Python is one of the most significant programming languages in the modern era. Even though the language was developed almost three decades ago, there is so much constantly evolving that it still holds immense value and a lot more to offer, especially in terms of Data Science and Artificial Intelligence.
The current version of Python 3.10 has evolved from the previous Python 2 era, and the growth of this programming language, as well as its community, is at an all-time high.
With the immense popularity achieved in Data Science and Artificial Intelligence due to the consistent developments and advancements in these technologies, there is a high curiosity to see how far these tremendous subjects will fly, especially with Python as the primary development language for them.
We will experience a new era as we have more enthusiasts who are constantly engulfing most of these modern concepts and contributing immensely to the progression of these fields. With the next year approaching rapidly, many of us have new goals to learn new intriguing topics and progress further.
In this article, our primary focus is to establish a base understanding of all essential concepts that are useful for Data Science and gain a primary understanding of how we can utilize Python becoming more proficient in the fields of Artificial Intelligence, Machine Learning, and Data Science.
We will focus on specific topics that developers should mostly focus on to achieve the best results while working on Data Science projects. If you are looking for further advanced works on improvising your Python Programming, check out the following article below to understand some of the best practices for Python.
Iterative Statements:
After gaining a brief understanding of the significance of object-oriented programming with Python, let us explore the concept of iterative statements in Python. Most programming languages like Java and C++ usually make use of quite a few iterative statements such as for loop, while loop, do-while statements, switch case, and other similar iterations.
In Python, we mostly only utilize the For loop or the While loop effectively. Most of the computations are performed with these two iterative statements. With the help of Python programming, you can run these iterative loops as long as a certain condition is satisfied (i.e., True). Hence, it becomes easy to execute a particular block of code until the required purpose is continuously satisfied.
Whether it’s Data Science or simple Python programming, iterative statements are considered a must. Almost every single project of either subject utilizes these repeating loops for the execution of a specific task. Most of my projects from my previous articles also make use of these statements. One of the best examples of the following is from one of my previous blogs for creating a language profanity tester. Check out the code below and visit the following article for more information on the same.
sentence = "You are not only stupid , but also an idiot ."
def censor(sentence = ""):
new_sentence = ""
for word in sentence.split():
if word in Banned_List:
new_sentence += '* '
else:
new_sentence += word + ' '
return new_sentence
OOPs:
Python is an object-oriented programming language, and it is one of the most essential aspects of Python. However, this feature is sometimes neglected because of the other amazing features of Python. Hence, this topic should be our primary focus for getting started with Python for Data Science. When working with numerous aspects of Python, it is sometimes possible to forget the significance of object-orient programming.
Every library for machine learning, data science, or any deep learning framework built-in Python will constitute mainly two essential primary components, namely objects and classes. Real-world entities like classes, encapsulation, polymorphism, and inheritance are also implemented quite well in Python. Hence, our goal is to understand all the concepts in extreme detail, and we will explore these concepts in-depth in the next article.
Below is some quick starter code for getting started with classes. Check out the following article for further information about this code block.
class Derivative_Calculator:
def power_rule(*args):
deriv = sympy.diff(*args)
return deriv
def sum_rule(*args):
derive = sympy.diff(*args)
return derivdifferentiatie = Derivative_Calculator
differentiatie.power_rule(Derivative)
Lists:
A list is a mutable ordered sequence of elements. Mutable means that the list can be modified or changed. Lists are enclosed within Square Brackets ‘[ ]’. Lists are a type of sequenced data structure with each element in the list being assigned a specific index number by which it can be accessed. Each item or element in a list is separated by a comma (,).
lst = ['one', 'two', 'three', 'four']
lst.append('five')
lst
Output:
['one', 'two', 'three', 'four', 'five']
The append functions is one of the most significant command that is utilized in the world of programming and data science. There are several other functions that we can perform and manipulate on lists. To learn more about the other options that are available to use, I would highly recommend checking out a detailed version of mastering lists with Python programming from the link provided below.
Dictionaries:
Dictionaries allow users to access keys and values accordingly. Let’s say you had to store some data of a person, then dictionaries are something you would consider using, such as storing a contact name along with their number. Dictionaries can also store multiple data elements associated with the particular. A specific name of a student in a school can have marks of numerous subjects stored. Dictionaries are data structures in Python that are defined as an unordered collection of data. Below is some sample code and output to get started with dictionaries.
# Return a list of tuples of the dictionary items in the (key, value) form
my_dict = {1: 'A', 2: 'B', 3: 'C'}
print(my_dict.items())
# Return a new view of the dictionary keys
my_dict = {1: 'A', 2: 'B', 3: 'C'}
print(my_dict.keys())
# Return a new view of the dictionary values
my_dict = {1: 'A', 2: 'B', 3: 'C'}
print(my_dict.values())
Output:
dict_items([(1, 'A'), (2, 'B'), (3, 'C')])
dict_keys([1, 2, 3])
dict_values(['A', 'B', 'C'])
The above starter code should allow the users to get a brief understanding of some of the elementary concepts of how to use dictionary values and key elements. If you are looking forward to an extended guide on dictionaries and sets, I would recommend checking out the following article to gain more knowledge about these topics.
Functions:
Functions allow users to quickly manipulate repeatable tasks within a code block under the def function name(): command. This concept is extremely useful in programming, especially data science, where you will need to repeat specific actions over large sets of data. Utilizing a function for achieving this goal will reduce the high amount of computations that the developer will need to perform.
Python also allows its users to directly access some of its anonymous (or advanced) function options that will help to develop your projects quicker with higher efficiency. I have already covered the following topic in immense detail in another article, and I would recommend checking it out if you are interested in exploring this topic further. The link for the same is provided below.
Understanding Advanced Functions In Python With Codes And Examples!
Exploring Python Libraries For Data Science:
The best feature about Python is the enormous number of libraries that are available for this programming language. For almost every type of task that you want to perform or any type of project that you want to work on, Python offers a library that will simplify or reduce the work by a humungous amount.
With the help of some of the best Data Science libraries offered by Python, you can complete any type of task that you are aiming to achieve. Let us explore some of the must-know libraries for beginners in Data Science.
1. Pandas:
For working with data science, one of the primary requirements is to analyze the data. One of the best libraries that Python offers its users is the Pandas library, through which you can access most of the content that is available on the internet in a structured format. It provides the developers with an option to access numerous files in various formats such as text, HTML, CSV, XML, latex, and so much more. Below is one of the examples with which you can access the CSV format type data.
data = pd.read_csv("fer2013.csv")
data.head()

