Skills you'll learn:
Data Analyst
Nanodegree Program
Clean up messy data, uncover patterns and insights, and communicate your findings. You'll start with an introduction to data analysis tools, including Jupyter Notebook, NumPy, pandas, and Matplotlib. Using these tools, you will ask questions about data and answer them through data collection, exploration, wrangling, and visualization. This intermediate-level program includes real-world projects where you will choose your own datasets, research questions, and analysis approach. As you progress through the program, each course will repeat the data analysis process while introducing more advanced techniques, such as applying data imputation to fill in missing data and applying appropriate encodings when developing data visualizations.
Clean up messy data, uncover patterns and insights, and communicate your findings. You'll start with an introduction to data analysis tools, including Jupyter Notebook, NumPy, pandas, and Matplotlib. Using these tools, you will ask questions about data and answer them through data collection, exploration, wrangling, and visualization. This intermediate-level program includes real-world projects where you will choose your own datasets, research questions, and analysis approach. As you progress through the program, each course will repeat the data analysis process while introducing more advanced techniques, such as applying data imputation to fill in missing data and applying appropriate encodings when developing data visualizations.
Intermediate
2 months
Last Updated December 3, 2024
Prerequisites:
Intermediate
2 months
Last Updated December 3, 2024
Skills you'll learn:
Prerequisites:
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 • 3 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 • 3 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
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.
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.
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
1,232 Reviews
Anonymous
January 23, 2023
It’s excellent for learning from the starch also it’s challenging in a good way!
miya M.
January 11, 2023
iLOVE IT
Nikita G.
January 3, 2023
It's very interesting for me to learn SQL and Python, thank you so much for your course!!!!
Mubarak A.
December 25, 2022
Very Well
Pedro F.
December 25, 2022
Good insights.
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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.