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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
9 Reviews
Rating based on 9 student reviews.

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Title
Data Analyst Nanodegree
Rating
★★★★☆ (9 Reviews)
Overview
Learn how to find insights from data and prepare for a career in data science.
Credential Type
Provider
Institution
Cost
$200/month
Effort
Minimum 10hrs/week
Duration
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 14th Aug, 2017
Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population. We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software. For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).
☆☆☆☆☆ (0) 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.
★★★★☆ (7) 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.
★★★★★ (16) 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.
★★★★☆ (17) 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 14th Aug, 2017
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
This course will cover the design and analysis of A/B tests, which are online experiments used throughout tech industry by companies like Google, Amazon, and Netflix.

9 Reviews.

Name
Allan Reyes
completed this credential in Feb 2015.

Both breadth and depth

Eli Kastelein
Name
Eli Kastelein
Job
Data analyst
completed this credential in Apr 2016.

Eli Kastelein

Michaël Lambé
Name
Michaël Lambé
Job
Software developer & data analyst
Field of study
Computer science / electromechanical science
Education
Bachelors Degree
completed this credential in Jul 2015.

Data analyst Nanodegree

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