To understand more about Pandas and conquer the analytics utility behind this library, I would recommend checking out one of my previous articles on the fourteen most essential Pandas operations that must be included in every data scientist’s arsenal. Below is the following link for the same.
2. Matplotlib:

Once you finish analyzing your data, the next essential step is to visualize them accordingly. For the visualization of data, matplotlib alongside seaborn is one of the best options available in Python. You can visualize almost any essential entity with this fantastic library with simplistic codes. It supports numerical extensions like NumPy, which you can combine together to visualize most of the data elements.
The above image representation shows a bar graph constructed with the help of the matplotlib library. There are several more visualizations, graphs, and other statistical visuals that we can perform with matplotlib. To learn more about the different types of visualization for Data Science projects, check out the following link provided below.
8 Best Visualizations To Consider For Your Data Science Projects!
3. NumPy:
Numerical Python or NumPy, in short, is one of the best options available in Python for the computation of mathematical problems. You can utilize the concept of numpy arrays for simplifying the complex math that is involved in the field of data science. It helps you in handling large, multi-dimensional arrays and matrices and in the efficient construction of your data science projects.
Without the proper utility of numpy, it becomes almost impossible to solve most of the complex math problems and machine learning projects. Hence, it is essential to understand this concept in great detail. It is recommended that the viewers check out the following article below on fifteen numpy functionalities that every data scientist must understand.
15 Numpy Functionalities That Every Data Scientist Must Know
4. Scikit-learn:
Scikit-learn is one of the best libraries with which you can implement all the essential machine learning algorithms such as classification, regression, clustering, preprocessing (as shown in the below code), model selection, dimensionality reduction, and so much more. The library toolkit makes use of simplistic but highly efficient tools for the analysis and computation of data. It is not only simple to install like the other three previously mentioned modules, but it is also built on top of these crucial packages like matplotlib, numpy, and scipy. For beginners, this open-source tool is a must-learn to implement machine learning projects more effectively.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(questions, response, test_size=0.20)
5. NLTK:
Natural language toolkit is one of the best libraries to deal with human language data. In the beginning, most machine learning and data science projects will deal with handling a lot of natural languages processing tasks. Cleansing your data is one of the most essential steps that is required in the data preparation stage for most problems related to natural language processing. Hence, this library is extremely critical to learn and get a hang of on if you are getting started with the field.
import nltk
sentence = "Hello! Good morning."
tokens = nltk.word_tokenize(sentence)
If you are looking into the image processing side of things, then the computer vision library Open-CV is something that is highly recommended. Check out the complete guide to the following library from the following link below.
OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!
Conclusion:

"Code is like humor. When you have to explain it, it’s bad." – Cory House
Python is a revolutionary programming language as it has managed to stay relevant over the decades due to its simplicity, ease of learning, versatility, and many other fabulous features. With the emergence of Artificial Intelligence and Data Science in the past few years, Python has created a massive reputation for itself being one of the dominant languages in these fields and something that everyone must seek to grasp eventually.
In this article, we covered most of the essential concepts to get started with Python for becoming more proficient at Data Science. We focused on most of the elemental topics in Python that find tremendous utility in most areas of Data Science and will be helpful for the successful completion of most projects. If you are able to master all of the methodologies mentioned in this piece, you will be able to cruise through most of the basic Data Science projects with ease.
If you want to get notified about my articles as soon as they go up, check out the following link to subscribe for email recommendations. If you wish to support other authors and me, then subscribe to the below link.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles in relation to the topic covered in this piece that you might also enjoy reading!
Generating QR Codes With Python In Less Than 10 Lines
5 Best Python Projects With Codes That You Can Complete Within An Hour!
Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!