Program Overview
This program will teach you core computer science competencies in programming and data structures. Understanding how programming works is essential in many technical disciplines such Information Technology, Software Engineering, Cybersecurity, and Computer Science. The courses utilize the C++ programming languages to establish a solid foundation in programming and data structures for the students. Students gain valuable hands-on experience programming solutions to problems in the labs. I the labs, students will practice their core programming skills and will also develop many advanced data structures including, hashtables, sorting and search algorithms, binary trees, AVL trees, graph algorithms and many more advanced computing topics. In addition to the applied programming labs, students will also gain an understanding of computational complexity through the analysis of the data structures and programs that are developed.
What you will learn
- Identify and explain a programming development lifecycle, including planning, analysis, design, development, and maintenance.
- Demonstrate a basic understanding of object-oriented programming by using structures and classes in software projects.
- Use object-oriented programming techniques to develop executable programs that include elements such as inheritance and polymorphism.
- Document and format code in a consistent manner.
- Apply basic searching and sorting algorithms in software design.
- Apply single-and multi-dimensional arrays in software.
- Use a symbolic debugger to find and fix runtime and logical errors in software.
- Demonstrate a basic understanding of programming methodologies, including object oriented, structured, and procedural programming.
- Describe the phases of program translation from source code to executable code.
- Design and develop programs that utilize linked lists to store data internally.
- Design and develop programs that utilize stacks and queues to manage collections of data.
- Design and develop programs that recursion to solve problems that can be expressed with recurrence.
- Utilize binary search trees and balanced trees to implement fast retrieval of data from a collection of data stored in memory.
Course List
1Introduction to Programming in C++
Course Details
1
Learn the fundamentals of programming in the C++ programming language, including iteration, decision branching, data types and expression.
2Advanced Programming in C++
Course Details
2
Learn the advanced programming topics in the C++ programming language, including functions, computation complexity, arrays and strings.
3Introduction to Data Structures
Course Details
3
Learn the advanced programming topics in the C++ programming language, including pointers, dynamic storage, recursion, searching, and sorting.
4Advanced Data Structures
Course Details
4
Learn the advanced programming topics in the C++ programming language, including file processing, linked lists, stacks, queues, trees, binary search trees and tree balancing algorithms.
Meet Your Instructors
Aspen Olmsted
Adjunct Professor at New York University Tandon School of Engineering
Aspen Olmsted is an adjunct faculty member in the New York University Tandon School of Engineering in the Computer Science and Engineering department. Aspen's fulltime job is as an assistant professor and Graduate program director at the College of Charleston. He obtained a Ph.D. in Computer Science and Engineering from The University of South Carolina. Before his academic career, he was CEO of Alliance Software Corporation. Alliance Software developed N-Tier enterprise applications for the performing arts and humanities market. Dr Olmsted’s research focus is on the development of algorithms and architectures for distributed enterprise solutions that can guarantee security and correctness while maintaining high-availability. In his Secure Data Engineering Lab, Aspen mentors over a dozen graduate and undergraduate students each year
Itay Tal
Industry Assistant Professor at New York University Tandon School of Engineering
Tel-Aviv University 2005
M.Sc., Computer Science
Tel-Aviv University 1998
B.Sc., Computer Science and Mathematics
Program overview
Accountants with deep financial expertise, business acumen, real-world experience, decision-making ability, and effective communication skills are vital to businesses of all sizes and have tremendous career opportunities. This MicroMasters® program in Accounting is designed for smart, hardworking students with integrity and passion who want to substantially enhance their understanding of accounting. The program curriculum includes courses in the three most fundamental areas of accounting: financial accounting, managerial accounting, and income taxes. Learn from world-renowned, award-winning accounting faculty at the top-ranked Kelley School of Business at Indiana University. The MicroMasters program will introduce you to the following key areas in the accounting profession:
Financial Reporting I – How do firms measure and report financial position and financial performance in a set of financial statements?
Managerial Accounting and Corporate Control – How do firms make critical strategic decisions using accounting information?
Taxes and Decision Making – How do income taxes impact firms, their strategies, capital structure, and after-tax profits?
The three graduate-level courses in the MicroMasters program represent 30% of the coursework in the online Master of Science in Accounting from the Kelley School of Business at Indiana University offered on the edX platform.
The online Master of Science in Accounting program is designed for individuals who want to transition to an accounting career and for individuals who want to deepen their understanding of accounting’s role in investing, banking, and corporate management. The Kelley School of Business is currently ranked #7 for graduate accounting programs by Public Accounting Report.
Completing this MicroMasters program in accounting can strengthen your application to the online Master of Science in Accounting program, as well as advance your career.
What you will learn
- What information do firms measure and report in a wide array of transactions, events, and arrangements in balance sheets, income statements, and statements of cash flows?
- What are key elements of financial reporting, such as revenues, expenses, gains and losses, net income, assets, liabilities, and owners’ equity?
- How do firms develop and use key internal information such as product costs, cost allocations, customer profitability, budgets, and planning to execute and control firm strategy?
- How do income taxes impact firms? How do firms measure and report taxable income and taxes payable? And how do firms strategically plan for and manage the impact of taxes on profitability?
- Accounting standards, including U.S. Generally Accepted Accounting Principles (U.S. GAAP) and International Financial Reporting Standards (IFRS).
Program Class List
1Financial Reporting I
Course Details
1
Earn a strong foundation in financial reporting concepts and methods, and use your skills to prepare and analyze financial statements.
2Managerial Accounting and Corporate Control
Course Details
2
How do managers use internal accounting data to make decisions? In this course, you’ll discover how to prepare and analyze accounting information to make complex business decisions.
