introduction to data analytics coursera peer graded assignment

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IBM – Introduction to Data Analytics – Detailed Review

Jason Glover

This is the first course in the IBM Data Analyst Professional Certificate program. In IBM’s introduction to data analytics, you will learn a little bit about everything related to the data profession. The course includes video lectures, short readings, multiple choice quizzes, and one final assignment. You will not delve deeply into any of the subjects covered; instead, you will receive a solid overview of many different topics.

Table of Contents

What you will learn, ratings summary, detailed ratings, other information, pros and cons, summary of each module (week).

IBM’s Introduction to Data Analytics will briefly discuss the following topics:

• What data analyst do
• Types of data professionals
• The data analysis process
• Responsibilities of data analysts
• Skills required to become a data analyst
• Day in the life of a data analyst
• Types of data
• Sources of data
• Technologies used by data analysts
• Database types and processing tools
• Data transformation
• Data wrangling and cleaning
• Data visualization
• Statistical analysis and data mining
• Career advice

Overall Rating

IBM’s Introduction to Data Analytics provides a good introduction to the data profession. If you’re interested in learning more about the data profession, this is a good place to start.

If you hope to be regaled by compelling examples or witty statements, you may find this course lacking. The lectures are straightforward and to the point; nonetheless, they are packed with useful information. Aspiring data analysts will be impressed by the variety of content covered by the course.

Depth of Content

The content is not deep; instead, you will learn a little bit about everything. It’s an introductory course so this is not a surprise; nonetheless, some brief exposure to code or functions would have been helpful in elucidating the nature of the type of work data analysts perform.

Variety of Content

Since this is an overview course, you will encounter a HUGE variety of content. You will learn a little bit about everything related to the data profession.

The videos are clear, coherent, and feature many helpful illustrations. Here is an example of the typical PowerPoint slide you will see.

The typical video just includes the PowerPoint slide and a voice describing the slide; however, some videos also include a number of short interview responses from data analyst professionals. These interview responses create a more personal feel by showing the speaker; however, most of the videos do not show the speaker. If seeing the speaker is something that interest you, this may not be the course for you.

The readings are short and mostly just summarize the material contained in the lecture videos.

There is nothing confusing about this course. All the content works as expected, the lectures are clear and coherent, and the learning platform works seamlessly.

The quizzes are short and straightforward. They are basically just checking to make sure that you were listening to the lectures and reading the material. The quizzes are multiple choice and untimed; in addition, you can retake the quizzes up to 3 times every 8 hours. The only somewhat challenging thing about these quizzes is that you often will have to remember details or review lectures to find the precise wording utilized in quiz questions.

Assignments

The last module of IBM’s Introduction to Data Analytics course requires students to complete a peer-graded final assignment. The final assignment involves analyzing a credit card transaction table and a simple line chart. The purpose of the analysis is to discover anomalies that are indicative of credit card fraud. The analysis does not require coding or functions; instead, you simply have to look for abnormal data points then answer a few questions about what you discovered. This is not a long or difficult assignment. It should take no more than an hour to complete. In addition to completing this assignment, you must also review one of your fellow student’s assignments. The decision to choose credit card fraud as a topic for the final assignment is realistic, relevant, and interesting; however, some may find the assignment a bit too short. Peer-reviewed rather than instructor-reviewed is not ideal; however, subjectivity and grading errors are limited since the grading rubric is very specific. Regarding the time required to receive a peer review, my assignment was reviewed about an hour after I submitted it.

Instructors

Rav Ahuja is the creator of the course and listed as the primary instructor. He is a Global Program Director at IBM and leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. The presenter’s (narrator’s) name is Bella West. She is clear, coherent, and speaks neither too fast nor too slow. She doesn’t waste time with fluff or filler; instead, she always remains on topic and delivers the information in a concise manner. There are several data professionals who make brief appearances in this course. They answer career related questions about the profession.

Learning Platform

IBM’s Introduction to Data Analytics, is hosted on Coursera. Coursera is one of the most popular e-learning platforms in the world. It was established in 2012 by Stanford University professors Andre Ng and Daphne Koller. Coursera has many courses and collaborate with leading universities and companies such as Google, Stanford University, IBM, and Meta.

