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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 8th Apr, 2019
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.<br /> <br /> 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. <br /> <br /> 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
Understanding statistics is essential to understand research in the social and behavioral sciences. In almost all research studies, statistics are necessary to decide whether the results support the research hypothesis. In this course you will learn the basics of descriptive statistics; not just how to calculate them, but also how to evaluate them. An important part of the material treated in this course will prepare you for the next course in the specialization, namely the course Inferential Statistics.&nbsp;<br><br>We will start with the concepts variable and data, the difference between population and sample and types of data. Then we will consider the most important measures for centrality (mean, median and mode) and spread (standard deviation and variance). These will be followed by the concepts contingency, correlation and regression. All these statistics make it possible to represent large amounts of data in a clear way, enabling us to spot interesting patterns.&nbsp;<br><br>The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. We will end the course with a short preview of inferential statistics - statistics that help us decide whether the differences between groups or correlations between variables that we see in our data are strong enough to conclude that our predictions were confirmed and our hypothesis is supported.<br><br>You will not only learn about all these concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.
★★★☆☆ (9) 8 weeks Self paced
<p>In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this!</p><p>Students will learn how to gather and extract data from widely used data formats. They will learn how to assess the quality of data and explore best practices for data cleaning. We will also introduce students to MongoDB, covering the essentials of storing data and the MongoDB query language together with exploratory analysis using the MongoDB aggregation framework.</p><p>This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data.</p><p>This course is also a part of our <a href="https://www.udacity.com/course/data-analyst-nanodegree--nd002">Data Analyst</a> Nanodegree.</p><br/><br/><b>Why Take This Course?</b><br/><p>At the end of the class, students should be able to:</p><ul><li> Programmatically extract data stored in common formats such as csv, Microsoft Excel, JSON, XML and scrape web sites to parse data from HTML.</li><li> Audit data for quality (validity, accuracy, completeness, consistency, and uniformity) and critically assess options for cleaning data in different contexts. </li><li> Store, retrieve, and analyze data using MongoDB.</li></ul><p><br /><br>This course concludes with a final project where students incorporate what they have learned to address a real-world data analysis problem.</p>
★★★★★ (18) 8 weeks Self paced
<p>Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by <a href="http://en.wikipedia.org/wiki/John_Tukey">John Tukey</a>, exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods.</p><p>If you&#39;re interested in supplemental reading material for the course check out the <a href="http://www.amazon.com/gp/product/0201076160/ref=as_li_ss_il?ie=UTF8&camp=1789&creative=390957&creativeASIN=0201076160&linkCode=as2&tag=udacity-20">Exploratory Data Analysis</a> book. (Not Required)</p><p>This course is also a part of our <a href="https://www.udacity.com/course/data-analyst-nanodegree--nd002">Data Analyst</a> Nanodegree.</p><br/><br/><b>Why Take This Course?</b><br/><p>You will...</p><ul><li> Understand data analysis via EDA as a journey and a way to explore data</li><li> Explore data at multiple levels using appropriate visualizations</li><li> Acquire statistical knowledge for summarizing data</li><li> Demonstrate curiosity and skepticism when performing data analysis</li><li> Develop intuition around a data set and understand how the data was generated.</li></ul>
★★★★☆ (18) 10 weeks Self paced
<p>Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. </p><p>Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.</p><p>This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.</p><p>This course is also a part of our <a href="https://www.udacity.com/course/data-analyst-nanodegree--nd002">Data Analyst</a> Nanodegree.</p><br/><br/><b>Why Take This Course?</b><br/><p>In this course, you’ll learn by doing! We’ll bring machine learning to life by showing you fascinating use cases and tackling interesting real-world problems like self-driving cars. For your final project you’ll mine the email inboxes and financial data of Enron to identify persons of interest in one of the greatest corporate fraud cases in American history.</p><p>When you finish this introductory course, you’ll be able to analyze data using machine learning techniques, and you’ll also be prepared to take our Data Analyst Nanodegree. We’ll get you started on your machine learning journey by teaching you how to use helpful tools, such as pre-written algorithms and libraries, to answer interesting questions.</p>
★★★☆☆ (20) 4 weeks 26th Aug, 2019
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
<p>This course will cover the design and analysis of A/B tests, also known as split tests, which are online experiments used to test potential improvements to a website or mobile application. Two versions of the website are shown to different users - usually the existing website and a potential change. Then, the results are analyzed to determine whether the change is an improvement worth launching. This course will cover how to choose and characterize metrics to evaluate your experiments, how to design an experiment with enough statistical power, how to analyze the results and draw valid conclusions, and how to ensure that the the participants of your experiments are adequately protected.</p><br/><br/><b>Why Take This Course?</b><br/><p>A/B testing, or split testing, is used by companies like Google, Microsoft, Amazon, Ebay/Paypal, Netflix, and numerous others to decide which changes are worth launching. By using A/B tests to make decisions, you can base your decisions on actual data, rather than relying on intuition or HiPPO&#39;s - the highest paid person&#39;s opinion! Designing good A/B tests and drawing valid conclusions can be difficult. You can almost never measure exactly what you want to know (such as whether the users are &quot;happier&quot; on one version of the site), so you need to find good proxies. You need sanity checks to make sure your experimental set-up isn&#39;t flawed, and you need to use a variety of statistical techniques to make sure the results you&#39;re seeing aren&#39;t due to chance. This course will walk you through the entire process. At the end, you will be ready to help businesses small or large make crucial decisions that could significantly affect their future!</p>

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