This is a hands-on applied class which will explore one or more of the following applications: 1. Music Recommendation 2. Natural Language Understanding on Voice Assistants 3. Emotion Detection from Faces - Students are encouraged to bring their own computers.

In this course, students will learn about important concepts in entrepreneurship that will help them become more informed about what it takes to start a business. Concepts that will be explored: - Minimal Viable Product - Valuation - Business Models - Funding - Scaling - Teams

- What if you had a machine that could do all your homework for you? - How do you take control of your fitness using your FitBit or Smart Watch? - How do you create music if you feel you're not a good musician? - How do you control your diet when you know there's something you want to eat and you can't control the urge? - How do you accurately manage your time? - How do you control your spending and improve your credit score? - How do you control addiction to gaming and smoking? - How do you read your textbook in less than 10 minutes? - How do you use AI to recommend you a strategy to get into your dream college? - How can AI strengthen your personal safety and notify you when danger is present? In this class, we will not only answer the above questions, but we will also discuss examples of limitations and provide a seminar to share individual experiences of interacting with data and artificial intelligence. Conceptually, we will go over a day-to-day data science workflow and introduce opportunities to pursue Data Science internships through a non-profit called ChanR Analytics.

- How you can apply data science to change your life - How can you use maps to describe the impact of solar in California? (Guest speaker) - How can you use data science to prevent bullying? - How can you use data science to make money while you're sleeping? - How do self driving cars work? - How can data science help you improve your mental health? In this class, you will learn to solve real world issues with data science in a hands-on manner. We will develop an an end-to-end workflow which involves the use of Python programming, data collection, exploratory data analysis, geographic information systems (GIS), data visualization, data transformation, cloud computing and framework design. Machine Learning will be adopted to some extent from an understandable point of view, but actual predictive model development will take place in the Machine Learning course. - Students are encouraged to bring their own computers.

This is a hands-on applied class which will explore the following applications and tools: - EDM Track Generation with IBM Watson Beat and Google Magenta - Tools - Python Programming - GitHub - Google Cloud Platform - Logic Pro - Detecting Faces in Images - Tools - JavaScript - Tensorflow.js - Dart - Flutter - App Emulator (Visual Studio Code or Android Studio) - Students are encouraged to bring their own computers.

(Also labeled H148. Co-taught by Hannah Skutt and Rishov Chatterjee) What is this course about? ## Two Parts: - Personalized Education - Discussion Topics - Why do we try to become educated? - Does the education we currently receive truly motivate us? - Is it fair that the world competes with each other through quality education? - Do you like school? - Do you think schools meet their purpose? - If you could design your own education, what would you do? - Lecture Topics - The Current Educational System - Pros and Cons - STEM versus Liberal Arts Education - STEAM Education - Massively Open Online Courses (MOOCs) - edX - Coursera - Udacity - Udemy - Advances in Educational Technology - Bridging the Gap - Personalized, Accredited Education - Scalability with Artificial Intelligence - STEMCube - Information - Discussion Topics - What is the difference between fact and opinion? - What does it mean to make an informed decision? - Does our voice really matter when we vote in an election? - When can opinions coincide with one's ethical point of view? - Lecture Topics - Bias - Opinions versus Facts - Informed Decisions - Ethical Points of View - Truth Manipulation - The Russian Interference Investigation - USA versus Edward Snowden - Sociological and Anthropological Factors Against Truth - Educational Justice

In this course, we'll look at two important fields that come together side-by-side: 1. Data Science 2. Decision Science The interesting thing about these fields is that because they're so interdisciplinary, nobody really has an explicit definition for them besides combining all the fields of study that go into them on a daily basis. So why is it necessary to know about these two fields? For one, it's highly in demand and pretty much all technology in the future is going to be embedded with concepts that stem from these fields. And secondly, it's pretty awesome because you don't have to be a natural math or computer science savant to break into these growing fields. Before going into the fine details of these state-of-the-art disciplines, I'll be making it clear to you what exactly Data and Decision Science are and how it's different from the other buzz words that you hear about such as "Machine Learning" or "Artificial Intelligence". We'll definitely include a hands-on element in the course which will rely on using a powerful tool called a Jupyter notebook in which we can write notes and code in one place. The code we'll write will be written using the Python programming language. Don't worry if you don't know any programming, I'll provide a very friendly introduction to what you need to know to follow along in-class and also from the course prerequisites. For the hands-on element, we'll look at using concepts from data science and decision science to predict Boston housing prices using a machine learning model which you know about from basic Algebra $$ y=mx+b $$ , but we'll build on it more formally as $$ ŷ =β1x1+β0 $$ and see how we can get into other methods like using more than one variable to predict the housing price ( $$ ŷ =β3x3+β2x2+β1x1+β0 $$ ) or creating a method that'll give us an interpretable process of making the decision whether or not to buy a house. You'll also learn a lot about statistics, calculus, and linear algebra in this course without feeling intimidated. Other than the hands-on elements, I'll leave you with other very valuable skills that can come in handy if you ever want to become an entrepreneur or a product manager for a company in the tech industry. I'll also advocate how these fields are very welcoming to the liberal arts because the knowledge from those fields of study can be very valuable for getting amazing results for the objectives of data and decision scientists.

This is the course you should take if you're really interested in getting hands-on with data science. I hope to break this class into three projects that we'll work on together as a group: 1. Classifying Toxic Tweets Online We'll be data scientists working with real world data from Twitter where we will work with the Twitter API and build a text dataset and we'll look at doing a technique called sentiment analysis to classify whether or not a tweet is toxic. 2. Exploring Mental Health in Los Angeles We'll work with geographic information systems information to build an interactive map of mental health support facilities in Los Angeles and do some clustering and rule mining on this map. We'll work with geocoding using a mixture of Google Maps and the Esri Python API. 3. Music Generation We'll work within a growing field of Data Science which is generating music from audio samples. We'll look at using a LSTM recurrent neural network, a Deep Belief Network and a Hidden Markov Model to generate an electronic dance music piece or a hip hop soundtrack. We may even get an opportunity to work with the Spotify API.