks including web development, system administration, scientific computing, and more!
Getting Started
The first step to using Python in a data science project is installing the language and related modules. There are two ways to do this:
The first method is to download Python directly from the official site (https://www.python.org/downloads/). This will give you access to all recent versions of Python and tools like Jupyter Notebooks, which we'll discuss later on in this article. The second option involves finding pre-compiled binaries for your operating system (OS) on the Anaconda distribution website (https://repo.continuum.io/archive). Anaconda has prepackaged everything you need for most standard projects, so it's easy to install on most major platforms without having to worry about compatibility issues or missing dependencies between packages during installation processes
Variables and Objects
Variables and objects are the containers that hold data. Variables hold a single piece of information, while objects can hold multiple pieces of information and have methods (functions) to perform operations on them.
A variable has a name, an optional type, and a value assigned to it:
>>> myName = 'Tristan' # A string object is created with a value of “Tristan”
>>> myAge = 28 # An integer object is created with a value of 28
The same goes for objects:
>>> person1 = {'name':'John', 'age':31}# An object named person1 is created with keys ‘name’ and ‘age’
Objects can be accessed using dot syntax or bracket syntax:
Data Types for Data Science
Data types in Python are used to define the type of a variable, i.e., what kind of data it can store. Data types can be divided into three categories: numeric, string, and other (None). Numeric types represent numbers with decimal points while string types represent text or characters. The other category contains two values: None and True/False.
The data type is determined at the time you create a variable using a statement like n = 3 or s = “Hello World!”; where n is assigned an integer value 3, whereas s is assigned a string value containing "Hello World!". Here we have created two variables called n and s respectively; these variables are instances of different objects that belong to their respective classes (int and str).
By default there is no explicit declaration on how large or small your integer numbers should be stored by Python - this happens automatically based on how much memory you want your program consumes. If you want some control over how big your integers are then you need to declare them explicitly with one of the following formats: int(x) (Integer from x) long(x) (Long from x) float(x) complex(y+zj)
Python Lists
Lists are a collection of items. They can be any type of Python object, and they're mutable—meaning you can change their contents at any time!
You'll use lists for storing data, information about your data (e.g., the mean, or standard deviation), or even other lists. To see how flexible lists are, let's take a look at how to create one:
>>> my_list = [2, 3]
This creates a list containing two integers: 2 and 3. You can access individual elements using square brackets:
>>> print(my_list[0]) # Prints '2'
2
Functions
Functions are a fundamental part of the [Python] language. They enable you to define reusable code, which is one of the most powerful features of Python. With functions, you can write code that you only have to write once and then call whenever needed instead of having to rewrite it each time.
You define a function by using the def keyword followed by your function’s name and brackets ([]) containing its parameters (arguments), if any, followed by a colon (:) character at the end of the line. For example:
>>> def my_func(a, b):
... print("My function was called with", a, b)
Running this code won’t do anything until we call our function from another part of our program:
>>> my_func(10, 15) #call our function with 10 and 15 as arguments here
My function was called with 10 15
Notice how when we run this program we get an error message because we haven’t passed in any arguments yet! Try running these lines instead:
>>> my_func() # no args passed so it will return an empty list with no errors
The output should look something like the below:
[].
Packages
Packages are a way of organizing code, and they're also used to share code. When you think about it, packages are similar to the folders on your computer—they're a place in which related files can be stored and accessed easily.
Packages are essential for data science because they allow you to import modules into your program without having to worry about where those modules were originally stored. This means that if you find a module online that does some useful work (like plotting), then all you need do is import it into your program and use it right away!
There are many packages available today for data science, including pandas (for manipulating lists), matplotlib (for plotting), NumPy (for mathematical operations), sci-kit learn (a machine learning library), and others.
Python is a great first language for data science.
Python is one of the most popular languages used in data science. It's a high-level language that can be used for many different tasks, including web development and scientific computing. Python is considered a general-purpose language because it can do so much: It has been used to write desktop apps, web apps, business software, and games—and now you can use it for exploring data as well!
Python has several advantages over other tools for learning about data science:
It's easy to learn. Many people find that they're able to pick up programming concepts more quickly when using Python compared with other languages like Java or C++. The reason behind this is that Python doesn't require much knowledge before you start writing programs—you don't need to know how memory works or what pointers are! You just need some basic concepts such as variables (which store information), functions (which perform actions), and conditionals ("if" statements).
It's easy to read and write code in Python because there are only 26 characters available in its alphabet; no complex curly braces here! This makes reading through someone else's code less intimidating than looking at something written in another language that might have hundreds of characters available for use but only uses one type of syntax (e.g., curly braces).
Become an Expert at NumPy
NumPy is a fundamental part of data science, so it’s important that you understand it. You should know how to use NumPy arrays and matrices, which are two-dimensional data structures. You also need to know about the wide range of functions available in NumPy—many other packages rely on this functionality.
To understand Data Science you need to know to program, and python is a great language to learn.
Python is a great language to learn for people who want to get into data science and machine learning. Python is easy to learn, yet powerful enough to handle complex tasks easily. The community around this language is big and growing like crazy, with many opportunities for python developers.
Conclusion
Python can be used for a variety of purposes, but one of its biggest draws is that it's easy to learn. You can easily learn Python for Data Science Course by SkillUp Online. The syntax is simple and consistent, and there are many resources available online for learning more about Python programming in general or specific coding techniques. Because some data science tasks require knowledge about statistics and mathematics, this article covers both topics (as well as others) in detail so that you can get started with data science right away!