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Data Analytics Foundations for Accountancy II

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community.

To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class.

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community.

If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center.

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What's inside

Syllabus

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Assists professionals in the accounting industry by offering practical expertise and implementation support

Incorporates popular programming languages and packages, such as Python and Sci-kit Learn, to enhance the practical utility of its teachings

Explores the most up-to-date theories and methodologies in machine learning, staying at the forefront of the industry

Provides a comprehensive guide to all aspects of the machine learning process, including data preparation, model selection, and result interpretation

Demonstrates the application of machine learning in the real world, preparing students for job application

Led by Robert J. Brunner, who has extensive experience in the field and is recognized for his contributions to machine learning

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Reviews summary

Data analytics for accountancy

According to learners, this course provides a solid foundation in applying data analytics and machine learning algorithms to the field of accountancy. Many found the coverage of fundamental algorithms like logistic regression, decision trees, clustering, and anomaly detection particularly useful for potential applications such as fraud detection. The hands-on Python labs are frequently highlighted as a valuable component, offering practical experience. However, a significant number of students noted that the course assumes a high level of proficiency in Python, which can be a major hurdle if not stated clearly as a prerequisite. Some also expressed a desire for more detailed, specific accountancy case studies integrated throughout the course to strengthen the link between the theoretical concepts and their practical application in the field.

Material is dense; pace can feel fast for some.

"The lectures sometimes felt rushed, especially when explaining the mathematical underpinnings."

"It's a lot of material packed into eight modules, sometimes feeling overwhelming."

"The instructor moves too fast, especially during coding demos."

Could benefit from more specific accountancy examples.

"While the accountancy examples were present, I wished there were more detailed case studies relevant to the field."

"My main critique is that the connection to accountancy could be stronger throughout, perhaps with more real-world datasets or case studies from the field."

"The link to accountancy felt tenuous in some modules."

Covers a wide range of fundamental ML algorithms.

"A solid introduction to several key ML algorithms. The course covers a wide range of topics."

"Good coverage of fundamental algorithms. I particularly appreciated the discussion on evaluating model performance..."

"This one dives deeper into practical algorithms and concepts like feature engineering and ensemble methods."

Course content is useful for accountancy applications.

"Excellent course that bridges the gap between data analytics concepts and their application in accounting."

"The most useful parts for me were the modules on clustering and anomaly detection, which have direct applications in fraud detection."

"An excellent course... helps you understand how they can be applied [in accountancy]."

Assumes significant prior Python programming skill.

"Some of the Python code examples were a bit complex for a 'foundations' course, assuming more prior coding experience than I had."

"Very difficult course if you are not already proficient in Python. The focus seemed more on the programming..."

"I struggled with the programming assignments in Python, feeling that stronger coding prerequisites should have been stated."

"Completely lost in this course... The assignments were frustratingly difficult without significant external help."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Analytics Foundations for Accountancy II with these activities:

Review Basic Machine Learning Concepts

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Reviewing core concepts in machine learning can help ensure a strong understanding of the foundation of this course.

Browse courses on Machine Learning

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Review linear regression concepts and algorithms.
Go over neighbor-based algorithms, such as k-nearest neighbor.
Refresh your knowledge of logistic regression and its role in classification.

Refresh your Python skills

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Review foundational Python concepts and techniques to ensure proficiency before starting the course.

Browse courses on Python

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Review basic syntax and data types
Practice data manipulation and cleaning techniques
Explore data visualization libraries and techniques

Review the book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

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Gain a deeper understanding of machine learning concepts and practical implementation techniques through a detailed review of this comprehensive book.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the book thoroughly, taking notes and highlighting important concepts
Engage with the exercises and examples provided in the book to reinforce your understanding
Create a summary or mind map that captures the key takeaways from each chapter

Six other activities

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Join a study group to work on course exercises and projects together

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Collaborate with peers to reinforce course concepts, discuss different perspectives, and enhance your problem-solving abilities.

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Find or form a study group with other students
Meet regularly to work on course exercises and projects together
Discuss different approaches and solutions, and learn from each other's perspectives

Follow online tutorials on advanced machine learning techniques

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Enhance your understanding of complex machine learning topics by seeking out and following guided tutorials that delve into specific algorithms and techniques.

Browse courses on Machine Learning

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Identify specific topics or techniques you want to explore further
Find reputable online tutorials or courses that cover these topics
Complete the tutorials and apply the learned techniques to your own projects

Solve LeetCode problems related to machine learning algorithms

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Challenge yourself and refine your problem-solving skills by tackling LeetCode problems that test your understanding of machine learning concepts.

Browse courses on Machine Learning

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Select LeetCode problems that cover specific machine learning algorithms or concepts
Read and understand the problem statement and requirements
Implement the solution using appropriate machine learning techniques
Debug and refine your solution to meet the time and space complexity requirements

Develop a data analysis dashboard for a non-profit organization

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Showcase your data analysis skills by creating a visual and interactive tool that helps a non-profit organization understand and communicate their impact.

Browse courses on Data Visualization

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Meet with the non-profit organization to understand their needs
Design and develop the dashboard using appropriate tools
Present the dashboard to the non-profit organization and gather feedback

Create a blog post or article summarizing the key concepts of machine learning

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Solidify your understanding by explaining machine learning concepts in your own words and sharing your knowledge with others.

