Data Analyst Nanodegree

Discover Insights from Data
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About This Nanodegree

Learn to use programming to manipulate large data sets, apply statistical methods to validate the accuracy of your results, and effectively communicate your insights.

After completing this program at your own pace, you’ll graduate with a portfolio of projects, a Nanodegree certificate, and the confidence to propose new solutions in interviews and projects at work.

What is a Nanodegree?

  • Content by leaders Content by Industry Leaders

    We built this program with expert analysts and leading technology companies like Facebook and MongoDB to ensure you master the skills needed to meet the requirements of industry.

  • Fast feedback Fast And Effective Human Feedback

    Our mentor network of experienced data analysts will return your project within 24 hours, with line-by-line comments and tips to ensure you are mastering the material.

  • Career guidance Personalized Career Guidance

    Take advantage of free resume and online profile reviews. You’ll also receive access to our network of hiring partners, and prepare for hiring questions with mock interviews.

  • Self placed Self-Paced Progress

    Work at your own pace, on your own schedule. You can even pause your subscription for when life gets busy or you need a short break.

  • Tailored portfolio Tailored Project Portfolio

    Build a portfolio of industry-relevant projects. Upon graduation, you’ll have an array of example work to impress any employer.

  • 1 on 1 1-on-1 Coaching And Mentorship

    Whether you're stuck on a project or confused by a concept in class, our mentors can answer any question in personal 1-on-1 sessions.

Data Analyst Nanodegree Program Syllabus

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Data Analyst Nanodegree Syllabus

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Pre-requisite knowledge

Students should have basic programming experience in Python, such as being comfortable implementing loops, conditional statements, and knowing when to use an array over a list. Any students lacking this prerequisite knowledge can take the first four lessons in Intro to Computer Science to acquire the necessary Python experience.

  • Module 1
    Statistics

    Standard deviations, confidence intervals, z-scores, and t-tests

    Project: Test a Perceptual Phenomenon

    Design and implement your own hypothesis test for a version of the Stroop test

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    • Identify several statistical study methods and describe the positives and negatives of each
    • Describe the variability in a sample or population using the range and standard deviation
    • Convert distributions into the standard normal distribution using the Z-score
    • Apply the concepts of probability and normalization to sample data sets
    • Use confidence intervals to determine how accurately a sample of data represents a broader population

    You’ll design and implement your own hypothesis test for your own version of the Stroop test. You’ll use statistical inference to draw a conclusion based on the results, summarizing your findings to give readers a good intuition for the data.

    The Stroop test is one of the most popular tests in experimental psychology, and has been replicated over 700 times since it’s initial publication in 1929. At a high level, the test measures how interfering stimuli affect human reaction time.

  • Module 2
    Intro to Data Analysis

    NumPy arrays, pandas DataFrames, and vectorized operations

    Project: Investigating a Dataset

    Pose your own question about a dataset, investigate its contents and communicate your findings

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    • Use NumPy arrays, pandas series, and vectorized operations to ease the data analysis process
    • Use two-dimensional NumPy arrays and pandas DataFrames
    • Understand how to group data and to combine data from multiple files

    In this project, you’ll choose from two data sets: passenger and crew information from the Titanic or baseball statistics from 1871-2014.

    You’ll then pose your own question about the dataset and apply each step of the data analysis process to investigate its contents and communicate your findings for others to learn from.

  • Module 3
    Data Extraction and Wrangling

    SQL, MongoDB, and assess data quality

    Project: OpenStreetMap Improvements

    Clean some OpenStreetMap data for a part of the world that you care about

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    • Properly audit the validity, accuracy, completeness, consistency, and uniformity of a dataset
    • Understand how data is modeled in relational (SQL) and document (MongoDB) databases
    • Scrape websites for data you need and store it in a database
    • Write your own queries to retrieve and summarize data from databases

    OpenStreetMap is an open source collaborative project to create a free editable map of the world. It is used in different ways by Craigslist, Foursquare, World Bank, Red Cross, and many NGOs.

    In this project, you’ll contribute your new data skills by helping to clean some OpenStreetMap data for a part of the world that you care about.

