Udacity Connect Bay Area

Become a Data Analyst in 4 months

Face-to-face learning, accelerated success. Part-time.

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Apply by November 27th. First come, first served seating. Learn more.

Data Analyst Nanodegree Program

Sessions Start December, January | Saturdays

No sessions held on Dec 23, Dec 30, Jan 13, Feb 17, Mar 31.

  • San Francisco | San Jose | Santa Clara

  • Master data science career skills.

Available Sessions
  • December 2nd - April 21st Saturday, 10am - 5pm San Francisco or San Jose
  • January 6th - May 5th Saturday, 10am - 5pm San Francisco or San Jose
Apply Now

No sessions held on Dec 23, Dec 30, Jan 13, Feb 17, Mar 31.

About the Data Analyst Nanodegree Program

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 organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

Download the Program Syllabus to learn more!

Co-Created By:
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  • Timeline
    4 months
  • Skill Level intermediate: Entering students should have programming experience (preferably Python), and ideally some background in descriptive and inferential statistics.
    • 1
    • 2
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Equity and other salary components for Data Analyst at Paysa

What You Will Learn

Prerequisite Knowledge

Students should have beginner-level experience in Python and experience using Git for version control.

Need to Prepare?

Any students lacking this prerequisite knowledge can check out Udacity's Intro to Programming Nanodegree program.

  • Statistics and Intro to Data Analysis with Python

    Describe data using statistics. Learn Python libraries essential for data analysis. Use Jupyter notebooks and go through the entire data analysis process.

  • Data Wrangling

    Dive deeper into cleaning and reshaping data using Python and SQL.

  • Statistics and Intro to Data Analysis with Python

    Use R to explore a dataset - create visualizations, search for outliers and discover relationships between variables. Perform statistical hypothesis testing and make inferences.

  • Machine Learning and Data Visualization

    Learn the fundamentals of machine learning - feature selection, classification, regression, and unsupervised learning. Learn data viz and design principles. Create interactive dashboards. Tell stories with data.

Projects You Will Build

Project 1 - Analyze Bay Area Bike Share Data

Analyze Bay Area Bike Share Data

Analyze data from a bike share company found in the San Francisco Bay Area using basic Python code. Clean a dataset for analysis, run code to create visualizations from the wrangled data, and analyze trends shown in the visualizations.

Use basic Python to visualize and analyze a Bay Area company’s trends.

Project 2 - Compute Statistics from Card Draws

Compute Statistics from Card Draws

Demonstrate your knowledge of descriptive statistics by conducting an experiment dealing with drawing from a deck of playing cards and create a write-up containing your findings.

Using descriptive statistics, conduct an experiment and create a write-up containing your findings.

Project 3 - Investigate a Dataset

Investigate a Dataset

Choose one of Udacity's curated datasets and investigate it using NumPy and pandas. You’ll complete the entire data analysis process, starting by posing a question and finishing by sharing your findings.

Investigate a Udacity-curated dataset using NumPy and pandas, by posing a question and sharing your findings.

Project 4 - Wrangle OpenStreetMap Data

Wrangle OpenStreetMap Data

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.

Use data munging techniques to clean the OpenStreetMap data for a part of the world that you care about.

Project 5 - Explore and Summarize Data

Explore and Summarize Data

Use R and apply exploratory data analysis techniques to explore a selected data set for distributions, outliers, and anomalies.

Explore a selected data set with R for distributions, outliers, and anomalies.

Project 6 - Test a Perceptual Phenomenon

Test a Perceptual Phenomenon

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

Use descriptive statistics and a statistical test to analyze the Stroop effect.

Project 7 - Identify Fraud from Enron Email

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.

Build an algorithm to identify Enron employees who may have committed fraud based on public Enron datasets.

Project 8 - Create a Tableau Story

Create a Tableau Story

Create a data visualization from a data set that tells a story or highlights trends or patterns in the data. Use Tableau 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.

Use Tableau to create a data visualization from a data set that tells a story or highlights trends or patterns in the data.

Typical Day Schedule

  • 10 AM
    Review Udacity project and deadlines
  • 11 AM
    Project Work
  • 12 PM
    Break for lunch
  • 1 PM
    Continue building project
  • 2 PM
    Review personal progress and set goals
  • 3 PM
    (Optional) Two hours open study time

FAQ

  • Why should I apply for Udacity Connect?

    Students who pursue a blended learning approach by adding an in-person component finish on average more than 30% faster than those students working strictly online. As a Udacity Connect student, you’ll benefit from in-person collaboration with peers and instructors to complete projects, overcome challenges, and master new concepts. You’ll stay on track through weekly check-ins with Session Leads who provide additional lecture on difficult course material, help with goal-setting, time management, and motivation, and you’ll gain critical career insights from guest speakers who are working professionals in relevant fields.

    See More Questions

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  • “The fact that I had to report to my Session Lead each Saturday drove me to work faster on my Nanodegree program. The discipline and motivation pushed me to get my work done.”

    — Vivek, Udacity Connect Graduate
  • “I could interact with students who were far ahead of me and get past roadblocks which I could not have done on my own.”

    — Fernando, Udacity Connect Graduate

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