Data Scientist Nanodegree v7.0.1

Nanodegree key: nd025

Version: 7.0.5

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Get hands-on experience running data pipelines, designing experiments, building recommendation systems, and more.

Content

Part 01 : Introduction to Data Science

Module 01: Introduction to Data Science
Module 02: Career Support
- Lesson 01: Optimize Your GitHub Profile
  
  Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile

Part 02 : Supervised Learning

Learn to build supervised machine learning models to make data-informed decisions. Learn to evaluate and validate the quality of your models.

Module 01: Supervised Learning
- Lesson 01: Machine Learning Bird's Eye View
  
  Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.
- Lesson 02: Linear Regression
  
  Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!
- Lesson 03: Perceptron Algorithm
  
  The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.
- Lesson 04: Decision Trees
  
  Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
- Lesson 05: Naive Bayes
  
  Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.
- Lesson 06: Support Vector Machines
  
  Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!
- Lesson 07: Ensemble Methods
  
  Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.
- Lesson 08: Model Evaluation Metrics
  
  Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!
- Lesson 09: Training and Tuning
  
  Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.
- Lesson 10: Finding Donors Project
  
  You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!
  
  Project Description - Finding Donors for CharityML
  
  Project Rubric - Finding Donors for CharityML

Part 03 : Deep Learning

Gain a solid foundation in neural networks, deep learning, and PyTorch.

Module 01: Deep Learning
- Lesson 01: Introduction to Neural Networks
  
  In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in python right here in the classroom.
- Lesson 02: Implementing Gradient Descent
  
  Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
- Lesson 03: Training Neural Networks
  
  Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
- Lesson 04: Keras
  
  In this section you'll get a hands-on introduction to Keras. You'll learn to apply it to analyze movie reviews.
- Lesson 05: Deep Learning with PyTorch (updated version)
  
  Learn how to use PyTorch for building deep learning models.
- Lesson 06: Deep Learning with PyTorch
  
  Learn how to use PyTorch for building deep learning models
- Lesson 07: Image Classifier Project
  
  In this project, you'll build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.
  
  Project Description - Create Your Own Image Classifier
  
  Project Rubric - Create Your Own Image Classifier

Part 04 : Unsupervised Learning

Learn to build unsupervised machine learning models, and use essential data processing techniques like scaling and PCA.

Module 01: Unsupervised Learning
- Lesson 01: Clustering
  
  Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
- Lesson 02: Hierarchical and Density Based Clustering
  
  We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
- Lesson 03: Gaussian Mixture Models and Cluster Validation
  
  In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.
- Lesson 04: Dimensionality Reduction and PCA
  
  Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.
- Lesson 05: Random Projection and ICA
  
  In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).
- Lesson 06: Project: Identify Customer Segments
  
  In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.
  
  Project Description - Identify Customer Segments with Arvato
  
  Project Rubric - Identify Customer Segments with Arvato

Part 05 : Software Engineering

Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software. Then you'll work on your software skills by coding a Python package and a web data dashboard.

Module 01: Software Engineering
- Lesson 01: Introduction to Software Engineering
  
  Welcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.
- Lesson 02: Software Engineering Practices Pt I
  
  Learn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.
- Lesson 03: Software Engineering Practices Pt II
  
  Learn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.
- Lesson 04: Introduction to Object-Oriented Programming
  
  Learn the basics of object-oriented programming so that you can build your own Python package.
- Lesson 05: Portfolio Exercise: Upload a Package to PyPi
  
  Create your own Python package and upload your package to PyPi.
- Lesson 06: Web Development
  
  Develop a data dashboard using Flask, Bootstrap, Plotly and Pandas.
- Lesson 07: Portfolio Exercise: Deploy a Data Dashboard
  
  Customize the data dashboard from the previous lesson to make it your own. Upload the dashboard to the web.

Part 06 : Data Engineering

In data engineering for data scientists, you will practice building ETL, NLP, and machine learning pipelines. This will prepare you for the project with our industry partner Figure 8.

Module 01: Data Engineering
- Lesson 01: Introduction to Data Engineering
  
  You will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.
- Lesson 02: ETL Pipelines
  
  ETL stands for extract, transform, and load. This is the most common type of data pipeline, and you will practice each step in this lesson.
- Lesson 03: NLP Pipelines
  
  In order to complete the project at the end of the course, you will need some natural language processing skills. Here you will practice engineering machine learning features from text data.
- Lesson 04: Machine Learning Pipelines
  
  You'll use the Scikit-Learn package to code a machine learning pipeline. With these skills, you can ingest data, create features, and train a machine learning algorithm in just one step.
Module 02: Project
- Lesson 01: Project: Disaster Response Pipeline
  
  You’ll build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.
  
