Data Scientist

Name: Data Scientist Nanodegree Program
Rating: 4.8 (807 reviews)

Nanodegree Program

What if you could recommend the perfect movie, predict housing prices to guide smart investments, or anticipate healthcare outcomes like diabetes and breast cancer? These are just a few of the challenges Data Scientists tackle every day. Data science is the driving force behind solving complex problems across industries. It can help HR departments retain their employees, allow policymakers to predict economic outcomes, and even enable NASA scientists to monitor environmental changes. In this program, you’ll not only tackle these real-world applications but also gain the skills to envision new solutions yourself. You’ll build projects that directly mirror the work done by data scientists in the field, incorporating the latest AI tools in both your workflows and your deliverables.

Advanced
3 months
Last Updated January 23, 2025

Skills you'll learn:

Blog postsMagic methods

Prerequisites:

GithubMatrix operationsmatplotlibLinear regressionPandas

Advanced

3 months

Last Updated January 23, 2025

Skills you'll learn:

Blog posts • Magic methods • Unit testing • Data storytelling

Prerequisites:

Github • Matrix operations • matplotlib

Courses In This Program

Course 1 • 45 minutes

Welcome to the Data Scientist Nanodegree Program

Welcome to the Data Scientist 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.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

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.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 • 2 weeks

Introduction to Data Science and Supervised Machine Learning

This course is for data analysts who want to apply machine learning. You will begin with an introduction to the fundamental concepts and processes that differentiate data science from other fields. Then you will dive deeper into machine learning algorithms, including underlying math concepts like gradient descent, ensemble models like random forests, and an introduction to neural networks and deep learning. Once you can harness these algorithms, you will apply model evaluation techniques for both accuracy and fairness. The course culminates with advice for effectively communicating findings to stakeholders. Your final project will involve building a machine learning model and writing a blog post about your analysis, to build your data science portfolio.

Lesson 1

The Data Science Process

Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

Lesson 2

Supervised Machine Learning Algorithms

Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

Lesson 3

Machine Learning Model Evaluation

Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

Lesson 4

Model Interpretability and Fairness

To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

Lesson 5

Communicating to Stakeholders

Create a GitHub repository and Medium blog post to communicate your findings

Lesson 6 • Project

Project: Data Science Blog Post

Complete the CRISP-DM process with a dataset of your choice, and deploy your findings in the format of a blog post.

Lesson 1

The Data Science Process

Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

Lesson 2

Supervised Machine Learning Algorithms

Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

Lesson 3

Machine Learning Model Evaluation

Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

Lesson 4

Model Interpretability and Fairness

To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

Lesson 5

Communicating to Stakeholders

Create a GitHub repository and Medium blog post to communicate your findings

Lesson 6 • Project

Project: Data Science Blog Post

Complete the CRISP-DM process with a dataset of your choice, and deploy your findings in the format of a blog post.

Course 3 • 3 weeks

Software Engineering for Data Scientists

Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software, working with Python packages, and building web data dashboards. You'll take a look "under the hood" of Python packages like scikit-learn and then practice creating and sharing virtual environments, packages, and web data dashboards. You will also become familiar with code quality and reproducibility tools including unit tests, linting, and GitHub actions. In your final project, you will develop a FastHTML dashboard that allows users to interact with a machine learning model through a web interface.

Lesson 1

Object-Oriented Programming

Explore object-oriented programming (OOP), including classes, instances, magic methods, inheritance, and polymorphism. Utilize professional coding patterns for efficient Python software development.

Lesson 2

Code Reproducibility

Use and create virtual environments, write Python packages, manage file paths in a way that enables cross-platform compatibility, and ensure code quality through testing and linting.

Lesson 3

Data Science Dashboards

Dig into deployment options for ML models using web servers, web forms, dynamic endpoints, and interactive dashboards with Python and FastHTML.

Lesson 4 • Project

Project: Data Science Dashboard

Build a dashboard web application that allows managers to monitor an employee's performance and their predicted risk of recruitment.

Lesson 1

Object-Oriented Programming

Explore object-oriented programming (OOP), including classes, instances, magic methods, inheritance, and polymorphism. Utilize professional coding patterns for efficient Python software development.

