Data Analyst Nanodegree Program

What prerequisites do I need?
In order to succeed in this program, you should have some experience using Python (i.e. running libraries and writing scripts). If you’ve already dabbled with NumPy and pandas, even better! If you have no experience with Python or have never written a line of code in your life, you should get started with Programming for Data Science with Python.

Is this the right program for my skills & goals?
This program is perfect for you if you’re considered the “data person” at your job, but are tired of winging it through trial and error. This program is designed to give data enthusiasts the full skillset they need to interrogate messy data sources efficiently and consistently. This program is also perfect for anyone who wants to start their career path as a Data Scientist, and want to sharpen their application of statistics and handling unstructured data.

Do I have to finish within 4 months?
Not at all! Udacity’s Nanodegree programs are 100% self-paced. If you need more time to complete your degree, you can extend your access to the program one month at a time until you finish. If you decide you need to stop for a few months, you can pause your subscription and re-activate it later.

What if I realize this isn’t what I need?
No problem — choosing a technical online course that matches your unique background and goals is a challenge. That’s why we have a refund policy, so you can dive into all the course materials and make sure it’s exactly what you need. If you decide it’s not the right course for you, just contact our support team within the first 2 days* of enrollment, and we'll help you find the right program for you and switch you over.
*Refund window may vary depending on your location. Please see the Refund Policy for more information.

Module 1
Introduction to Data Analysis
Estimated Time: 4 WEEKS @ 5-10 HRS / WEEK

• Learn how to create interactive, shareable data reports with Jupyter Notebooks
• Get set up with Anaconda, the leading package management & deployment solution for data science
• Investigate datasets using two of Python’s most powerful data science libraries, NumPy & pandas

Module 2
Practical Statistics
Estimated Time: 4 WEEKS @ 5-10 HRS / WEEK

• Learn to apply key concepts like Simpson's Paradox, conditional probability, Bayes Theorem & more
• Explore various probability distributions and learn how they apply to hypothesis testing
• Practice conducting invaluable statistical techniques like A/B tests, multiple linear regression & logistic regression

Module 3
Data Wrangling
Estimated Time: 4 WEEKS @ 5-10 HRS / WEEK

• Gather data from multiple sources, including reading saved files, downloading from URLs, scraping web pages, and accessing data from APIs
• Identify data quality issues and use metrics to categorize them by validity, accuracy, completeness, consistency, and uniformity
• Clean data using Python and pandas and test your cleaning code visually and programmatically

Module 4
Data Visualization with Python
Estimated Time: 4 WEEKS @ 5-10 HRS / WEEK

• Understand various pitfalls that can impact the effectiveness and interpretability of visualizations
• Learn how to present data in a wide range of statistically-valid visualizations, including histograms, scatterplots, heatmaps, and more
• Learn how to customize almost any aspect of your visualizations (including how to present multiple variables at once)

Design, test & publish real projects every month, following the real-world workflow of a Data Analyst

To hammer home what you learn, you’ll need to complete projects handling a variety of data, both clean and "dirty." By the time you’re done, you’ll know how (and when!) to implement every tool in a Data Analyst’s toolbelt.

Project 1
EXPLORE GLOBAL WEATHER TRENDS

Get familiar with SQL, and how to download data from a database. Analyze local and global temperature data and compare global temperature trends.

Project 2
CHOOSE YOUR OWN DATA ADVENTURE

Implement an end-to-end analysis on a dataset of your choice (choose from movie, census, sports, or firearm data!).

Project 3
CONDUCT A/B TESTING FOR AN ECOMMERCE BUSINESS

Help a business understand its A/B test results and decide whether they should push new changes to their website.

Project 4
WRANGLE MESSY DATA FROM MULTIPLE SOURCES

Gather dirty data from a variety of sources, assess its quality and tidiness, then clean and present it graphically.

Project 5
VISUALIZE & PRESENT YOUR DATA FINDINGS

Learn sound design principles for visualization, then use a variety of tools & libraries to present your findings.

“I feel like I got more out of these courses than many of the courses I took in university for my engineering degree. The projects are difficult, but they build up a portfolio of industry-applicable examples of your work to show to potential employers. I just finished this week, and have my first interview at a data analytics company next week!"
John Enyeart, 2017 Udacity graduate (Now working as an engineer at Amazon)

“This is an excellent program and will prepare you to tackle almost any data analysis and visualization problem. In just a few short months, I have gone from knowing nothing about data analysis to solving real-world problems at my company. All of the [modules] in this Nanodegree are designed to teach as much information as possible in relatable situations and scenarios, so that the maximum amount of information is retained.”
Raza Anees, 2017 Udacity graduate (now a senior advisor ATCO Frontec)