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Data Analyst

Nanodegree Program

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

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

Intermediate

3 months

Real-world Projects

Completion Certificate

Last Updated June 18, 2024

Skills you'll learn:
Latent variables • Data visualization design • Data fluency • Exploratory data analysis
Prerequisites:
Inferential statistics • Basic Python • Elementary algebra

Courses In This Program

Course 1 15 minutes

Welcome to the Data Analyst Nanodegree Program

Lesson 1

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Course 2 3 weeks

Introduction to Data Analysis with Pandas and NumPy

Learn the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. You will work with data in Python using libraries like NumPy and pandas.

Lesson 1

The Data Analysis Process

Learn about the data analysis process and the Python packages used in this course

Lesson 2

Jupyter Notebooks

Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.

Lesson 3

Exploring and Inspecting Data

Use the pandas library to load data, view its properties, and start asking data analysis questions

Lesson 4

Manipulating Data using Pandas and NumPy

Use the pandas library to perform data cleaning, filtering, and reshaping tasks. This includes troubleshooting issues with data as well as optimizing for memory usage and speed.

Lesson 5

Communicating Results

Draw conclusions and communicate results to stakeholders by calculating statistics and creating basic data visualizations with the pandas library

Lesson 6 • Project

Investigate a Dataset

Choose one of Udacity's curated datasets, perform an investigation, and share your findings.

Course 3 4 weeks

Advanced Data Wrangling

Data wrangling is a set of processes for turning raw and messy data into a clean format to answer interesting questions from the data. In this course, you will learn the three phases of data wrangling: gathering, assessing, and cleaning data.

Lesson 1

Introduction to Data Wrangling

You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.

Lesson 2

Gathering Data

You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.

Lesson 3

Assessing Data

You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.

Lesson 4

Cleaning Data

You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.

Lesson 5 • Project

Real World Data Wrangling with Python

You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.

Course 4 4 weeks

Data Visualization with Matplotlib and Seaborn

Learn to apply sound design and data visualization principles to the data analysis process. Learn how to use analysis and visualizations to tell a story with data.

Lesson 1

Data Visualization in Data Analysis

In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.

Lesson 2

Design of Visualizations

Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.

Lesson 3

Univariate Exploration of Data

In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.

Lesson 4

Bivariate Exploration of Data

In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.

Lesson 5

Multivariate Exploration of Data

In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.

Lesson 6

Explanatory Visualizations

Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!

Lesson 7 • Project

Communicate Data Findings

Choose a dataset, either your own or a Udacity-curated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.

Taught By The Best

Photo of Josh Magee

Josh Magee

Senior Data Scientist

Josh is a Senior Data Scientist at Local Logic, where he models commercial real estate trends, acquisitions, and sustainable cities. He was formerly Assistant Professor of Data Analytics at Stonehill College, and was a postdoctoral researcher in nuclear physics at Lawrence Livermore National Laboratory.

Photo of Ria Cheruvu

Ria Cheruvu

AI Software Architect

Ria is an AI Software Architect and technical lead at Intel. She has a master's in data science from Harvard University, and is an accomplished industry speaker and instructor. She formerly served as Intel NEX’s AI Ethics Lead Architect, leading trustworthy AI product creation, and as a Teaching Fellow for Harvard Data Science. Ria has multiple patents and publications on AI and ethics, and enjoys contributing to open-source communities to advance innovation.

Photo of Matt Maybeno

Matt Maybeno

Principal Software Engineer

Matt is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

Ratings & Reviews

Average Rating: 4.8 Stars

1,184 Reviews

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About Data Analyst

Our Data Analyst Nanodegree program is a meticulously crafted data analyst online course that imparts essential skills for cleaning up messy data, uncovering patterns and insights, making predictions with machine learning, and effectively communicating findings. This intermediate-level program involves real-world projects where learners can apply their skills in data visualization, exploratory data analysis, latent variables, and more. The curriculum includes hands-on experience with Python, Pandas, NumPy, as well as advanced data wrangling and visualization using Matplotlib and Seaborn. At Udacity, we empower our learners with practical, industry-relevant skills taught by professionals like Josh Magee, Ria Cheruvu, and Matt Maybeno. Our data analyst course is designed not just to impart knowledge but to ensure its application in real-world scenarios, enhancing both understanding and skill retention. Join us to advance your career in data analysis, where we provide the tools and support for your professional growth.

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