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Advanced Business Analytics Capstone(으)로 돌아가기

콜로라도 대학교 볼더 캠퍼스의 Advanced Business Analytics Capstone 학습자 리뷰 및 피드백

4.4
15개의 평가
•
3개의 리뷰

강좌 소개

The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights. In this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. For the first week, the goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. In the second week, you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance. Beginning in the third week, we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio. In the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation....

최상위 리뷰

필터링 기준:

Advanced Business Analytics Capstone의 3개 리뷰 중 1~3

교육 기관: rayed a

•

Mar 04, 2019

Great List of Courses for People who are interested

교육 기관: PRALAY P

•

Jun 23, 2018

excellent course!

교육 기관: Sally M

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Jul 10, 2017

This is a very poorly structured course. Assignments are not clear. The grading rubrics have multiple errors.

For example, week 1 Assignment: "Data visualization - part A. Create a plot to illustrate the distribution of interest rate (int_rate). Make sure to use a plot appropriate for illustrating distribution."

Distribution plots are histograms, density plots and dot plots. The rubric first asks if one of those plots was used. Then the next question grades THIS SAME PLOT based on relationship between two variables (scatterplot), NOT distribution. I correctly plotted a histogram, but lost a point because it was not a scatterplot?! It's not supposed to be!

Another example from the same assignment: "Are there columns that have rows with missing values or outliers? The missing values are tagged as NA in the dataset. Suggest strategies to handle them."

4 points

8 correct columns identified as having outliers (installment, annual_inc, revol_bal,revol_util, total_acc, acc_open_past_24mths, and total_pymnt) and strategies to handle them suggested

3 points

At least 6 correct columns identified

etc.

There are only 7 columns with outliers!!! You list them and only them in the first answer choice. Yet I lost a point because it's not 8 columns!!!

The ASP is a joke. It is SLOOOOW. Like, the assignment that should have taken me an hour to do turned into 6 because the platform would crash every 30 seconds. I can't believe anyone actually pays to use it. We'd be much better off using a programming language like R or Python to do these assignments.

Also, the assignments that depend on running a model in ASP should specify what random seed to use in partitioning the data, what exact classification tree type to use, so that we would all get same answers, and it would be easier to grade other people's work.

The rest of this specialization was great, but this last course just ruined the whole impression for me.

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