*The wordcloud above is taken on the descriptions below. The only modifications were to group by words with same roots, remove stop words, and apply a minimum frequency.

 

Columbia University(Cum GPA 4.13)

Masters of Science in Applied Analytics 

APAN 5900 CAPSTONE PROJECT                                                                  (In Progress)

The course serves as the capstone project for the MSAA degree. As an industry-driven project, the capstone requires synthesis of program content applied to real-world challenges to apply the leadership, strategic management, communication and modern analytics core coursework to industry-sponsored analytics projects. Working in teams, with faculty and industry sponsors, students critically assess a company’s real-world data challenges and opportunities, formulate a problem definition, derive insights and develop an integrated data-savvy analytics plan and solution. The project helps students develop and apply the technical, leadership, and communication skills required to identify and implement solutions/approaches and make a great impression on sponsoring client organizations.

“Utilizing NLP (natural language processing) to analyze enforcement actions and identify risky financial institutions” project with Accenture.

APAN 5335 Machine Learning                                                                       (In Progress)

In recent years, machine learning techniques have made significant impact in a wide range of application areas in various industries. This course provides an introduction to machine learning concepts and algorithms, as well as the application areas. Topics will include supervised and unsupervised learning, learning theory etc.

APAN 5420 Anomaly Detection                                                                    (In Progress)

Anomaly detection helps in the early detection of critical outliers in a system. Based on the context, these outliers can be detrimental and result in loss of resources and time through errors, fraud, manipulation of stocks, and other such malicious activities. Outliers can also be beneficial for example in investing, and arbitrage. Business decisions that leverage anomaly detection, which used to require intense human resource and capacity, can now be completed in a short time through versatile models and automation.

In this course, students will learn how to find these unusual occurrences in the data. Students will be provided hands-on experience in multiple contexts with complex datasets that they must further manipulate through industry-specific data engineering. This course will enable students to build advanced supervised and unsupervised machine learning models to find these anomalies.

APAN 5500 Data Visualization and Design                                                  (In Progress)

This course provides an overview of modern data visualization and design theories, methods and techniques, which will allow the student to simplify complex data and analytics, and improve comprehension, communication, and decision-making.

Students will study the principles governing visual representations of data and analysis from graphic design, visual art, perceptual psychology, and cognitive science and they will learn techniques for creating effective visualizations based on those principles.

Students will develop a broad-based understanding of the history of data visualization and will learn about the latest data-visualization techniques. Lectures will be accompanied with hands-on experience with data and data visualization, including tools such as Tableau, R, D3 and ViVA.

——————————————————-  COMPLETED  ———————————————————-

APAN 5100 Applied Analytics in Organizational Context                                                4.33

Applied analytics is about the strategic use of data for decisions within a given environment. This course helps students understand the organizational environment in which their practice exists. As such, it is the introductory course to the professional practice of applied analytics and the first course in the leadership sequence.

The course focuses on the attributes of organizations and the overall ecosystem in which they operate. Students learn about the management structures, mechanisms for decision making, and leadership models with which an analytical operation must fit. Students also learn about the broader context—economic, technological, social, and demographic—as well as trends in how analytics are used and organized in modern organizations. Its aim is for students to understand the professional practice of analytics, including an understanding of business requirements for analytics. Students will define the “analytical maturity” of the organization and describe the impact that growing its capabilities can have on its place in the environment.

APAN 5200 Applied Analytics Frameworks and Methods                                     4.0

This course covers foundational data analytics concepts and techniques to help organizations turn data into informative insights. The chain of inferences leading from data collection to application for decision-making constitutes a comprehensive and coherent validation framework for the use of data to inform real-life problems. The course covers techniques for addressing a set of claims about a problem based on data such as exploratory data analysis, regression, and decision tree learning. It introduces computational methods in statistics, natural language processing and machine learning, and how these techniques are integrated and deployed within database frameworks to effectively harness the power of data analytics in an organization.

Students learn how different analytic techniques are used to address critical data issues facing an organization and how best to apply those methods. Students have the opportunity to apply these analytic techniques to real problems in specific industries associated with their area of interest.

