Data Analyst

An Introduction to Your Nanodegree Program

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

The Data Analysis Process

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

Jupyter Notebooks

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.

Exploring and Inspecting Data

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

Manipulating Data using Pandas and NumPy

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.

Communicating Results

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

Investigate a Dataset

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

Introduction to Data Wrangling

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.

Gathering Data

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

Assessing Data

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

Cleaning Data

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

Real World Data Wrangling with Python

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

Data Visualization in Data Analysis

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

Design of Visualizations

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

Univariate Exploration of Data

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

Bivariate Exploration of Data

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.

Multivariate Exploration of Data

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

Explanatory Visualizations

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!

Communicate Data Findings

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.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Congratulations!

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.