Cara menggunakan python dsa course free

6 Million+ learners

Stories of success

The AI for Leaders program was thoughtfully designed to include a high level of exposure to experts practicing in the field. Each topic was covered in enough detail to build a solid knowledge base.

Chris Hegeman

Director of Marketing Analytics & Business Intelligence- Embedded Technology/ IoT

Dell, Inc, US

The learning experience was very enriching and the mentors and advisors were very supportive all along. I'm extremely glad I signed up for the program. I definitely got what I wanted from the program and strongly recommend it.

Javier R. Olaechea

Data Solution Integration Advisor

ExxonMobil, US

I found the Data science program to have the perfect combination of relevant content, flexibility, academic rigor, and practical content that allowed me to immediately put all into practice at work.

Gabriel Arbe

Director of Operations, Latin America

VMWARE, US

The AI for Leaders program has been a great learning experience with right levels of depth and breadth across the subject. It is a well administered program which enhanced my learning experience.

Raghavendra P Setty

Vice President

Charles Schwab & Co. In, US

I believe the AIML course is of high-quality, and all the instructors are highly prepared in every topic. I also believe our Mentor is doing a fantastic job every week, and his personal insights and additions to the materials are very valuable.

Gaston Alvarado Maza

Global Category Manager

Materion Corporation,US

The data science journey was full of intense learning, but the best experience I have had. I would be willing to do it over and over again. I will highly recommend it. Thank you The University of Texas and Great Learning!

Kati Laev

Customer Service Administrator

CMT Limited UK

The program was flexible and increased my understanding of AI and the processes for integrating it into business. I would highly recommend this program to business professionals who want to understand AI and its applicability in projects better.

Hugh Hanlon

Defense Research Analyst-Associate, Booz Allen Hamilton (United States)

Booz Allen, US

The AIML program has given me solid foundational knowledge around Python, AI, and ML. The program content was very well-curated, and it covered the right things in the right depth for a program like this.

Pertuso Dryonis

Sr. Advisor Applied Data Analytics, Hess Corporation (United States))

Hess, US

And 1000+ more

                                                                            Python Data Structures

What is Python?

Python is an object-oriented language that most developers use. Most professionals who are entering the field prefer to use Python due to its versatility, simplicity, and community support. Individuals can start learning and practicing coding with Python. It has a large number of libraries that make it easy to use. 

It is an interpreted, high-level, and dynamic language that has built-in data structures, dynamic typing, and dynamic binding. Thus, making it ideal for rapid application development. It can also be used as a scripting language for connecting existing components. Due to its easy-to-learn and concise syntax, Python prioritizes readability and lowers the cost of software maintenance. Python supports modules and packages that foster program modularity and allow the reuse of code. Another great feature of Python is its interpreter and standard library, as it is free to download.

Python is popular among programmers because of the enhanced productivity it offers. The edit-test-debug cycle is extraordinarily rapid because there is no compilation step. In Python, a bug input will never result in a segmentation fault because the scripts are easy to debug. If the interpreter finds a mistake, it throws an exception. If the interpreter fails to catch the exception, it prints a stack trace. 

Why Python?

There are multiple reasons for Python being the best option. It is one of the most popular programming languages and is easy to use. It also supports machine learning applications and is fast when compared to several other languages. The syntax is easier to understand, and pre-existing libraries minimize the need to code every piece of logic from scratch.

Python is a fast compiler, and because it is java-based, programmers can use it for more than analytical research, analytical modeling, and statistical modeling. Python-based web apps can be directly linked to the analytical models running in the background.

It can be easily combined with other programming languages or platforms, thus allowing IT analysts, programmers, and developers to transfer to the analytics domain. This is possible due to the common object-oriented design of Python. 

It also offers strong documentation support due to its coding style. As a result, the following points outline why we should use Python:

• Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc.)
• Python has a simple syntax similar to the English language
• Compared to other languages, Python code can be written in fewer lines
• Prototyping can be done quickly in Python because it runs on an interpreter system that allows code to be executed as soon as it is written
• Python can be treated in a functional or object-oriented manner

Types of Data Structures in Python

Data Structures are built-in to Python and allow you to store and access data. List, Dictionary, Tuple, and Set are the names of these structures.

Users have complete control over the functionality as they can construct their data structures in Python. Some of the common data structures are Queue, Stack, Tree, and Linked List. 

1. Lists in Python

Lists are used to store data types such as text, integer, and other lists as well. Thus, they are one of the most flexible data structures in Python. 

2. Tuples in Python

Tuples are another common Python built-in data structure. These are very similar to Lists, with the exception that they are immutable. This means that no value can be added, deleted, or modified after a tuple is created.

3. Dictionary in Python

Another Python data structure for storing immutable but unordered heterogeneous things is the dictionary. This means that when you try to access the elements, they may not appear in the same sequence as when you first put them.

The way elements are stored in dictionaries, however, distinguishes them from lists. In a dictionary, we access elements by key values rather than index, as we did in a list. As a result, dictionaries include key-value pairs rather than single entries.

4. Sets in Python

Multiple instances of the same element in your list or tuple aren't always desirable. You can use a predefined data structure here. A set is a changeable, unordered collection of elements with unique values.

About This Course

If you want to learn Python Data structures online, this is ideal for a kick-start. The Python Data Structures course is 1-hour long and is presented in video format along with a quiz to test your learning on completion of the course.

Introduction to Python, basic concepts of Python such as its features and uses, List (a collection of values with same or different type), Tuple (immutable), Set (a collection of unique elements), Dictionary (having key and value pair) and Difference between Data Structures in Python are all covered in detail in the Python Data Structures course curriculum. Upon course completion, you will receive a certificate from Great Learning, which you can use on your LinkedIn page, printed resumes and CVs, and other documents.

Enroll in this free beginner Python Data Structures course right away and start learning. All the best to you and have a Great Learning!