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.

Getting Help

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

The Skills That Set You Apart

Welcome to the Course

This lesson will give you an overview of the course, discuss pre-requisites and stakeholders.

The Data Science Process

In this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.

Communicating to Stakeholders

In this lesson, you will be creating a post to communicate your findings via Medium.

Project: Writing a Data Scientist Blog Post

In this project, you will create a blog post and Github repository that you can use as you build your data science portfolio.

Introduction to Software Engineering

Welcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.

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.

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.

Introduction to Object-Oriented Programming

Learn the basics of object-oriented programming so that you can build your own Python package.

Portfolio Exercise: Upload a Package to PyPi

Create your own Python package and upload your package to PyPi.

Web Development

Develop a data dashboard using Flask, Bootstrap, Plotly and Pandas.

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.

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.

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.

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.

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.

Project: Disaster Response Pipeline

You’ll build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Intro to Experimental Design & Recommendations 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.

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.

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.

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.

Portfolio Exercise: Starbucks

In this lesson, you will analyze data that was originally used in screening interviews for data scientists at Starbucks.

Introduction to Recommendation Engines

In this lesson, you will learn about the different methods used to create recommendation engines.

Matrix Factorization for Recommendations

In this lesson, you will learn how machine learning is being used to make recommendations.

Project: Recommendation Engines

Put your skills to work to make recommendations for IBM Watson Studio's data platform.

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.

Congratulations!

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.

Neural Networks

Luis will give you an overview of logistic regression, gradient descent, and the building blocks of neural networks.

Deep Neural Networks

A deeper dive into backpropagation and the training process of neural networks, including techniques to improve the training.

Convolutional Neural Networks

Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.

Introduction to the Course

In this lesson, you will learn more about this course - what will be covered, and who you will be learning from - let's get started!

The Power of Spark

In this lesson, you will learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it.

Data Wrangling with Spark

In this lesson, we'll dive into how to use Spark for cleaning and aggregating data.

Debugging and Optimization

In this lesson, we will cover various troubleshooting techniques and potential ways of optimizing the performance of your Spark applications.

Machine Learning with Spark

In this lesson, we'll explore Spark's ML capabilities and build ML models and pipelines.

[DSND Capstone] Cloud Deployment Instructions

Why Python Programming

Welcome to Introduction to Python! Here's an overview of the course.

Data Types and Operators

Familiarize yourself with the building blocks of Python! Learn about data types and operators, compound data structures, type conversion, built-in functions, and style guidelines.

Control Flow

Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.

Functions

Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Scripting

Setup your own programming environment to write and run Python scripts locally! Learn good scripting practices, interact with different inputs, and discover awesome tools.

NumPy

Learn the basics of NumPy and how to use it to create and manipulate arrays.

Pandas

Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

Basic SQL

In this section, you will gain knowledge about SQL basics for working with a single table. You will learn the key commands to filter a table in many different ways.

SQL Joins

In this lesson, you will learn how to combine data from multiple tables together.

SQL Aggregations

In this lesson, you will learn how to aggregate data using SQL functions like SUM, AVG, and COUNT. Additionally, CASE, HAVING, and DATE functions provide you an incredible problem solving toolkit.

SQL Subqueries & Temporary Tables

In this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.

SQL Data Cleaning

Cleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.

[Advanced] SQL Window Functions

Compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.

[Advanced] SQL Advanced JOINs & Performance Tuning

Learn advanced joins and how to make queries that run quickly across giant datasets. Most of the examples in the lesson involve edge cases, some of which come up in interviews.

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.

Design of Visualizations

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

Univariate Exploration of Data

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

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.

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.

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!

Visualization Case Study

Put to practice the concepts you've learned about exploratory and explanatory data visualization in this case study on factors that impact diamond prices.

Shell Workshop

The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.

What is Version Control?

Version control is an incredibly important part of a professional programmer's life. In this lesson, you'll learn about the benefits of version control and install the version control tool Git!

Create A Git Repo

Now that you've learned the benefits of Version Control and gotten Git installed, it's time you learn how to create a repository.

Review a Repo's History

Knowing how to review an existing Git repository's history of commits is extremely important. You'll learn how to do just that in this lesson.

Add Commits To A Repo

A repository is nothing without commits. In this lesson, you'll learn how to make commits, write descriptive commit messages, and verify the changes you're about to save to the repository.

Tagging, Branching, and Merging

Being able to work on your project in isolation from other changes will multiply your productivity. You'll learn how to do this isolated development with Git's branches.

Undoing Changes

Help! Disaster has struck! You don't have to worry, though, because your project is tracked in version control! You'll learn how to undo and modify changes that have been saved to the repository.

Working With Remotes

You'll learn how to create remote repositories on GitHub and how to get and send changes to the remote repository.

Working On Another Developer's Repository

In this lesson, you'll learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project.

Staying In Sync With A Remote Repository

You'll learn how to send suggested changes to another developer by using pull requests. You'll also learn how to use the powerful `git rebase` command to squash commits together.

Introduction

Take a sneak peek into the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.

Vectors

Learn about vectors, the basic building block of Linear Algebra.

Linear Combination

Learn how to scale and add vectors and how to visualize the process.

Linear Transformation and Matrices

What is a linear transformation and how is it directly related to matrices? Learn how to apply the math and visualize the concept.

Descriptive Statistics - Part I

In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.

Descriptive Statistics - Part II

In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.

Admissions Case Study

Learn to ask the right questions, as you learn about Simpson's Paradox.

Probability

Gain the basics of probability using coins and die.

Binomial Distribution

Learn about one of the most popular distributions in probability - the Binomial Distribution.

Conditional Probability

Not all events are independent. Learn the probability rules for dependent events.

Bayes Rule

Learn one of the most popular rules in all of statistics - Bayes rule.

Python Probability Practice

Take what you have learned in the last lessons and put it to practice in Python.

Normal Distribution Theory

Learn the mathematics behind moving from a coin flip to a normal distribution.

Sampling distributions and the Central Limit Theorem

Learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

Confidence Intervals

Learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

Hypothesis Testing

Learn the necessary skills to create and analyze the results in hypothesis testing.

Case Study: A/B tests

Work through a case study of how A/B testing works for an online education company called Audacity.

Regression

Use python to fit linear regression models, as well as understand how to interpret the results of linear models.

Multiple Linear Regression

Learn to apply multiple linear regression models in python. Learn to interpret the results and understand if your model fits well.

Logistic Regression

Learn to apply logistic regression models in python. Learn to interpret the results and understand if your model fits well.

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.

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.