Free Audit Version

The free audit version is impressive. With the free audit version, you can view all the content. You can view videos, read articles, take and submit practice quizzes, use the discussion forum, view graded quizzes, and view peer-graded assignments. The only things you cannot do is submit graded quizzes, submit the peer-graded assignment, and receive a verified certificate. To enroll in the free audit option , make sure to look for a small “Audit the course” option on the bottom of the “Step 2 of 2” window during enrollment.

Unrestricted Free Trial

The unrestricted free trial is 7 days long and allows students to have full access to all the content. You may cancel before the free trial ends; however, if you don’t cancel, you will automatically be charged each month until you cancel.

In the first module of this course, you will learn all about what data professionals do. You will learn about the types of data professionals, the types of analyses, the data analysis process, responsibilities of data analyst, skills required to be a data analyst, and the day in the life of data analyst.

In the second module of IBM’s Introduction to Data Analytics, you will begin to learn more about the specifics of data. For example, you will learn about the different types of data, sources of data, technology used by data analysts, and Big Data platforms. You will also learn about database types and database processing tools.

The first part of module 3 discusses the sources of data and the technology used to collect data from those sources. This is mostly a review of module 2. The next portion of this module discusses how raw data is transformed into a clear, coherent, and accurate form. There are specific steps required to check for problematic data. It is essential to check for things like duplicate data, outliers, inconsistent data types, and syntax errors. Fortunately, there are numerous powerful tools used to wrangle and clean data. In this module, you will learn about some of those tools.

Module 4 introduces students to the methods used for analyzing data and visualizing data. This is one the most interesting modules because it delves into what one primarily thinks of when considering data analysis: statistical analysis and data mining. Statistical analysis applies statistical methods to data in order to develop an understanding of what the data represents. Statistical methods such as mean, median, mode, standard deviation, hypothesis testing, confidence intervals, and regression analysis are briefly discussed. Data mining is the process of extracting knowledge from data. This module summarizes some of the data mining techniques and tools. The last lecture in this module talks about visualization. Visualization is the process of communicating information through elements such as graphs and charts.

The last module of IBM’s Introduction to Data Analytics begins by discussing the various opportunities available to people with data analytics skills. Data professionals from various fields provide first-hand education and career advice. The next part of module 5 is the final assignment. The final assignment requires students to detect and mitigate credit card fraud by analyzing a credit card transaction table. See the “assignments” section for additional details. In addition to completing this assignment, you must also grade a fellow student’s assignment.

IBM’s Introduction to Data Analytics provides a solid introduction to the data profession. If you’re interested in learning more about the data profession, this course is a good place to start. If you’re uncertain about the course, I recommend you enroll in the free audit option . The audit option allows you to access nearly all the content for free.

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Introduction to Data Analytics

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

Some content may not be translated

Instructor: Rav Ahuja

Financial aid available

528,086 already enrolled

(15,049 reviews)

Recommended experience

Beginner level

All you need to get started is basic computer literacy, high school level math, and access to a modern web browser such as Chrome or Firefox.

What you'll learn

Explain what Data Analytics is and the key steps in the Data Analytics process

Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst

Describe the different types of data structures, file formats, and sources of data

Describe the data analysis process involving collecting, wrangling, mining, and visualizing data

Skills you'll gain

• Data Science
• Spreadsheet
• Data Analysis
• Microsoft Excel
• Data Visualization

Details to know

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• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 5 modules in this course

Ready to start a career in Data Analysis but don’t know where to begin? This course presents you with a gentle introduction to Data Analysis, the role of a Data Analyst, and the tools used in this job. You will learn about the skills and responsibilities of a data analyst and hear from several data experts sharing their tips & advice to start a career. This course will help you to differentiate between the roles of Data Analysts, Data Scientists, and Data Engineers.

