Data Scientist: Machine Learning

This no-code course introduces the foundational how’s and why’s of data. How do statistics help us make conclusions from data? Why is good design critical for communicating data stories through data viz? What are the different kinds of analysis we can perform on a dataset? This course will help you feel empowered to answer these questions (and more!) and work with data with confidence. You will learn how to evaluate data quality, interpret statistical conclusions, create and read data visualizations, and analyze data responsibly.

We live in a data-driven world: people search through data to find insights to inform strategy, marketing, operations, and a plethora of other categories. There are a ton of businesses that use large, relational databases, which makes a basic understanding of SQL a great employable skill not only for data scientists, but for almost everyone. In this course, you'll learn how to communicate with relational databases through SQL. You'll learn—and practice with 4 projects—how to manipulate data and build queries that communicate with more than one table.

Build a foundation in programming with Python with a focus on Data Science!

Continue building your Python Skills while applying them to real data science challenges.

Learn how to use the Python pandas library and lambda functions for Data Science.

In this course, you will learn about exploratory data analysis techniques in Python, including: - EDA for data preparation - Summary statistics - Data visualization techniques - EDA prior to building a machine learning model Prior to taking this course, you should have some knowledge of base Python and experience with pandas DataFrames. Exploratory data analysis is an important part of any Data Scientist or Analyst's workflow, so we highly recommend this course for anyone who is interested in working with data.

Learn how and when to use the essential statistical tools Data Scientists use to analyze data.

If a picture is worth a thousand words, then a visualization is worth more than a thousand data points. Learn how to make them here!

Use your understanding of data visualization to analyze and plot data about GDP and life expectancy.

Clean, well-structured data is essential to data science but cleaning data requires both a keen eye and technical skills. Develop both here!

Use your knowledge of data analysis to interpret data about endangered animals for the National Park Service.

Get ready for your data science specialization by reviewing how each role works to make meaning from data.

Expand your knowledge of Python with Classes and Modules.

Build your mathematics foundation in preparation for Machine Learning.

Continue to build your Hypothesis Testing and Experimental Design skills!

Build a foundational understanding of machine learning and feature engineering.

Learn how to implement and evaluate Supervised Learning algorithms.

Learn about feature selection methods in feature engineering.

Learn about Unsupervised Learning algorithms, their implementation and where they're used.

Learn about ensembling methods in machine learning!

Continue building your machine learning knowledge with Support Vector Machines, Recommender Systems, and Naive Bayes Classifiers.

Continue building your machine learning knowledge with Support Vector Machines, Recommender Systems, and Naive Bayes Classifiers.

Use your knowledge of machine learning to build, train, and test predictions you draw about data from OKCupid.