APAN 5300 Research Design                                                                                                 4.33

Organizations need quantitative analysis to solve complex problems and make consequential choices. Research design provides both a coherent framework for collecting relevant evidence and strategies for evaluating that evidence. Knowledge of research design enables organizations to make adaptive and effective use of quantitative analysis in solving problems and making choices. This course serves as a foundational course in the Applied Analytics program.

In this course, you will approach problems as methodological thinkers: you will assess whether the organization is asking the right questions, choosing a relevant design, gathering appropriate and meaningful evidence, and using the appropriate statistical analysis to answer those questions.

APAN 5700 Analytics and Leading Change                                                                           4.0

The successful implementation of analytics depends not only on developing good insights and good strategy, but is also an exercise in managing the necessary changes. The inspiring stories about the importance of analytics today are about how what was learned through analytics was actually implemented to enable an organization to improve its operations, effectiveness, or return on investment.

This course–the third in the sequence of analytics leadership core courses—is about changing the behavior and the culture of organizations, with particular emphasis on how to successfully introduce the methods and results of analytics. Students explore the motivations, obstacles and interventions of change, and learn to build alliances, facilitate difficult meetings and develop a transformation plan. The course focuses on practical skills as they are being developed at organizations with pioneering analytics capabilities today.

Students will review some of the most important academic research and business publications on change management and the implementation of analytics. However, the course is also intended to enhance practical skills, so students will engage in some real-world practice and role-playing with classmates. As they master each module, students will incrementally develop a plan to introduce analytics into the organization where you currently work, or have worked, or hope to work.

APAN 5400 Modern Database Architecture                                                                          4.33

Great managers of analytic projects are more than mere data users; they are key decision makers and strategic owners in the underlying data processes. This course provides students with foundational context for managing data so that it can be leveraged and used with confidence.

Analytic teams work closely with technology partners in managing data. Languages and techniques unique to each team can impede cooperation. To bridge this gap, this course provides a broad overview of data technology concepts including database engines and associated technologies.

Sound policies and procedures are also essentials to ensure high quality of data throughout the analytics lifecycle. But the challenges of putting these measures into practice are significant. There are often legacy repositories and business functions to unravel, as well as social and political barriers to overcome. Data ownership and accountability are hard to implement. Operational disruption and conflicting stakeholder requirements pose additional barriers.

This course will expose students to foundational data principles, governance processes and organizational prerequisites needed to overcome challenges to ensure data quality.

APAN 5600 Strategic Leadership                                                                                           4.0

This course focuses on the step after insights have been generated from data, and asks the question: what needs to change in an organization’s strategy to benefit from those insights? It is the second in the sequence of analytics leadership core courses.

Students will learn how to evaluate the strategic environment, the strategic models that might be useful for their organization, and the implementation of a strategy. The course will also ask students to learn theory and research findings and then apply what they have learned to real situations. This will include an exercise in strategic business “wargaming.”

Having developed an understanding of organizational strategy, special emphasis is then placed on the interplay between analytics and strategic considerations in an organization. The course teaches students about the practical application of analytics to strategic thinking on two levels: that of the organization (how are analytics used to drive the organization’s strategy?) and the analytics team (how is the organization’s strategy driving the activity of the analytics team?).

APAN 5800 Storytelling with Data                                                                                             4.0

Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).

Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.

ERMC 5580 Bayesian Data Analysis                                                                                           4.0

An introduction to Bayesian data analysis. Includes a concise introduction to required concepts in probability and computing with R. Focuses on hands-on use of Bayesian methods. Specific topics include: Introduction to the Bayesian statistics; Elementary Bayesian Computation; Bayesian Hierarchical Models; Bayes Factors; Monte Carlo; Markov chain Monte Carlo MCMC Diagnostics; Sequential Monte Carlo; Bayesian Networks; Bayesian Causal Modeling. In order to gain entry, your resume should reflect a math/statistics background and familiarity with R programming.

