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Data Analyst Nanodegree

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Data Analyst Nanodegree

COURSE
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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 Course?

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.

Prerequisites and Requirements

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.

What Will I Learn?

P0: 7 Day Warm-Up: Find the Optimal Chopstick Length

An opportunity to get started with data analysis and receive some quick feedback about your progress.

Set up iPython notebook and commonly used data analysis libraries on your own computer. Use them to dig into the results of an experiment testing the optimal length of chopsticks and present your findings.

P1: Test a Perceptual Phenomenon

Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology. Give your readers a good intuition for the data and use statistical inference to draw a conclusion based on the results.

P2: Investigate a Dataset

Choose one of Udacity's curated datasets and investigate it using NumPy and Pandas. Go through the entire data analysis process, starting by posing a question and finishing by sharing your findings.

P3: Wrangle OpenStreetMap Data

Choose any area of the world in https://www.openstreetmap.org and use data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for a part of the world that you care about.

P4: Explore and Summarize Data

Use R and apply exploratory data analysis techniques to explore relationships in one variable to multiple variables and to explore a selected data set for distributions, outliers, and anomalies.

P5: Identify Fraud from Enron Email

Play detective and put your machine learning skills to use by building an algorithm to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.

P6: Make Effective Data Visualization

Create a data visualization from a data set that tells a story or highlights trends or patterns in the data. Use either dimple.js or d3.js to create the visualization. Your work should be a reflection of the theory and practice of data visualization, such as visual encodings, design principles, and effective communication.

P7: Design an A/B Test

Make design decisions for an A/B test, including which metrics to measure and how long the test should be run. Analyze the results of an A/B test that was run by Udacity and recommend whether or not to launch the change.

Data Analyst Interview Dry-Run Review

Resume Review

In this project, you will update your resume according to the conventions that recruiters expect and get tips on how to best represent yourself to pass the "6 second screen". You will also make sure that your resume is appropriately targeted for the job you’re applying for. We recommend all students update their resumes to show off their newly acquired skills regardless of whether you are looking for a new job soon.

LinkedIn Profile Review

In this project, you will look at your LinkedIn profile through the lens of a recruiter or hiring manager, focusing on how your experience, education, and interests represent you as a potential candidate for a company or collaborator on a project.

Syllabus

7 Day Warm-Up: Find the Optimal Chopstick LengthAn opportunity to get your feet wet and get some quick feedback about your progress.

Set up iPython notebook and commonly used data analysis libraries on your own computer. Use them to dig into the results of an experiment testing the optimal length of chopsticks and present your findings.

Prepare for this project with: Lesson 1 of Statistics

Test a Perceptual Phenomenon

Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology. Give your readers a good intuition for the data and use statistical inference to draw a conclusion based on the results.

Prepare for this project with: Statistics (available separately as Inferential Statistics and Descriptive Statistics)

Investigate a Dataset

Choose one of Udacity's curated datasets and investigate it using NumPy and Pandas. Go through the entire data analysis process, starting by posing a question and finishing by sharing your findings.

Prepare for this project with: Intro to Data Analysis

Wrangle OpenStreetMap Data Choose any area of the world in OpenStreetMap and use data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the OpenStreetMap data for a part of the world that you care about. Choose between MongoDB or SQL as the data schema to complete your project.

Explore, Summarize, and Discover Interesting Insights from Datasets Use R to apply exploratory data analysis techniques. Practice understanding a single variable and relationships between multiple variables, and explore a selected data set for distributions, outliers, and anomalies.

Prepare for this project with: Data Analysis with R

Identify Fraud from the Enron Email Dataset Play detective and put your machine learning skills to use by building an algorithm to identify Enron Employees who may have committed fraud based on the public Enron financial and email dataset.

Prepare for this project with: Intro to Machine Learning

Tell Stories with Data Visualization Create a data visualization from a data set that tells a story or highlights patterns in the data. Your work will be a reflection of the theory and best practices of data visualization.

Prepare for this project with: Data Visualization

Design and Analyze an A/B Test

Make design decisions for an A/B test, including which metrics to measure and how long the test should be run. Analyze the results of an A/B test that was run by Udacity and recommend whether or not to launch the change.

Prepare for this project with: A/B Testing

As with all our Nanodegree programs, we regularly audit and review our Data Analyst Nanodegree curriculum-- both courses and projects. We do so largely based on student and industry feedback, and we make adjustments wherever and whenever we identify opportunities for improvement. Any potential impacts on degree requirements are always communicated to students actively working towards the Nanodegree.