Data Analyst

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Learn about the data analysis process and the Python packages used in this course

Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.

Use the pandas library to load data, view its properties, and start asking data analysis questions

Use the pandas library to perform data cleaning, filtering, and reshaping tasks. This includes troubleshooting issues with data as well as optimizing for memory usage and speed.

Draw conclusions and communicate results to stakeholders by calculating statistics and creating basic data visualizations with the pandas library

Choose one of Udacity's curated datasets, perform an investigation, and share your findings.

You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.

You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.

You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.

You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.

You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.

In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.

Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.

In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.

In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.

In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.

Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!

Choose a dataset, either your own or a Udacity-curated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.