mastering-python-for-data-science-in-7-simple-steps

Mastering Python for Data Science in 7 Simple Steps

13 mins read

The data science landscape has changed dramatically in the past few years, but one of the most significant changes has been the shift from R to Python as the language of choice for most data science professionals.

So why did Python take over R’s throne? While there are many reasons, here are 7 steps to mastering Python for data science that will help you get started with your own explorations into data analysis and interpretation.

Python is one of the most common programming languages used in data science. It’s simple and straightforward, and its syntax makes it easy to read and write as well as learn quickly.

If you’re looking to learn Python quickly, this step-by-step guide will give you the big picture of what you need to know to become an intermediate Python coder for data science applications.

Learn Python

Python is a high-level object-oriented programming language released in 1991. Python is very interpretable and efficient. Thanks to its ingenuity, Python is versatile and suitable for data science. I started with languages ​​such as C, C ++ and Java.

When I finally came across Python, I found it to be very elegant, easy to learn, and easy to use. Python is the best way to get started with machine learning for everyone, including those with no programming or coding language experience. Python has some flaws, for example, it’s considered a “slow” language, but it’s still one of the best languages ​​for AI and machine learning.

There are various other languages, such as Julia and Golan, that may well compete with Python in the coming years, but the latter is better for now. The main reasons Python is so popular in data science despite other languages ​​like R.Data Scientist Jobs are quite tougher one to get place.

Understand the Language

As mentioned earlier, Python is a simple language and is generally consistent.

It’s rapidly growing in popularity compared to other programming languages, making it suitable for novice programmers.

We have a wealth of resources on various libraries and frameworks to support data science. Versatility and platform independence. This means that Python can also import important modules written in other programming languages.

There is a great community that is constantly updated. The Python community is generally full of great people who are constantly updating to improve Python. You can download it here to get started with Python.
Understanding the basics of the Python programming language is arguably the most important aspect of mastering Python.

There are many important concepts such as keywords and identifiers, variables, iterative statements such as “for” loops and “while” loops, comment lines, control statements, and so on. If you try to cover most of the topics mentioned above, this article will be too huge.

Therefore, this section will endeavor to address some of the more important issues. In the future, I’ll try to write another article that covers the complete Python roadmap.

Step 1: Learn Basic Variables

In Python, variables are created when you assign a value to them. The assignment operator is the equal sign (=). You can also use the plus (+) and minus (-) operators to add or subtract values from a variable. To learn more about variables, including how to create and use them, check out this tutorial. -Assignment Operators: =, +, – 

-String Variable: str 

-Numeric Variable: int , float, bool

-Int (Integers): an integer variable that has no decimal places 

-Float (Floating Point Numbers): a number with decimal places that uses either the decimal point (‘.’) or the exponential notation (‘e’ or ‘E’) 

-Bool (Boolean): can be either True or False

Step 2: Get Familiar with Numpy

Numpy is a powerful tool for scientific computing in Python. It’s fast and efficient, and it allows you to work with large data sets. In order to use Numpy effectively, you need to be familiar with its core features. Here are seven simple steps to get you started: 

1. Learn the basics of NumPy arrays. 

2. Get comfortable with indexing and slicing NumPy arrays. 

3. Understand the difference between NumPy arrays and regular Python lists. 

4. Learn how to use NumPy functions and methods. 

5. Get familiar with NumPy’s linear algebra capabilities. 

6. Use NumPy to load and manipulate data from files.

7. Become acquainted with some advanced topics like multi-dimensional arrays, broadcasting, and memory efficiency optimization.

Numpy makes many tasks easier in Python that would otherwise require writing code in another language like C or Fortran. It has comprehensive documentation on its website, so spend some time there if you have any questions!

credits: freecodecamp.org

Step 3: Explore Pandas Data Structures

The Pandas library is built on top of NumPy and provides easy-to-use data structures and data analysis tools for Python. In this step, you’ll learn about the different types of data structures that Pandas offers and how to use them effectively. First, we need to load the library: import pandas as pd. Next, we’ll look at two important data structures: Series and DataFrame.

