Data Scientist: Analytics

Discover what you will learn on your journey to becoming a Data Scientist: Analytics 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. ### Take-Away Skills You will learn how to evaluate data quality, interpret statistical conclusions, create and read data visualizations, and analyze data responsibly.

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

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 and get ready to prove a case with data!

Put your Matplotlib data visualization skills to the test with these challenge projects!

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

Why Learn Tableau for Data Visualization? Visualizations are at the center of telling compelling data stories, and Tableau empowers everyone to tell a story with data, regardless of coding experience. Tableau is a leading business intelligence (BI) platform known for its friendly user experience and strong community. This course teaches you how to use Tableau to build visually appealing, interactive visualizations and dashboards. In this course, you’ll learn how to load and join data, develop effective and interactive visualizations, and build compelling data stories — all in Tableau. Finally, you’ll put your skills to the test with both an end-to-end guided project and an independent capstone project.

Why Learn Microsoft Excel? Microsoft Excel is one of the most popular data tools in the business world. Excel is designed as a one-stop shop for storing and analyzing data, with built-in tools to help you transform data of all kinds into business insights. Used for everything from complex financial modeling to tracking shipment orders, knowledge of Excel is foundational to any career in data analysis or business intelligence. In this course, you’ll learn to take raw data and produce insightful analyses using Excel's import tools, formulas, sorting and filtering tools, pivot tables, and charts. You will also learn best practices for managing, protecting, and cleaning data in Excel.

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