Coursera Applied Data Science with Python Review 2024

Looking for the best applied data science course aren’t you? We’ll you’re in the right spot. We’ve taken the class for you so you can learn about our Coursera Applied Data Science with Python review.

You should consider learning applied data science if you’re on the verge of changing or upgrading your career. Coursera applied data science with python specialization certification is one of the best online courses out there.

This course of Applied Data Science with Python is offered by the University of Michigan by collaborating with Coursera. Before you read the complete review of this course. Let us learn what is applied data science.

FAQ on Coursera Applied Data Science with Python Review

5 Courses in Applied Data Science with Python Specialization Certification 

This specialization certification course comprises of 5 courses where learners will learn data science using Python programming language. You can start this course if you have a basic understanding of Python programming language.

If you’re not familiar with the Python programming, subscribe to this Python for Everybody course before getting started with Applied Data Science.

Using Python programming language learners will use various Python libraries for data science such as Matplotlib, Scikit-learn, Pandas, nltk, and network to gain insight into their data.

Below are the five courses offered by the University of Michigan for Applied Data Science with Python Specialization.

1. Introduction to Data Science in Python
2. Applied Plotting, Charting & Data Representation in Python
3. Applied Machine Learning in Python 
4. Applied Text Mining in Python
5. Applied Social Network Analysis in Python

Let’s review what each course of Applied Data Science with Python Specialization offers.

1. Introduction to Data Science in Python

In the first course, you will be provided a refresher training on Python programming language. Although Python is the prerequisite for the Applied Data Science specialization course, this refresher training will provide insight into using them for a data science course.

Learners will be able to grasp some concepts such as lambdas and NumPy, both are popular Python libraries for data science.

You will learn data manipulation and cleaning techniques using Python’s Panda Library, the abstraction of the Series and DataFrame, a guide to use functions such as merge, groupby, and pivot tables.

By the end of this course, you’ll are ready to take the tabular data, clean it, manipulate it, and finally run the basic inferential statistical analyses.

2. Applied Plotting, Charting & Data Representation in Python

Data representation is essential to become successful in the field of data science. 

In the second course of applied data science with python specialization, you’ll use the Python Matplotlib library to learn visualization basics. Where the visualization basics will focus on reporting and charting.

You’ll also learn about bad visualization and good visualization techniques. And how statistical measures translate to get a good visualization.

The later part of the course will teach you how to make a visualization using Python and the best practices to follow while creating charts. By learning the best practice of data visualization using Python you’ll be able to identify a specific problem and tackle them with a specified method.

3. Applied Machine Learning in Python 

The third course of Coursera applied data science with python focuses on machine learning concepts. You’ll learn applied machine learning that will focus on methods and techniques for statistics.

The course will begin with the discussion of how machine learning is different and learn in-depth about the Python library Scikit-learn toolkit. This course will concentrate on the supervised learning approach of machine learning to create predictive models. 

By the end of this course, you’ll learn more advanced topics of machine learning techniques. Learners will be able to identify and tell the difference between the unsupervised learning and supervised learning technique. They will also be able to identify and apply the technique needed for a particular dataset.

4. Applied Text Mining in Python

The fourth course of Coursera Applied data science with Python review will introduce you to the basics of text mining and text manipulation. 

Learners will learn and understand how Python used to handle texts, the structure of text for both humans and machines, and an overview of the nltk framework to manipulate text.

By the second week, this course will focus on manipulation needs, cleaning text, and preparing text to use in the machine learning process. Next, you will learn basic natural language processing.

Natural language processing will be used to process different types of text and see how text classification is accomplished.

5. Applied Social Network Analysis in Python

The final part of the Coursera applied data science with python review course will provide an intro to network analysis. 

Here you will go through tutorials using the NetworkX library. The course starts with the basics of network analysis and motivations for why you might use networks. Then you’ll learn the network robustness and concept of connectivity. 

Learning the importance of the node is essential in the concept of network. Finally, this course covers the evolution of networks, models of network generation, and link prediction problem.

Summary

Coursera Applied Data Science with Python review has provided insight into what this course is offering. Since there are no prerequisites to learn applied data science, anyone can subscribe to this course.

After completing the course, you should consider taking additional Data Science projects from Coursera. This helps you to experience more hands-on practice sessions.

Since this is a specialization course consisting of 5 courses, this also comes with a hands-on project. To earn the Applied Data Science certificate from Coursera, you must complete the hands-on project.

You can display the certificate on professional networks such as Linkedin or you can share with your employers. The certificate is the gateway for your career change or upscaling your career. 

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