Lesson 2

Code Reproducibility

Use and create virtual environments, write Python packages, manage file paths in a way that enables cross-platform compatibility, and ensure code quality through testing and linting.

Lesson 3

Data Science Dashboards

Dig into deployment options for ML models using web servers, web forms, dynamic endpoints, and interactive dashboards with Python and FastHTML.

Lesson 4 • Project

Project: Data Science Dashboard

Build a dashboard web application that allows managers to monitor an employee's performance and their predicted risk of recruitment.

Course 4 • 4 weeks

Data Science Pipelines

Pipelines are essential tools for data scientists. They enable you to seamlessly combine various different features and preprocessing approaches into a single pipeline object. In this course, you will build reproducible, modular scikit-learn pipelines that prevent subtle data leakage issues and allow you to harness new types of data. These data sources include image data, where you will leverage SVM (support vector machine) and CNN (convolutional neural network) models, and text data, where you will leverage modern NLP (natural language processing) tools including LLMs (large language models) hosted on Hugging Face.

Lesson 1

Scikit-Learn Pipelines

Master scikit-learn pipelines for efficient data workflows, preprocessing, feature engineering, and model optimization, enhancing machine learning projects with automation and clarity.

Lesson 2

Computer Vision Pipelines

Explore computer vision by learning image preprocessing, feature extraction, and building classification pipelines with SVMs and CNNs using tools such as OpenCV and PyTorch.

Lesson 3

NLP Pipelines

Build machine learning pipelines with text data features, including tokenization, vectorization, and part-of-speech tagging with spaCy.

Lesson 4 • Project

Project: Data Science Pipeline

Create a machine learning model pipeline with scikit-learn, using numeric and text data to predict whether or not a customer would recommend a product.

Lesson 1

Scikit-Learn Pipelines

Master scikit-learn pipelines for efficient data workflows, preprocessing, feature engineering, and model optimization, enhancing machine learning projects with automation and clarity.

Lesson 2

Computer Vision Pipelines

Explore computer vision by learning image preprocessing, feature extraction, and building classification pipelines with SVMs and CNNs using tools such as OpenCV and PyTorch.

Lesson 3

NLP Pipelines

Build machine learning pipelines with text data features, including tokenization, vectorization, and part-of-speech tagging with spaCy.

Lesson 4 • Project

Project: Data Science Pipeline

Create a machine learning model pipeline with scikit-learn, using numeric and text data to predict whether or not a customer would recommend a product.

Taught By The Best

David Elliott

Data Scientist, Data Engineer

David Elliott is both a data scientist and a data engineer at a small data management company. He has extensive experience in education, both as an instructor and as a curriculum developer.

Antje Muntzinger

Professor of Computer Vision

Antje Muntzinger is a professor of computer vision at Stuttgart University of Applied Sciences, where she teaches AI and applied mathematics. Previously, she was an algorithm developer and tech lead for sensor fusion in the field of automated driving at Mercedes. She holds a PhD in engineering and a diploma in mathematics.

Nathan Klarer

CEO

Nathan is an expert in the scientific fields of bioengineering-bioinformatics and machine learning. He is responsible for building multiple successful enterprises that have driven tens of millions of dollars of investment globally.

Joshua Bernhard

Staff Data Scientist, Marketplace

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

Jo-L Collins

Senior Lead Data Scientist

Jo-L Collins is a Data Scientist and former Data Science instructor at Flatiron School, where they use a variety of inferential and descriptive analysis techniques, paired with airflow development, to answer complex data questions.

Victor Geislinger

Machine Learning Engineer

Victor Geislinger is a machine learning engineer and is dedicated to sharing his knowledge with others. Victor recently joined Google as a software engineer focused on AI/ML but has been programming and educating others for over a decade.

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.

Student Reviews

Average Rating: 4.8 Stars

807 Reviews

Dattaji K.

January 27, 2023

Going good although a bit slow due to time constraints.

Maxim K.

January 25, 2023

Small bugs, but the content and the tasks are really great for the job preparation!

Saad A.

January 10, 2023

great start

Nihal K.

December 28, 2022

Just Brilliant.

Kerim Kutluhan T.

December 15, 2022

It is definitely more challenging than any coursera course I have taken related to the subject and therefore I believe I am learning more. thank you

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Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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