You will familiarize yourself with the data ecosystem, alongside Databases, Data Warehouses, Data Marts, Data Lakes and Data Pipelines. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you’ll be able to understand the fundamentals of the data analysis process including gathering, cleaning, analyzing and sharing data and communicating your insights with the use of visualizations and dashboard tools. This all comes together in the final project where it will test your knowledge of the course material, and provide a real-world scenario of data analysis tasks. This course does not require any prior data analysis, spreadsheet, or computer science experience.

What is Data Analytics

In this module, you will learn about the different types of data analysis and the key steps in a data analysis process. You will gain an understanding of the different components of a modern data ecosystem, and the role Data Engineers, Data Analysts, Data Scientists, Business Analysts, and Business Intelligence Analysts play in this ecosystem. You will also learn about the role, responsibilities, and skillsets required to be a Data Analyst, and what a typical day in the life of a Data Analyst looks like.

What's included

9 videos 3 readings 4 quizzes 1 discussion prompt

9 videos • Total 39 minutes

• Course Introduction • 2 minutes • Preview module
• Modern Data Ecosystem • 4 minutes
• Key Players in the Data Ecosystem • 5 minutes
• Defining Data Analysis • 5 minutes
• Viewpoints: What is Data Analytics? • 3 minutes
• Responsibilities of a Data Analyst • 4 minutes
• Viewpoints: Qualities and Skills to be a Data Analyst • 4 minutes
• A Day in the Life of a Data Analyst • 5 minutes
• Viewpoints: Applications of Data Analytics • 2 minutes

3 readings • Total 22 minutes

• Data Analytics vs. Data Analysis • 2 minutes
• Summary and Highlights • 10 minutes

4 quizzes • Total 45 minutes

• Graded Quiz • 15 minutes
• Practice Quiz • 9 minutes
• Practice Quiz • 6 minutes

1 discussion prompt • Total 5 minutes

• Introduce yourself • 5 minutes

The Data Ecosystem

In this module, you will learn about the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will gain an understanding of various types of data repositories such as Databases, Data Warehouses, Data Marts, Data Lakes, and Data Pipelines. In addition, you will learn about the Extract, Transform, and Load (ETL) Process, which is used to extract, transform, and load data into data repositories. You will gain a basic understanding of Big Data and Big Data processing tools such as Hadoop, Hadoop Distributed File System (HDFS), Hive, and Spark.

11 videos 2 readings 4 quizzes

11 videos • Total 67 minutes

• Overview of the Data Analyst Ecosystem • 3 minutes • Preview module
• Types of Data • 4 minutes
• Understanding Different Types of File Formats • 4 minutes
• Sources of Data • 7 minutes
• Languages for Data Professionals • 8 minutes
• Overview of Data Repositories • 4 minutes
• RDBMS • 7 minutes
• NoSQL • 7 minutes
• Data Marts, Data Lakes, ETL, and Data Pipelines • 6 minutes
• Foundations of Big Data • 5 minutes
• Big Data Processing Tools • 6 minutes

2 readings • Total 20 minutes

4 quizzes • total 66 minutes.

• Graded Quiz • 18 minutes
• Practice Quiz • 15 minutes
• Practice Quiz • 18 minutes

Gathering and Wrangling Data

In this module, you will learn about the process and steps involved in identifying, gathering, and importing data from disparate sources. You will learn about the tasks involved in wrangling and cleaning data in order to make it ready for analysis. In addition, you will gain an understanding of the different tools that can be used for gathering, importing, wrangling, and cleaning data, along with some of their characteristics, strengths, limitations, and applications.

7 videos 2 readings 4 quizzes

7 videos • Total 39 minutes

• Identifying Data for Analysis • 5 minutes • Preview module
• Data Sources • 4 minutes
• How to Gather and Import Data • 6 minutes
• What is Data Wrangling? • 6 minutes
• Tools for Data Wrangling • 5 minutes
• Data Cleaning • 6 minutes
• Viewpoints: Data Preparation and Reliability • 4 minutes

4 quizzes • Total 48 minutes

Mining & visualizing data and communicating results.

