peer graded assignment introduction to data analytics

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Introduction to Data Analytics

IBM and Meta via Coursera Help

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• Working with Data
• This week, you will learn what data analytics are and what a data analyst does. You’ll be introduced to the OSEMN framework as well as important business metrics, KPIs and their value to a business.
• Obtaining and Scrubbing Data
• In the second week you will learn how to discover different sources of data and how to evaluate their validity. You will also explore different data formats. You’ll begin to apply the OSEMN framework by learning the steps in the data cleaning process as well as how to handle missing or incorrect data in your datasets.
• Exploring and Modeling Data
• This week moves onto the Exploring and Modeling phases of OSEMN. You will learn how to inspect and summarize your data as well as evaluate data relationships. You will discover the purpose of data modeling and common types of data models and data visualizations.
• Interpreting Data
• This week you will learn how to interpret the data you have working with and relate the results of your analysis back to a specific business goal. You will also learn how to create a story for a presentation of your data in order to explain and engage an audience.
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• Berbelek @berbelek 1 year ago The specificity of this course (it opens a specialization consisting of 9 courses) is that it contains a lot of generalities and should rather be reviewed as a relatively good introduction to the specialization as a whole. Helpful
• AA Aedrian Abrilla @aaabrilla 2 years ago As expected, this course, from a leader in the data analytics/science field, provides an excellent overview of the state of the art in data analytics. Unlike other courses that I tried in the topic, this offering hit the ground running by emphasizing high-yield points from both the official material and the expert viewpoints. I am looking forward to finish the Professional Certificate where this course belongs. Helpful
• KK Kai 11 months ago The OSEMN data analysis framework was useful as a refresher for data analysis projects. Obtain Scrub Explore Model iNterpret Helpful
• CJ Hor 1 year ago Easy to understand and systematic. I enjoy going through every topic. This is a stepping stone in starting a new career in data analyst. Helpful

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Coursera | Introduction to Data Analytics(IBM) | Final Assignment

tags:  Data Science DSci    coursera    big data    data analytics

Other links: Pretend to have notes (write them when you have time) Coursera | Introduction to Data Analytics (IBM) | Quiz Answers

Coursera | Introduction to Data Analytics | Final Assignment

Final assignment: data analysis in action, my submission.

• Rubric (scoring standard/reference answer)

This assignment is not difficult, there is no problem if you are careful

Using Data Analysis for Detecting Credit Card Fraud

Companies today are employing analytical techniques for the early detection of credit card frauds, a key factor in mitigating fraud damage. The most common type of credit card fraud does not involve the physical stealing of the card, but that of credit card credentials, which are then used for online purchases. Imagine that you have been hired as a Data Analyst to work in the Credit Card Division of a bank. And your first assignment is to join your team in using data analysis for the early detection and mitigation of credit card fraud. In order to prescribe a way forward, that is, suggest what should be done in order for fraud to get detected early on, you need to understand what a fraudulent transaction looks like. And for that you need to start by looking at historical data.

Descriptive techniques of analysis, that is, techniques that help you gain an understanding of what happened, include the identification of patterns and anomalies in data. Anomalies signify a variation in a pattern that seems uncharacteristic, or, out of the ordinary. Anomalies may occur for perfectly valid and genuine reasons, but they do warrant an evaluation because they can be a sign of fraudulent activity.

Past studies have suggested that some of the common events that you may need to watch out for include:

• A change in frequency of orders placed, for example, a customer who typically places a couple of orders a month, suddenly makes numerous transactions within a short span of time, sometimes within minutes of the previous order. Orders that are significantly higher than a user’s average transaction.
• Bulk orders of the same item with slight variations such as color or size—especially if this is atypical of the user’s transaction history.
• A sudden change in delivery preference, for example, a change from home or office delivery address to in-store, warehouse, or PO Box delivery.
• A mismatched IP Address, or an IP Address that is not from the general location or area of the billing address.

Before you can analyze the data for patterns and anomalies, you need to:

• Identify and gather all data points that can be of relevance to your use case. For example, the card holder’s details, transaction details, delivery details, location, and network are some of the data points that could be explored.
• Clean the data. You need to identify and fix issues in the data that can lead to false or incomplete findings, such as missing data values and incorrect data. You may also need to standardize data formats in some cases, for example, the date fields.

In the next section you will be asked to answer the following 5 (five) questions based on this case study:

• List at least 5 (five) data points that are required for the analysis and detection of a credit card fraud. (3 marks)
• Identify 3 (three) errors/issues that could impact the accuracy of your findings, based on a data table provided. (3 marks)
• Identify 2 (two) anomalies, or unexpected behaviors, that would lead you to believe the transaction may be suspect, based on a data table provided. (2 marks)
• Briefly explain your key take-away from the provided data visualization chart. (1 mark)
• Identify the type of analysis that you are performing when you are analyzing historical credit card data to understand what a fraudulent transaction looks like. [Hint: The four types of Analytics include: Descriptive, Diagnostic, Predictive, Prescriptive] (1 mark)

Rubric (scoring criteria/reference answer)

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Peer-graded Assignment: Final Assignment

Peer-graded assignment: final assignment >> data science methodology, instructions:.

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.

Assignment Solution :

Which topic did you choose to apply the data science methodology to? (2 marks)

For example, using the food recipes use case discussed in the labs, the question that we defined was, “Can we automatically determine the cuisine of a given dish based on its ingredients?”.

You can always refer to the labs as a reference with describing how you would complete each stage for your problem.

Related Questions & Answers:

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