Data Analyst Nanodegree

Discover Insights from Data

Earn a Certificate

  • Nanodegree via Udacity and Facebook
  • $200/month for 9-12 months
  • 1:1 feedback - Rigorous, timely project and code reviews
49 Reviews
Rating based on 49 student reviews.

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Data Analyst Nanodegree
★★★★★ (49 Reviews)
Learn how to find insights from data and prepare for a career in data science.
Credential Type
Minimum 10hrs/week
9-12 months

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. Learn to: * 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?

The Data Analyst Nanodegree is specifically designed to prepare you for a career in data science. As a Data Analyst, you will be responsible for obtaining, analyzing, and effectively reporting on data insights ranging from business metrics to user behavior and product performance. We have worked closely with leading industry partners to carefully design the ideal curriculum to prepare you for this role.

Required Knowledge

Data Analyst nanodegree students... * are interested in data science. * have a strong grasp of descriptive and inferential statistics. * have programming experience (preferably in Python) * have a strong understanding of programming concepts such as variables, functions, loops, and basic data structures like lists and dictionaries. Take the Readiness Assessment to see if you're ready to get started. General Requirements: * You are self-driven and motivated to learn. Participation in this program requires consistently meeting deadlines and devoting at least 10 hours per week to your work. * You can communicate fluently and professionally in written and spoken English. * You have access to a computer with a broadband connection, on which you’ll install a professional code/text editor (ie. Sublime Text or Atom) and programming languages like Python and R and associating data science libraries. * You will be a committed and contributing participant of the community.

★★★☆☆ (4) 7 weeks 18th Jun, 2018
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
★★★★★ (1) 6 weeks 4th Jan, 2016
Learn about descriptive statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of descriptive statistics in published research and how to generate descriptive statistics yourself, using freely available statistical software.
★★★☆☆ (9) 8 weeks Self paced
Data Scientists spend most of their time cleaning data. In this course, you will learn to convert and manipulate messy data to extract what you need.
★★★★★ (18) 8 weeks Self paced
Data is everywhere and so much of it is unexplored. Learn how to investigate and summarize data sets using R and eventually create your own analysis.
★★★★☆ (18) 10 weeks Self paced
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.
★★★☆☆ (19) 4 weeks 18th Jun, 2018
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
★★★★☆ (4) 4 weeks Self paced
Design and implement an A/B test to determine the efficacy of potential improvements to an online site or mobile app while specifying metrics to measure.

49 Reviews.

Pravin Mhaske
Technology lead
Field of study
Data science
Bachelors Degree
completed this credential in Mar 2017.

Data Analysis and R? Go for it!

Bruno Assis
Data analyst
Field of study
Data science
Bachelors Degree
Partially Completed this credential.

Bringing the Data Science market closer to you! :D

Joe Foley
Field of study
Data science
Bachelors Degree
Partially Completed this credential.

It is actually MUCH better than I had hoped