Data Analyst Nanodegree - Facebook

Udacity
Online

Kostenlos

Wichtige informationen

  • Kurs
  • Online
  • Wann:
    Freie Auswahl
Beschreibung

Learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings.

Wichtige informationen
Veranstaltungsort(e)

Wo und wann

Beginn Lage
Freie Auswahl
Online

Was lernen Sie in diesem Kurs?

Testing
Email
Data analysis
Statistics
Design
Project
Skills and Training

Themenkreis

Data Scientist was rated by Glassdoor as the # 1 Best Job in America for 2016. Join 1,606 other students in our Data Analyst Nanodegree!

We built this program with expert analysts and scientists at leading technology companies to ensure you master the exact skills necessary to build a career in data science.

Learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

  • Project 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.

    Supporting Courses

    Intro to Descriptive Statistics

    Project 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.

  • Project 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.

    Supporting Courses

    Intro to Inferential Statistics

    Intro to Descriptive Statistics

    Project 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.

  • Project 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.

    Supporting Courses

    Intro to Data Analysis

    Project 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.

  • Project 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.

    Supporting Courses

    Data Wrangling with MongoDB

    Project 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.

  • Project 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.

    Supporting Courses

    Data Analysis with R

    Project 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.

  • Project 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.

    Supporting Courses

    Intro to Machine Learning

    Project 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.

  • Project 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.

    Supporting Courses

    Intro to HTML and CSS

    Data Visualization and D3.js

    JavaScript Basics

    Project 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.

  • Project 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.

    Supporting Courses

    A/B Testing

    Project 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.