project topics代写、代做c/c++，Python编程语言、代写Java语言

日期：2019-03-16 11:06

Getting Started

I recommend you get started by brainstorming project topics or themes that interest your group. To narrow down your project to just one topic, think about:

What questions does your topic address or what problems does your topic solve? Why and to whom are these meaningful?

What's challenging about your topic?

Are there credible,public datasets available to explore the topic? See below for some suggested data sources.

Is a 6-week project long enough to explore the topic reasonably well?

Make sure that everyone in the group agrees on the topic. You will need a certain curiosity about your topic in order to stay motivated throughout the quarter.

Once you've selected a project topic, you can start working on the project proposal and the project itself.

As inspiration and an example of what can be done with public datasets, see? I Quant NY. Sample projects from previous years:

Sample 1

Sample 2

Report (grading criteria)

The final report is due in?finals week. The report should be 8-10 pages including writing and visualizations, but excluding code.

We will score your report according to the class rubric:

Reporting: Are there clear research questions that you asked, and did you address these in an orderly fashion? Did you make well justified conclusions? Is your project easy to read?

Visualization: Do your visualizations follow best practices such as those outlined in the graphics checklist and described in the class references? Do your visualizations support your conclusions?

Code: Is your code well-organized and easy to read? Is your code reproducible? Is your code documented? Is your code reasonably efficient? Did you use appropriate data structures and algorithms?

In addition, we will score your report on 5 characteristics, with the highest scores going only to the best projects in the class:

Data Collection: How much work was necessary to get your data into memory, which includes web APIs, web scraping, and reading data from files. Was your data extracted from an online source? Did you use multiple data sources? Was your data in a difficult file format? Data Cleaning and Storage: Did you process the data in an clear, efficient, and organized way? Do you join multiple data sources appropriately? Did you work with unstructured data? Did you store your processed data in an efficient way, using well-thought-out data structures or a database?

Exploratory Data Analysis: Did you explore the data before moving on with your analysis? Looking at the data can mean summary statistics, dealing with missing data, visualization, etc.

Statistics: Did you use statistics and machine learning? Was your use of statistics and machine learning valid? Did you choose appropriate methods based on your questions, the data, and your assumptions?

Interactive Visualization: Did you create any interactive visualizations? Do your interactive visualizations add insight to your project? Do they follow best practices for visualizations? Are they tailored to your specific topic and data (not generic or off-the-shelf)?

We will compute your project grade based on the rubric scores and your 3 highest-ranked characteristics. This way you are not necessarily required to do well in all 5 characteristics.

We will be more generous when grading smaller groups. The material and effort shown in your project should be roughly proportional to the number of people in your group.