A Series stores a list of values with a common index variable (usually integers). You can create a Series by either passing it a list or assigning it from another Series or DataFrame.

A DataFrame stores tabular data with columns of potentially different types that are related by one or more index variables (usually strings). It’s similar to an Excel spreadsheet or SQL table. You can create a DataFrame from scratch using its constructor function; however, it’s usually easier to load an existing dataset from CSV file into a DataFrame first and then explore its various components.

Step 4: Work with Pylab

Pylab is a powerful tool that can help you with your data analysis and visualization. In this step, we’ll show you how to use Pylab to make your data look its best. First, we’ll import the necessary libraries. Then, we’ll load our data into Pylab. Next, we’ll clean up our data. Finally, we’ll visualize our data using Pylab’s built-in plotting functions. Let’s get started!

Step 5: Use NumPy, SciPy, and Matplotlib to Create Visualizations

Python’s visualization libraries are incredibly powerful and versatile. In just a few lines of code, you can create complex visualizations that would take hours to create by hand.

NumPy, SciPy, and Matplotlib are the three most popular libraries for creating data visualizations. In this step, we’ll show you how to use them to create stunning visualizations that will help you better understand your data. Let’s start with an example using matplotlib, which is the simplest of all three libraries: import matplotlib.pyplot as plt; plt.figure(); plt.title(Histogram) ; x =  = 1;

You may have noticed that matplotlib plots its graphs on a separate window (rather than inline on our web page). You can fix that by adding one line of code: h = plt.figure() . This saves us from having to open another tab or window every time we want to see our plot!

Step 6: Understand How Machine Learning Works

Machine learning is a subset of artificial intelligence that deals with the creation of algorithms that can learn and make predictions from data. Machine learning is mainly used to make predictions about future events, such as whether a customer will churn or not.

In order to understand how machine learning works, you need to understand the different types of machine learning algorithms. There are three main types of machine learning algorithms: supervised, unsupervised, and reinforcement learning.

Supervised learning algorithms are trained on data that has been labeled by humans. Unsupervised learning algorithms are trained on data that has not been labeled. Reinforcement learning algorithms are trained on data that is both labeled and unlabeled.

Step 7: Explore Big Data

When it comes to data science, there’s a lot of data out there. And it can be overwhelming to try and tackle it all at once.

That’s why it’s important to start small and explore one big data set at a time. By doing this, you’ll gradually build up your skills and knowledge so that you can eventually tackle any big data set.

Plus, you’ll have a lot more fun along the way! So what is big data? It’s an ever-growing pile of digital records, generated by both people and machines alike. Some examples include social media posts, transactions on e-commerce sites, log files from web servers and mobile apps—just to name a few.

These huge piles of data are difficult to manage on their own but with enough processing power they can reveal insights about anything imaginable:

What are people saying about our product? 

What does our customer base look like? 

What trends do we see across the board? 

How do I find new customers? 

The possibilities are endless! But before jumping right into the deep end, make sure you’re ready. To get started with Big Data just follow these seven steps and then begin exploring!

credits: unsplash

Verdict

These are the

  1. Python is an unambiguous, easy-to-read, general-purpose high-level programming language which considers paradigms of structured, procedural, and object-oriented programming.
  2. Python is a widely used high-level interpreted language that is known for its ease of use and readability. 
  3. It has a large standard library that covers areas such as string processing, Internet protocols, operating system interfaces, and much more. 
  4. In addition to the standard library, there are many modules and packages available that allow you to do even more with the language. 
  5. Python is also available for many different platforms including Windows, Linux/Unix, Mac OS X, and more.
  6.  Python has powerful built-in data structures that include lists, dictionaries, and tuples.

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