  Project Description - Disaster Response Pipelines
  
  Project Rubric - Disaster Response Pipelines
Module 03: Career Support
- Lesson 01: Take 30 Min to Improve your LinkedIn
  
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 07 : Experimental Design & Recommendations

Learn to design experiments and analyze A/B test results. Explore approaches for building recommendation systems.

Module 01: Intro to Experiment Design and Recommendation Engines
- Lesson 01: Intro to Experiment Design and Recommendation Engines
  
  Why do we care about experiment design and recommendation engines? In this lesson, you'll get an overview of the topics you'll learn in this course.
Module 02: Experiment Design & A/B Testing
- Lesson 01: Concepts in Experiment Design
  
  In this lesson, you will learn about conceptual topics that must be considered when designing and running an experiment, in order to ensure good, interpretable results.
- Lesson 02: Statistical Considerations in Testing
  
  In this lesson, you will learn how statistics can be used to benefit the design of an experiment, as well as additional statistical tests that can be used to analyze results.
- Lesson 03: A/B Testing Case Study
  
  In this lesson, you will go through an A/B Testing case study to see how the conceptual and statistical concepts covered in the previous lessons can be applied in experiment designs.
- Lesson 04: Portfolio Exercise: Starbucks
  
  In this lesson, you will analyze data that was originally used in screening interviews for data scientists at Starbucks.
Module 03: Recommendation Engines
- Lesson 01: Introduction to Recommendation Engines
  
  In this lesson, you will learn about the different methods used to create recommendation engines.
- Lesson 02: Matrix Factorization for Recommendations
  
  In this lesson, you will learn how machine learning is being used to make recommendations.
Module 04: Project: Recommendations with IBM
- Lesson 01: Recommendation Engines
  
  Put your skills to work to make recommendations for IBM Watson Studio's data platform.
  
  Project Description - Recommendations with IBM
  
  Project Rubric - Recommendations with IBM

Part 08 : Data Scientist Capstone

Leverage what you’ve learned throughout the program to build your own open-ended Data Science project. This project will serve as a demonstration of your valuable abilities as a Data Scientist.

Module 01: Lessons
- Lesson 01: Communicating to Stakeholders
  
  In this lesson, you will be creating a post to communicate your findings via Medium.
Module 02: Capstone Project
- Lesson 01: Data Scientist Capstone
  
  Now you will put your Data Science skills to the test by solving a real world problem using all that you have learned throughout the program.
  
  Project Description - Capstone Project
  
  Project Rubric - Capstone Project

Part 09 : Congratulations

Congratulations on your completion of the Data Scientist Nanodegree!

Module 01: Congrats!
- Lesson 01: Congratulations!
  
  Congratulations on your completion of the Data Scientist Nanodegree!
  - Concept 01: Congratulations!

Content

Part 01 : Introduction to Data Science

Part 02 : Supervised Learning

Part 03 : Deep Learning

Part 04 : Unsupervised Learning

Part 05 : Software Engineering

Part 06 : Data Engineering

Part 07 : Experimental Design & Recommendations

Part 08 : Data Scientist Capstone

Part 09 : Congratulations

Part 10 (Elective) : [Capstone Content] Convolutional Neural Networks

Part 11 (Elective) : [Capstone Content] Spark

Part 12 (Elective) : Prerequisite: Python for Data Analysis

Part 13 (Elective) : Prerequisite: SQL

Part 14 (Elective) : Prerequisite: Data Visualization

Part 15 (Elective) : Prerequisite: Command Line Essentials

Part 16 (Elective) : Prerequisite: Git & Github

Part 17 (Elective) : Prerequisite: Linear Algebra

Part 18 (Elective) : Prerequisite: Practical Statistics

**Part 10 (Elective) :** [Capstone Content] Convolutional Neural Networks

**Part 11 (Elective) :** [Capstone Content] Spark

**Part 12 (Elective) :** Prerequisite: Python for Data Analysis

**Part 13 (Elective) :** Prerequisite: SQL

**Part 14 (Elective) :** Prerequisite: Data Visualization

**Part 15 (Elective) :** Prerequisite: Command Line Essentials

**Part 16 (Elective) :** Prerequisite: Git & Github

**Part 17 (Elective) :** Prerequisite: Linear Algebra

**Part 18 (Elective) :** Prerequisite: Practical Statistics