Applied Data Science with Python
Python is defined as “an interpreted, object-oriented, high-level coding language having dynamic semantics” by its creators. Its high-level built-in database structures, together with dynamic coding and binding, make it very appealing to use as a script or glue language to link existing components.”
Python is a general-purpose computer language that can utilize to create both web & desktop applications. It can use to develop complex numerical and scientific applications. With this kind of versatility, it’s no wonder that python is among the world’s fastest-growing computer languages. But what when we use Python language in applied data science? Is applied data science with python a good combination?
Keep reading the article, learn and explore more about applied data science with python specialization, and many more. You will also learn more about applied data science with python free course.
What is Python?
Python is the computer programming language frequently used to create sites and software, organize tasks, and analyze data. Python is a primary-purpose programming language, which means it can generate a wide range of programs and is not specialized for any particular problem. This versatility, combined with its ease of use for beginners, has rendered it one of the most widely used computer languages today. According to a survey by the market analyst firm RedMonk, it will be the second-most prevalent programming language amongst developers in 2021. It will be a great combination of applied data science with python IBM.
Python frequently uses for website and software development, task automation, data processing, and visualization. Due to its ease of learning, python has been implemented by many non-programmers, such as accounting professionals and researchers, for a range of everyday tasks, such as financial organization.
What is Applied Data Science?
Data Science is an emerging domain that has progressed from a ‘nice-to-have’ to a must-have in operating a customer-focused provider or business over the last decade. As with any developing field, its boundaries are being nudged, skill sets are being reinvented, and preconceived notions about just what a “Data Scientist” is undermined. Unsurprisingly, businesses have restructured to make the best possible use of this game-changing resource.
As a result, an Applied Data Scientist — a new job description — has emerged. But is this the case? It has been on the frontier for a long time, has undergone name changes, and remains one of the industry’s most significant and widely obligated skill sets. Therefore, applied data science with python is a great mixture and a future step toward success. Previously known as ‘Data Analysts,’ they are now occasionally referred to as ‘Computer Scientists’ or ‘Applied Data Scientists‘ in some organizations.
Applied Data Science with Python Specialization Track
There are 5 courses in Applied Data Science with Python Expertise. Each course focuses on a different aspect of using python for data science.
You will receive a certificate of completion for each course after successfully completing all five courses. These certificates can be added to your portfolio and LinkedIn profile. You can also learn applied data science with python. IBM has many other courses.
Here is the detail of each of the 5 courses from Coursera about applied data science with python free course.
Course-1: Introduction to Applied Data Science with Python
As the title suggests, “Intro to Applied Data Science with Python,” we expected this course to refresh my Python knowledge, but we were mistaken. Before enlisting in this course, you should be comfortable with python.
Don’t mistake this for a Python beginner’s course. More enhanced Python notions will be taught in this course. This course will teach you Numpy and Pandas, two significant Python data cleanup and handling toolkits. This course would also teach you the fundamentals of Pandas, such as how to hold data in Data Frames and query information stored in Data Frames. This course is a fundamental part of applied data science with python specialization.
This course will cover more advanced subjects like merging, data clustering, and data manipulation. This course will also initiate you to Jupyter Notebook.
Course-2: Applied Charting, Data, and Charting Representation with Python
After learning data manipulation and storage tools in Course 1, you would then learn something new in this course: data visualization. This course covers a wide range of data visualization theories. This course will teach you about the matlab and Seaborn libraries and how to develop innovative visualizations with python.
This course’s assignments will require you to conduct searches on Google and Stack Overflow.
Course-3: Applied Machine Learning with Python
This is a basic introduction to monitored machine learning methods. This course covers a wide range of Machine Learning concepts such as correlation, classification, clustering, artificial neural, and many others. You will also learn about important scikit-learn deep learning frameworks in this course.
This course will discuss both regression and classification-controlled techniques. These methods can help you understand and boost the effectiveness of your models.
Course-4: Applied Text Mining with Python
Coursera must take this course after completing all the above courses in Applied data science with python. This course will teach the fundamentals of text mining & text deception. The course begins with an analysis of how python handles text, the structure of text to both machines and humans, and a summary of the nltk text manipulation framework.
The 2nd week focuses on general manipulation requirements, such as regex (searching for text), text cleanup, and text preparation to be used by machine lessons. The third week will illustrate how the classification method is achieved by applying basic natural language techniques to text. The final week will look at a more advanced technique for identifying topics in documents and categorizing them based on similarity (topic modeling). Moreover, it is the best applied data science with python free course available in 2023 from Coursera.
Course-5: Applied Social Network Analysis with Python
We found this course a little more complicated than the other coursework in this specialization program. The NetworkX library will be used to teach you the fundamentals of network analysis in this course. This course will teach theoretical concepts about network analysis and explain how each topic uses in real-world networks.
This course’s final assignment was enjoyable because it allowed me to experiment with various models and analyze their performance.
After completing the above 5 courses, you will be able to apply for different jobs in applied data science with python specialization.
The Applied Data Science with Python course teaches you Data Science with Python at an intermediate level. So, once you’ve completed these specialized skills, you’ll need to work on projects using the skills you managed to learn in the five courses and broaden your portfolio with additional unique projects.
Only those with sufficient Python knowledge and a desire to learn more advanced applied data science with Python free course should pursue this specialization. Please do not attempt this if you are a newbie in python. Python for Everyone Specialization or other excellent Python courses is a good place to start. Comment if you have questions related to applied data science with python track and others.