  • Module 4
    Exploratory Data Analysis

    R, investigate datasets, reshape data frames

    Project: Explore and Summarize Data

    Demonstrate your mastery of EDA by exploring the variables, patterns, and oddities within a dataset

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    • Use R to perform exploratory data analysis (EDA)
    • Understand the distribution of a variable and to check for anomalies and outliers
    • Quantify and visualize individual variables within a dataset
    • Examine and identify tradeoffs in different types of data visualizations
    • Properly apply relevant techniques for exploring the relationship between any two variables in a data set
    • Reshape data frames and use aesthetics like color and shape to examine relationships among multiple variables

    In this project, you’ll choose from a variety of datasets ranging from wine quality to presidential campaign contributions and conduct your own exploratory data analysis (EDA).

    You’ll demonstrate your mastery of EDA by exploring the variables, structure, patterns, oddities, and underlying relationships within the dataset. You’ll present your findings in an R Markdown file, and chronicle the process you took to explore the dataset so others can audit your conclusions.

  • Module 5
    Machine Learning

    Naive Bayes, Support Vector Machines, F1 scores

    Project: Identify Fraud from Enron Email

    Build an algorithm to identify Enron employees who may have committed fraud

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    • Use Naive Bayes, regression, and k-means clustering algorithms
    • Implement Support Vector Machines (SVMs) to generate new features independently on the fly
    • Implement decision trees
    • Quantify machine learning results using precision, recall, and F1 score

    Enron was one of biggest corporate scandals of the early 2000s. After revealing a sustained history of accounting fraud, the company eventually filed bankruptcy and saw many of its executives indicted.

    In this project, you’ll play detective by building an algorithm for the public Enron financial and email dataset to identify Enron employees who may have committed fraud.

  • Module 6
    Data Visualization

    HTML, CSS, D3.js, dimple.js

    Project: Storytelling with Data

    Choose a dataset and use popular visualization libraries to create your own interactive visualizations

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    • Select appropriate visualization types for different insights
    • Incorporate different narrative structures into your visualizations
    • Incorporate animation and interaction to bring more audience insights into your visualizations using D3.js

    Great data analysis tells a story through data, and the most impactful communication is often visual.

    In this project, you’ll choose a dataset and use popular visualization libraries, dimple.js or D3.js, to create your own interactive visualizations.

  • Module 7
    Design an A/B Test (Optional)

    Defining experimental groups and validating metrics

    Project: Create an A/B Test

    Analyze the results of an A/B test and recommend whether or not to launch the change

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    • Understand the key concepts and considerations when designing and conducting an A/B test
    • Identify characteristics to consider when validating metrics
    • Identify which users to bucket into the control and experimental groups
    • Calculate the number of events and running time required to reach a statistical significant result

    A/B experiments are frequently used by technology companies to test multiple versions of a website or mobile app and determine the best version to launch.

    In this project, you’ll design an A/B test, including which metrics to measure and how long the test should be run. You’ll analyze the results of an A/B test that was run by Udacity and recommend whether or not to launch the change.

  • "I never thought my set of skills would involve any understanding of technology. I'm now able to apply what I learned directly in my job, and my boss loves me for it!"

    Matthew Bellissimo
    Matthew Bellissimo

    Was a Business Consultant at Microcenter

    Now a Business Analyst at Tryon Equestrian Partners

  • "I had a hard time finding a job due to lack of relevant experience. The projects I built in the program helped me build a strong resume, and launch my career."

    Soumya Ranjan Mohanty
    Soumya Ranjan Mohanty

    Was a Student

    Now a Data Specialist at Gramener

  • "I really enjoyed learning different skills related to being a data analyst. Coming from academia, the career-related advice was especially valuable in helping me land a job.

    Kevin Burnham
    Kevin Burnham

    Was an Arabic and Linguistics Professor

    Now a Data Analyst at Pilytix

Graduate in 12 months and get a 50% tuition refund

On average, students complete the Nanodegree program in 6 months

$200 USD / month

Join 1,738 students from 68 countries currently enrolled in the Data Analyst Nanodegree Program

Looking for Nanodegree Plus, our job-guarantee program? Enroll here!

Student Reviews

Loading the latest reviews from our Nanodegree Students Latest reviews from our Nanodegree Students

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Udacity 现已提供中文版本! A Udacity tem uma página em português para você! There's a local version of Udacity for you!

前往优达学城中文网站 Ir para a página brasileira Go to Indian Site or continue to Global Site