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In this Assignment, you will demonstrate your understanding of the data science methodology by applying it to a given problem. Pick one of the following topics to apply the data science methodology to:
You will have to play the role of the client as well as the data scientist to come up with a problem that is more specific but related to these topics.
Please note that this assignment is worth 10% of your final grade.
Data Science Methodology final assignment
I have chosen as topic for this task the application of data science in the field of credit cards. The reason behind this choice is that it is related with my finance education.
You are required to:
So the main problem for banks regarding credit cards is that they have to create a model to know to who they can provide them. Certain clients will not be feasible as they do not have the economic strength to back up this service.
So our question would be ” Can we automatically determine if a client is suitable to obtain a credit card?
Your answer needs to be a little bit longer. Write a few sentences to complete
your assignment.
Briefly explain how you would complete each of the following stages for the
problem that you described in the Business Understanding stage, so that you
are ultimately able to answer the question that you came up with. (5 marks):
1:Analytic Approach: As the problem requires a yes/no answer we will use a classification model
2: Data Requirements: To create the classification model we will require information regarding the bank clients. This info should include personal data of the client and should include the ones that defaulted and the one that paid.
3: Data Collection: We would use techiques like descriptive statistics and data evalution should be implemented in this phase to make sure that we have useful data for our model.
4: Data Undestanding and Preparation: In this step we need to evaluate the different variables of our data in order to undestant it better. For example we would calculate univariate statistics, such as mean or median and the correlation between variables. So we need to evaluate the quality of the data. In the data preparation phase we have to prepare the data in an specific way depending on the model.
5: Modeling and Evaluation: Lastly we create a classification model, evaluate the outcome and perform the corresponding changes untill we have a suitable model.
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This course is intended for audiences of all experiences who are interested in learning about new skills in a business context; there are no prerequisite courses.
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