LinkedIn Learning has compiled 10 Master Python for Data Science courses with a free trial month included to help students learn Data Science using Python while saving a buck.
The learning path helps students quickly learn the general programming principles and methods for Python, and then begin applying that knowledge to using Python in data science-related development.
The ‘Master Python for Data Science’ learning path includes 10 different courses and provides a certificate of completion upon each course completion.
1. Learning Python – Click here to Enroll
Python—the popular and highly readable object-oriented language—is both powerful and relatively easy to learn. Whether you’re new to programming or an experienced developer, this course can help you get started with Python.
In this course, Joe Marini provides an overview of the installation process, basic Python syntax, and an example of how to construct and run a simple Python program. Learn to work with dates and times, read and write files, and retrieve and parse HTML, JSON, and XML data from the web.
2. Data Ingestion with Python – Click here to Enroll
A sizable portion of a data scientist’s day is often spent fetching and cleaning the data they need to train their algorithms. In this course, learn how to use Python tools and techniques to get the relevant, high-quality data you need.
In this course, Instructor Miki Tebeka covers reading files, including how to work with CSV, XML, and JSON files. He also discusses calling APIs, web scraping (and why it should be a last resort), and validating and cleaning data. Plus, discover how to establish and monitor key performance indicators (KPIs) that help you monitor your data pipeline.
3. Python Essential Training – Click here to Enroll
Due to its power and simplicity, Python has become the scripting language of choice for many large organizations, including Google, Yahoo, and IBM. A thorough understanding of Python 3, the latest version, will help you write more efficient and effective scripts.
In this course, Bill Weinman demonstrates how to use Python 3 to create well-designed scripts and maintain existing projects. This course covers the basics of the language syntax and usage, as well as advanced features such as objects, generators, and exceptions. Learn how types and values are related to objects; how to use control statements, loops, and functions; and how to work with generators and decorators. Bill also introduces the Python module system and shows examples of Python scripting at work in a real-world application.
4. Python for Data Science Essential Training Part 1 – Click here to Enroll
Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python.
In this course, instructor Lillian Pierson takes you step by step through a practical data science project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate interactive graphs using the Plotly library. You should walk away from this training with basic coding experience that you can take to your organization and quickly apply to your own custom data science projects.
5. Python for Data Science Essential Training Part 2 – Click here to Enroll
Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python.
In this course, instructor Lillian Pierson takes you step by step through a practical data science project: building machine learning models that can generate predictions and recommendations and automate routine tasks. Along the way, she shows how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools such as neural networks and Bayesian models. You should walk away from this training with hands-on coding experience that you can quickly apply to your own data science projects.
6. Data Science Foundations: Python Scientific Stack – Click here to Enroll
Data science provides organizations with striking—and highly valuable—insights into human behavior. While data mining can seem a bit daunting, you don’t need to be a highly-skilled programmer to process your own data.
In this hands-on course, learn how to use the Python scientific stack to complete common data science tasks. Miki Tebeka covers the tools and concepts you need to effectively process data with the Python scientific stack, including Pandas for data crunching, matplotlib for data visualization, NumPy for numeric computation, and more.
7. Python Data Analysis – Click here to Enroll
Data science is transforming the way that government and industry leaders look at both specific problems and the world at large. Curious about how data analysis actually works in practice? In this course, instructor Michele Vallisneri shows you how, explaining what it takes to get started with data science using Python.
Michele demonstrates how to set up your analysis environment and provides a refresher on the basics of working with data structures in Python. Then, he jumps into the big stuff: the power of arrays, indexing, and tables in NumPy and pandas—two popular third-party packages designed specifically for data analysis. He also walks through two sample big-data projects: using NumPy to identify and visualize weather patterns and using pandas to analyze the popularity of baby names over the last century. Challenges issued along the way help you practice what you’ve learned.
Note: This version of the course was updated to reflect recent changes in Python 3, NumPy, and pandas.
8. Python for Data Science Tips, Tricks, & Techniques – Click here to Enroll
Modern work in data science requires skilled professionals versed in analysis workflows and using powerful tools. Python can play an integral role in nearly every aspect of working with data—from ingest, to querying, to extracting and visualizing.
This course highlights twelve tips and tricks you can put into practice to improve your skills in Python. These techniques are readily applied and in common data management tasks and include the following: how to ingest data using CSV, JSON, and TXT files; how to explore data using libraries like Pandas; how to organize and join data using DataFrames; how to create charts and graphic representations of data using ggplot in Python; and more.
9. Python for Data Visualization – Click here to Enroll
Data visualization is incredibly important for data scientists, as it helps them communicate their insights to nontechnical peers. But you don’t need to be a design pro. Python is a popular, easy-to-use programming language that offers a number of libraries specifically built for data visualization.
In this course from the experts at Madecraft, you can learn how to build accurate, engaging, and easy-to-generate charts and graphs using Python. Explore the pandas and Matplotlib libraries, and then discover how to load and clean data sets and create simple and advanced plots, including heatmaps, histograms, and subplots. Instructor Michael Galarnyk provides all the instruction you need to create professional data visualizations through programming.
10. Python Statistics Essential Training – Click here to Enroll
With this course, gain insight into key statistical concepts and build practical analytics skills using Python and powerful third-party libraries.
In this course, Instructor Michele Vallisneri covers several major skills: cleaning, visualizing, and describing data, statistical inference, and statistical modeling. All concepts are introduced by analyzing intriguing real-world datasets and discussed from a machine-learning perspective—which assumes that powerful computation can replace complex mathematics.