In this module, you will learn about the role of Statistical Analysis in mining and visualizing data. You will learn about the various statistical and analytical tools and techniques you can use in order to gain a deeper understanding of your data. These tools help you to understand the patterns, trends, and correlations that exist in data. In addition, you will learn about the various types of data visualizations that can help you communicate and tell a compelling story with your data. You will also gain an understanding of the different tools that can be used for mining and visualizing data, along with some of their characteristics, strengths, limitations, and applications.

8 videos 2 readings 4 quizzes

8 videos • Total 44 minutes

• Overview of Statistical Analysis • 8 minutes • Preview module
• What is Data Mining? • 5 minutes
• Tools for Data Mining • 6 minutes
• Overview of Communicating and Sharing Data Analysis Findings • 5 minutes
• Viewpoints: Storytelling in Data Analysis • 3 minutes
• Introduction to Data Visualization • 5 minutes
• Introduction to Visualization and Dashboarding Software • 7 minutes
• Viewpoints: Visualization Tools • 3 minutes

Career Opportunities and Data Analysis in Action

In this module, you will learn about the different career opportunities in the field of Data Analysis and the different paths that you can take for getting skilled as a Data Analyst. At the end of the module, you will demonstrate your understanding of some of the basic tasks involved in gathering, wrangling, mining, analyzing, and visualizing data.

8 videos 4 readings 2 quizzes 1 peer review

8 videos • Total 33 minutes

• Career Opportunities in Data Analysis • 5 minutes • Preview module
• Viewpoints: Get into Data Profession • 3 minutes
• Viewpoints: What do Employers look for in a Data Analyst? • 5 minutes
• The Many Paths to Data Analysis • 4 minutes
• Viewpoints: Career Options for Data Professionals • 3 minutes
• Viewpoints: Advice for aspiring Data Analysts • 3 minutes
• Viewpoints: Women in Data Professions • 3 minutes
• Generative AI for Data Analytics  • 4 minutes

4 readings • Total 32 minutes

• Using Data Analysis for Detecting Credit Card Fraud • 10 minutes
• Congratulations and Next Steps • 2 minutes
• Course Credits and Acknowledgements • 10 minutes

2 quizzes • Total 21 minutes

1 peer review • total 60 minutes.

• Peer-Graded Final Assignment • 60 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. For more information about IBM visit: www.ibm.com

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Learner reviews

Showing 3 of 15049

15,049 reviews

Reviewed on Nov 16, 2020

Course is really helped me understand the concept of Data Analytics. The viewer's points explained What, Why, and how Data Analytics. And the final assignment gives an exact idea about Data analysis.

Reviewed on Sep 3, 2022

Good informative course, could be a little more interactive. While each section had quick test at the end, it would've been nice to have had more engaging questions and activities throughout.

Reviewed on Mar 13, 2021

Great general and broad information on data analytics. Gives good ideas and examples of career paths that can be followed. I especially liked how it ranked the various careers and specializations.

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More questions

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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Introduction to Data Analytics for Business Coursera Quiz Answers

Introduction to Data Analytics for Business – Coursera

4.7 stars  (2,037 ratings)  , instructor:  david torgerson, week-1 quiz answer, week-2 quiz answer, week-3 quiz answer.

1.I n this quiz, you’ll be writing queries based on the following database. Follow this link to access a larger picture of the database.

Note: your SQL code entries will not be saved between quiz attempts! Please copy paste them somewhere so you don’t have to retype the entire code when you take the quiz again.

2. Write a query that provides a list of all planes that have a seat count of 100 or more, ordered from lowest to highest number of seats.

3. Write a query that provides the number of flights flown by each aircraft.

5. Write a query that provides the total number of flights by country.

7. How many CITY_PAIRS are there which depart from one of the following airports?

Refer to the following video if you need a refresher: video 6.

Week-4 Quiz Answers

Peer-graded assignment: final course assignment.

Part 2: Relational data model. Take a subset of the ideas from the conceptual model you constructed in Part 1 and design a simple relationship model similar to the ones we discussed in Module 2, Video 4

Part 4 : Sensitive data and data quality issues. Consider the data privacy and data quality implications of the data model you constructed in Part 2

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Final Peer Graded Assignment

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