Data Scientist: Inference Specialist

Why Principles of Data Literacy? 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!

Communication is an important part of your work as a data scientist. Learn best practices for creating reports and effectively explaining your findings to various audiences.

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

Deepen your statistics knowledge!

Continue to build your Hypothesis Testing and Experimental Design skills!

Gain proficiency in the math you will need for Regression and Inference!

Learn about Linear, Multiple, and Logistic Regression for Inference Data Science.

This course is dedicated to programmers who are already familiar with the world of programming and are looking to become acquainted with the R programming language. We designed this course to be a series of short, interactive articles that you can skim, dive into, or even skip. We did this so that you can use this course to both learn R and to check as a reference guide.

Learn the basic syntax, how to work with data structures, visualizations, and more.

You hear it all the time: ""correlation is not causation."" By taking this course, you'll find out what causation really is. Find out why things happen using causal methods, such as matching and weighting, instrumental variables, and difference in differences.

In this course, you will learn the conceptual foundations for determining causal inference and how to work with data to understand why things happen. In addition to the basic foundations, you will learn how to isolate variables and apply different techniques to deal with unruly datasets and interpret the results of your analysis.

Keep building your SQL skills with advanced techniques and hands-on practice.

Show off your knowledge of data analytics by developing your final portfolio project on a topic of your choice.