Browse courses on Machine Learning

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Choose a specific topic or aspect of machine learning to focus on
Research and gather information on the topic
Write a clear and concise blog post or article that explains the topic
Publish your article and share it with others

Build a machine learning model to predict customer churn

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Apply course concepts to a practical project, gaining hands-on experience in building and evaluating machine learning models.

Browse courses on Machine Learning

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Gather and pre-process real-world customer data
Choose and implement appropriate machine learning algorithms
Evaluate model performance and refine the model as needed
Communicate findings and insights from the model

Career center

Learners who complete Data Analytics Foundations for Accountancy II will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts gather data, use analytics to interpret that data, and communicate findings to help businesses make better decisions. They collaborate with other departments to find ways to make processes more efficient by streamlining collection and analysis techniques. This course in Data Analytics Foundations for Accountancy II will help build foundational skills in machine learning, data analysis, and statistics. These skills are essential for Data Analysts who use data to identify trends, patterns, and deviations that can then be used to help businesses address unmet needs.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists work with enormous volumes of data, structured or unstructured, to uncover patterns, trends, and actionable insights. They combine their knowledge of mathematics, statistics, and programming to develop and implement algorithms and models to help businesses translate this data into strategic advantages. This course will help students develop the skills they need to clean, process, analyze, and interpret large and complex datasets, which are core skills for Data Scientists.

See salaries and explore the career path for Data Scientist

Financial Analyst

Financial Analysts analyze business performance using data and make recommendations for investment strategies. They are typically involved in advising on mergers and acquisitions, issuing stock recommendations, and analyzing the performance of companies or entire industries. This course will provide foundational knowledge and skills in machine learning, data analysis, and statistics, which are in high demand in the financial industry.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

Market Research Analysts study market conditions to help businesses make better decisions about their products, services, and marketing strategies. They collect and analyze data from a variety of sources, including surveys, questionnaires, and interviews. This course covers important data analysis techniques including linear regression, decision trees, and support vector machines, which Market Research Analysts use to analyze market trends and customer behavior.

See salaries and explore the career path for Market Research Analyst

Business Analyst

Business Analysts use their knowledge of business processes and data analysis techniques to help organizations improve their performance. They work with stakeholders to identify and understand business needs, and then use data to develop solutions that can improve efficiency and profitability. This course will build the skills needed to collect, analyze, and interpret data, which are essential for Business Analysts in identifying opportunities for improvement and making recommendations for change.

See salaries and explore the career path for Business Analyst

Accountant

Accountants manage the financial records of businesses, ensuring that they are accurate and compliant with regulations. They analyze financial data to help businesses understand their performance and make sound financial decisions. This course will help build a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are increasingly being used by Accountants to improve the accuracy and efficiency of financial reporting.

See salaries and explore the career path for Accountant

Marketing Manager

Marketing Managers plan and execute marketing campaigns to promote products and services. They oversee all aspects of marketing, including market research, advertising, public relations, and sales promotion. This course covers important data analysis techniques that are essential for Marketing Managers to understand market trends, customer behavior, and marketing campaign effectiveness.

See salaries and explore the career path for Marketing Manager

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. They work with businesses to improve efficiency, productivity, and profitability by analyzing data and developing recommendations for improvement. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Operations Research Analysts to develop and implement effective solutions.

See salaries and explore the career path for Operations Research Analyst

Risk Manager

Risk Managers identify, assess, and manage risks that can impact a business. They work with businesses to develop and implement risk management strategies to minimize the impact of potential risks. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Risk Managers to develop and implement effective risk management strategies.

See salaries and explore the career path for Risk Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They work with hedge funds, investment banks, and other financial institutions to develop and implement trading strategies. This course will build a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Quantitative Analysts to develop and implement effective trading strategies.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses make better decisions. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Statisticians to design and conduct effective statistical studies.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers design and build the infrastructure that is used to store and process data. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Data Engineers to develop and implement efficient and scalable data storage and processing systems.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with businesses to develop software solutions that meet their specific needs. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which can be applied to develop software applications that can analyze and interpret data.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning models to solve complex business problems. They work with businesses to identify and solve problems that can be addressed using machine learning techniques. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Machine Learning Engineers to develop and implement effective machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

Data Architects design and build data architectures that enable businesses to manage and use their data effectively. They work with businesses to develop data strategies and ensure that data is available and accessible for analysis. This course will provide a foundation in data analysis techniques, including linear regression, decision trees, and support vector machines, which are essential for Data Architects to develop and implement scalable and efficient data architectures.

See salaries and explore the career path for Data Architect

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Analytics Foundations for Accountancy II.

Deep Learning

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Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, from supervised and unsupervised learning to Bayesian inference.

Deep Learning: Foundations and Concepts

Hardcover

$$$

Probabilistic Machine Learning

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Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers a wide range of topics, from Bayesian inference to supervised and unsupervised learning.

Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

The Elements of Statistical Learning

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Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from linear regression to support vector machines.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

Provides a comprehensive introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, from data preprocessing to model deployment.

Data Analytics Foundations for Accountancy II

Two deals to help you save

What's inside

Syllabus

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Save this course

Reviews summary

Data analytics for accountancy

Activities

Career center

Reading list

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