Best-in-class curriculum, personalized instruction, close mentoring, a peerless review model, and career guidance combine to equip students of this program with the skills necessary to obtain rewarding employment as a Data Analyst.
Take the Readiness Assessment to find out if you're ready to get started.
- Wrangle, extract, transform, and load data from various databases, formats, and data sources
- Use exploratory data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets
- Classify unlabeled data or predict into the future with applied statistics and machine learning algorithms
- Communicate data analysis and findings through effective data visualizations
We have designed this program by working closely with expert data analysts and scientists at leading technology companies, and in partnership with their hiring managers to ensure you emerge from your degree program with the skills and talents these companies are seeking.
Why Take This Nanodegree?
This Data Analyst Nanodegree is designed to prepare you for a career in Data Science, which is quickly becoming a top priority for organizations. This program’s curriculum was developed with leading industry partners to ensure students master the most cutting-edge skills. Graduates will emerge fully prepared for this amazing career.
This program is comprised of two Terms. Depending on your existing skills and experience, you'll begin the program in either Term 1 or Term 2. To enter at Term 2, you must have:
- Strong Python programming skills
- Solid understanding of inferential statistics and its applications
Otherwise, you'll begin in Term 1. All students must successfully complete Term 2 to graduate.
Term 1: Data Analysis with Python and SQL
Understanding of Descriptive Statistics
- Measures of Center
- Measures of Spread
- Histograms and Boxplots
- Probability distributions
Basic Data Skills
- Ability to work with data in a spreadsheet
- SQL knowledge a plus
Term 2: Advanced Data Analysis
Experience programming in Python
- Python standard libraries
- Working with data in Pandas
Understanding of inferential statistics and probability and their applications
- Sampling distributions
- Standardizing data
- A/B tests
- Linear regression