Teaching Experience

15 Tips for Landing a Data Science Job

LinkedIn Learning
Finding a job that you love can be hard. This course can help by providing you with quick strategies and tips for finding—and landing—a stellar job in the data science field. The course includes how to identify the type of data science job that best suits you, as well as take steps author_profile: true to overcome gaps in your resume. He details how to build impressive projects that showcase your skills, as well as craft a compelling data science resume and cover letter. Plus, get tips for building an online presence; finding relevant contacts to help you land a job; and preparing for your first interview.

Data Analytics Using Python

UC San Diego Extension
In this course, you will learn the rich set of tools, libraries, and packages that comprise the highly popular and practical Python data analysis ecosystem. This course is primarily taught via screen sharing programming videos. Topics taught range from basic Python syntax all the way to more advanced topics like supervised and unsupervised machine learning techniques.

Get a Remote Data Science Job

Planned To be Posted on LinkedIn Learning

Python for Data Visualization

LinkedIn Learning
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. You can see a sample video here.

Machine Learning Fundamentals

UC San Diego Extension
Utilizing machine learning to apply algorithms to their data has helped companies maximize efficiencies, pursue new markets, and create new products. This trend has prompted many industries to recognize the value of machine learning, creating a high demand for knowledge in this field. Understanding the theory of how machine learning algorithms work is not only important skill for being able to apply and debug code, but also an important skill for interviewing.
In this course, students will learn how machine learning algorithms work so they can better understand the strengths and weaknesses of popular machine learning algorithms and when to apply which algorithm in real world situations. Some of the algorithms we will cover in the course include logistic regression, k-nearest neighbors, decision trees, random forests, bagged trees, gradient boosting, principal component analysis, k-means, hierarchical clustering, support vector machines, naïve Bayes, and recommender systems. The course will also cover topics such as model validation, regularization, optimization functions, hyperparameter tuning, and methods to deal with unbalanced classes.
Toward the end of the course, we will touch on use cases where traditional machine learning algorithms are suboptimal and where deep learning can be more appropriate. These deep learning techniques will include image classification and natural language processing.

Machine Learning with Scikit-Learn

Machine Learning with Python

Stanford Continuing Studies
Utilizing machine learning to apply algorithms to their data has helped companies maximize efficiencies, pursue new markets, and create new products. This trend has prompted many industries to recognize the value of machine learning, creating a high demand for knowledge in this field.
This course will cover machine learning foundations and some of the leading open source tools in Python. We will start by learning the various strengths and weaknesses of different machine learning algorithms and then apply them to real-world situations. Additionally, we will touch on use cases where deep learning is appropriate, such as image classification, natural language processing, and speech recognition.
We will use the Python data science ecosystem to perform machine learning and deep learning. These tools are open source and popular among data scientists in both academia and industry. The tools we will use include Jupyter Notebooks, Pandas, plotting with Matplotlib and Seaborn, and machine learning with Scikit-Learn.
Some of the algorithms we will cover in the course include logistic regression, k-nearest neighbors, decision trees, random forests, principal component analysis, k-means, hierarchical clustering, and recommender systems. Students will leave the course with a solid understanding of several machine learning algorithms and the ability to use them when appropriate.