3Taxes and Decision Making
Course Details
3
How do you reduce a company or individual's taxes while complying with tax laws? Designed for accounting students, this course focuses on understanding the legal, conceptual, and integrative aspects of U.S. federal income taxation.
Meet Your Instructors
Ken Merkley
Associate Professor of Accounting at Indiana University
Ken Merkley is an Associate Professor (with tenure) in the Accounting Department at the Kelley School of Business at Indiana University. His research focuses on the role of information in capital markets. He specializes in examining corporate financial communication decisions and the influence of external capital market participants, such as investors, financial analysts, auditors, and lawyers. He serves on the editorial board of The Accounting Review and referees for top journals in accounting and finance. Prior to joining Kelley, he served on the faculty of Cornell University, where he was a distinguished teacher and researcher at the Johnson Graduate School of Business. He received the Barry and Ann Riding Fellowship and the Half Century Faculty Research award. In 2014, he was named by Poets & Quants as one of the Top 40 Business School Professors Under 40. He received his PhD from the University of Michigan and received his Master of Accountancy (MAcc) from Brigham Young University.
Brian P. Miller
Associate Professor of Accounting at Indiana University
Brian P. Miller is the PwC Faculty Fellow and Associate Professor at the Kelley School of Business at Indiana University. He received his Ph.D. from the Pennsylvania State University and has taught for more than 15 years at a number of universities.
Professor Miller’s teaching interests focus on financial and managerial accounting an emphasis on decision making, where he is able to combine his practical experience with his research interests.He has taught a variety of MBA, Executive MBA, and PhD programs. In addition to more traditional classroom learning environments, he has had the opportunity to lead groups of MBA students to Guatemala, where students can apply the skills they learn in the classroom to consulting projects. He has received numerous teaching awards during his career.
Prior to becoming an academic, he worked at public accounting and as a finance manager at Procter & Gamble. In particular, he served as a financial analyst in fabric care, a cost analyst in the company’s new business development group, and as a cost forecaster for a several billion dollar segment of the company. During his time at Procter & Gamble he was awarded the Outstanding Achievements in Fabric Care in Finance and Accounting.
His research is focused on measuring managerial talent, management disclosure decisions, and the penalties managers pay for misreporting. His work has been published in several prestigious journals including The Accounting Review, The Journal of Accounting and Economics, Management Science, and The Review of Accounting Studies. He serves as an Associate Editor at Management Science and on the editorial boards at several other prestigious journals. He is also the recipient of the awarded American Accounting Association’s Notable Contribution to the Accounting Literature Award.
In his free time, Brian loves spending time with his wife and kids, traveling, and competing in triathlons.
Greg Geisler
Clinical Professor of Accounting at Indiana University
Greg Geisler, PhD, CPA is a Clinical Professor of Accounting at Indiana University (Bloomington), where he teaches income tax courses.
At his former institution, the University of Missouri-St. Louis (UMSL), Greg won the Chancellor’s Teaching Excellence Award in 2017 and the Governor’s Excellence in Teaching Award in 2006. Both are awarded to only one UMSL professor per year.
He holds a PhD from the University of North Carolina at Chapel Hill, an MBA from the University of Pittsburgh, and a Bachelor’s degree from the University of Notre Dame.
Before entering academia full-time, he was a practicing CPA working in public accounting for 5 years.
Testimonials
Program Overview
This MicroMasters program is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems through implementing over one hundred algorithmic coding problems in a programming language of your choice.
No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we have invested thousands of hours designing challenges as an alternative to multiple choice questions that you usually find in MOOCs. We believe in learning through application, especially when it comes to learning algorithms.
For each algorithm you develop and implement, we have designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming.
What you will learn
- Understand essential algorithmic techniques and apply them to solve algorithmic problems
- Implement programs that work in less than one second even on massive datasets
- Test and debug your code even without knowing the input on which it fails
- Formulate real life computational problems as rigorous algorithmic problems
- Prove correctness of an algorithm and analyze its running time
Program Class List
1Algorithmic Design and Techniques
Course Details
1
Learn how to design algorithms, solve computational problems and implement solutions efficiently.
2Data Structures Fundamentals
Course Details
2
Learn how to design algorithms, solve comLearn about data structures that are used in computational thinking – both basic and advanced.putational problems and implement solutions efficiently.
3Graph Algorithms
Course Details
3
Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.
4NP-Complete Problems
Course Details
4
Learn about NP-complete problems, known as hard problems that can’t be solved efficiently, and practice solving them using algorithmic techniques.
5String Processing and Pattern Matching Algorithms
Course Details
5
Learn about pattern matching and string processing algorithms and how they apply to interesting applications.
6Dynamic Programming: Applications In Machine Learning and Genomics
Course Details
6
Learn about data structures that are used in computational thinking – both basic and advanced.
7Graph Algorithms in Genome Sequencing
Course Details
7
Learn how to use algorithms to explore graphs, compute shortest distance, min spanning tree, and connected components.
8Algorithms and Data Structures Capstone
Course Details
8
Synthesize your knowledge of algorithms and biology to build your own software for solving a biological challenge.
Meet your instructors
Pavel Pevzner
Ronald R. Taylor Professor of Computer Science
The University of California, San Diego
Daniel Kane
Assistant Professor,
Computer Science and Engineering & Dept. of Mathematics
UC San Diego
Alexander S. Kulikov
Visiting Professor
UC San Diego
Michael Levin
Chief Data Scientist
Yandex.Market
Neil Rhodes
Lecturer
UC San Diego
Phillip Compeau
Assistant Teaching Professor
Carnegie Mellon University
