Schedule¶
Please check here for the latest course schedule of activities.
Week |
Session |
Date |
Day |
Topic |
Summary |
Assignment |
Due |
|---|---|---|---|---|---|---|---|
1 |
1 |
08/31 |
Mon |
Course Overview & Introduction to the Data Science Lifecycle |
In this class we will simply be providing a high level overview of the class. We will introduce the basics of the concepts and the approaches used. Link |
This introductory assignment introduces the basics of loading files from a variety of formats and updating a number of different types of objects. It also introduces the concepts of packages. Starter |
09/10 |
1 |
2 |
09/03 |
Thu |
Python Basics |
This lecture discusses the general strategic impact of data, open data, data encoding, data provenance, data wrangling, includeing merging, aggregation, filtering. Continued introduction to coding includes conditionals, loops, functions, missing values, filtering, group-by. We will also introduce a basic Kaggle model for the Titantic dataset. Link |
||
2 |
09/07 |
Mon |
Labor day – no class |
||||
2 |
3 |
09/08 |
Tue |
Python Basics (First in Person Class, Tuesday follow Monday Schedule) |
This lecture discusses the general strategic impact of data, open data, data encoding, data provenance, data wrangling. Continued introduction to coding includes Numpy and Pandas Link |
This assignment will require you to gain some familiarity with working with a variety of different Python data structures (sets, lists, dictionaries) as well as packages (numpy, pandas) Starter |
09/17 |
2 |
4 |
09/10 |
Thu |
Python conditionals, loops, functions, aggregating. |
More operationalization of Python basics as they relate to data. Link |
||
3 |
5 |
09/14 |
Mon |
Python conditionals, loops, functions, aggregating (continued) |
More operationalization of Python basics as they relate to data. Link |
This has us create a few different functions and our first simple model. Starter |
09/24 |
3 |
6 |
09/17 |
Thu |
Python visualization, data manipulation , and feature creation. |
Introduction to visualiation, APIs, web scraping feature creation, and feature creation/extraction. The genaral goal is to get students to the point where they are able to start to do some data manipulation and utilize code they haven’t created (packages, functions) Link |
||
4 |
7 |
09/21 |
Mon |
Python visualization, data manipulation , and feature creation (continued) |
Introduction to visualiation, APIs, web scraping feature creation, and feature creation/extraction. The genaral goal is to get students to the point where they are able to start to do some data manipulation and utilize code they haven’t created (packages, functions) Link |
Some exercises with visualization and web scraping. Starter |
10/01 |
4 |
8 |
09/24 |
Thu |
Overview of Modeling |
We examine the basics of classess of supervised, unsupervised, reenforcement learning. Also examine overfitting and how cross validation is used for overfitting and how hypterparameters are used to optimize models. Link |
||
5 |
9 |
09/28 |
Mon |
Overview of Classification |
We examine the basics of classess of supervised, unsupervised, reenforcement learning. Also examine overfitting and how cross validation is used for overfitting and how hypterparameters are used to optimize models. Link |
Manipulating data Starter |
10/12 |
5 |
10 |
10/01 |
Thu |
Overview of Classification |
We examine the basics of classess of supervised, unsupervised, reenforcement learning. Also examine overfitting and how cross validation is used for overfitting and how hypterparameters are used to optimize models. Link |
||
6 |
11 |
10/05 |
Mon |
Python and Regression |
Regression models similarly a a major type of machine learning application. In this Link |
||
6 |
12 |
10/08 |
Thu |
Python and Regression |
Lab/homework Link |
||
7 |
10/12 |
Mon |
Columbus day – no class |
||||
7 |
13 |
10/15 |
Thu |
Unsupervised Models |
Unsupervised models are frequently used to subset data into subpoluations or to generate features. Link |
||
8 |
14 |
10/19 |
Mon |
Midterm Exam |
Midterm. Available 8:00 AM EST. Due Midnight. Link |
||
8 |
15 |
10/22 |
Thu |
Time Series Analysis |
Time series and panel data is a bit different and requires a different approach. Here we cover some of the basics. Link |
||
9 |
16 |
10/26 |
Mon |
Time Series Analysis |
Time series and panel data is a bit different and requires a different approach. Here we cover some of the basics. Link |
Unsupervised Starter |
11/05 |
9 |
16 |
10/26 |
Mon |
Time Series Analysis |
Time series and panel data is a bit different and requires a different approach. Here we cover some of the basics. Link |
Project First 3 sections |
11/08 |
9 |
17 |
10/29 |
Thu |
Text and NLP |
The goal of this class is to investigate basic concepts surrounding text mining. Link |
Midterm-Correction: Correct your midterm so that it passes all of the tests. Starter |
11/12 |
10 |
18 |
11/02 |
Mon |
Text and NLP |
The goal of this class is to investigate basic concepts surrounding text mining. Link |
Deep Learning Excel Lab Assignment |
11/16 |
10 |
19 |
11/05 |
Thu |
Introduction to Deep Learning |
Deep learning with Tensorflow Link |
Final Project Presentation |
12/06 |
10 |
19 |
11/05 |
Thu |
Introduction to Deep Learning |
Deep learning with Tensorflow Link |
Final Project |
12/13 |
11 |
20 |
11/09 |
Mon |
Introduction to Deep Learning |
Deep learning with Tensorflow Link |
||
11 |
21 |
11/12 |
Thu |
Introduction to Deep Learning |
Deep learning with Tensorflow Link |
||
12 |
22 |
11/16 |
Mon |
Image Data and Deep Learning |
Image data is different and deep learning has transformed the ability of machines to process image data. In this lecture we will get an overview of image processing and deep learning techniques. Link |
||
12 |
23 |
11/19 |
Thu |
NLP and Deep Learning |
NLP Data and Deep Learniing Link |
||
13 |
24 |
11/23 |
Mon |
R and Machine Learning |
The goal is to get you familiar with Spark and the general big data infrastructure. Link |
||
13 |
11/26 |
Thu |
Thanksgiving |
Advanced tools for model search |
|||
14 |
25 |
11/30 |
Mon |
Big Data |
The goal is to get you familiar with Spark and the general big data infrastructure. Link |
||
14 |
26 |
12/03 |
Thu |
Open project questions. |
The goal is to try to answer some of the questions you have seen that you weren’t sure about. Link |
||
15 |
27 |
12/07 |
Mon |
Final Presentations |
Please aim for a 5-7 min presentation covering key insights from EDA and modeling, with a focus on modeling. Link |
||
15 |
28 |
12/10 |
Thu |
Final Presentations |
Please aim for a 5-7 min presentation covering key insights from EDA and modeling, with a focus on modeling. Link |
||
17 |
29 |
12/15 |
Tue |
Final Exam |
The final exam will be comprehensive. Tuesday, 12/15 11:30-2:30 Link |