subject

Data Analyst

Become a Data Analyst

Earn a Certificate

  • Nanodegree via Udacity and Facebook
  • $1200 for 6 months
  • 1:1 feedback - Rigorous, timely project and code reviews
64 Reviews
Rating based on 64 student reviews.

Learn More

Title
Data Analyst
Rating
★★★★★ (64 Reviews)
Overview
Learn how to find insights from data and prepare for a career in data science.
Credential Type
Provider
Institution
Cost
$1200
Effort
Minimum 10hrs/week
Duration
6 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?

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.

Required Knowledge

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
★★★☆☆ (4) 7 weeks 17th Dec, 2018
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).
★★★★★ (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 17th Dec, 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.

64 Reviews.

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

Data Analysis and R? Go for it!

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

Bringing the Data Science market closer to you! :D

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

It is actually MUCH better than I had hoped