 

University Of Washington(Cum GPA 3.88)

Business Finance and Economics

B BUS 310 MANAGERIAL ECON                                                                                             4.0

Applies economic principles and quantitative methods to improve managerial decision making. Topics covered include: demand analysis, cost analysis, forecasting, asset valuation, information economics, government regulation of business.

B BUS 320 MARKETING MGMT                                                                                             3.8

Focuses on designing tools, concepts, and strategies for problem solving in marketing management.

B BUS 412 ADV BUSINESS LAW                                                                                             3.8

In-depth study of legal resolutions including courts, alternative dispute resolution and ethics; creditors’ rights and bankruptcy; agency and employment; corporations and securities; small business and owners limited liabilities; and government regulation of business.

B BUS 307 W-BUSINESS WRITING                                                                                         3.9

Provides theoretical and practical approaches to being a better ethical writer to prepare students to be more successful in business or other organizations.

B BUS 340 OPERTN & PROJ MGMT                                                                                       4.0

Examines service and manufacturing processes that deliver value to customers, introduces concepts and tools for critical analysis, emphasizes operating priorities (quality, cost, delivery, flexibility, social responsibility) an the underlying factors that support them.

B BUS 350 BUSINESS FINANCE                                                                                               4.0

Focuses on understanding the sources, uses, costs, and control of funds in business organizations. Issues include the internal management of working capital, sources of capital, financing new ventures, capital budgeting, and financing the growth of businesses.

B BUS 499 W-UNDERGRAD RESEARCH                                                                                 4.0

Research paper exploring unified growth theory in developmental economics.

BIS 347 HIST AM DOCUM FILM                                                                                             4.0
Exploration of the important technological and cinematic innovations of non-fiction films within their

cultural contexts, and examination of theoretical issues such as objectivity and the blurred line between fact

and fiction. Stresses the skills necessary for the critical evaluation and interpretation of documentary films.

BISLEP 301 W-LAW ECON PUB POLICY                                                                                  3.9

Examines the relationships among the fields of law, economics, politics, and public policy, with particular attention to problems of social, economic, and political change. Uses examples from various areas of public policy, including social, environmental, and education policy.

B BUS 454 INVESTMENTS                                                                                                      4.0

Introduction to the nature, problems, and process of evaluating particular securities and portfolio construction and administration. Special attention is directed to the risk and rate of return aspects of particular securities portfolios; and total wealth.

B BUS 480 GLOBAL ENV OF BUS                                                                                            4.0

Focuses on the major changes and issues facing businesses and managers operating in an increasingly global environment. Emphasizes topics such as trade policy, accelerating advances in technology, the changing nature of the work force, and societal expectations of business. Problems and issues from the perspective of directing the entire business enterprise.

B BUS 490 SPECIAL TOPICS (Quantitative Methods in Economics)                                     4.0

            Through studying and completing the homework assignments, the successful student in

this course will acquire training in formulating and understanding quantitative arguments,

and the mathematical tools that are essential to formulate and understand economic problems.

By the end of the course, the successful student will demonstrate the ability to:

• Formulate typical economic problems in mathematical language, solve them, and give an economic interpretation of the mathematical results;
• Compute equilibrium prices and quantities in both competitive and non-competitive markets, given supply and demand functions;
• Employ differential calculus to operationalize the economic principle of marginal analysis, and apply this principle to solve economic problems;
• Apply the tools of constrained and unconstrained mathematical optimization to the problem of profit maximization and cost minimization by firms, and of preference maximization subject to a budget constraint by consumers;
• Employ integral calculus to operationalize the notion of welfare in competitive markets, and to assess the impact of public policy and government intervention.

B BUS 441 BUS PROJECT MGT                                                                                               4.0

In-depth coverage of skills that prepare students for rules as business project leaders and team members. Topics include project selection, risk, definition, stakeholder analysis, communication plans, scheduling, software, resource allocation, monitoring, post-project assessment. Emphasis on critical thinking and analysis.

B BUS 459 SPEC TOPIC FINANCE (Actuarial Risk Management)                                          4.0

The course is an introduction to probability theory and basic risk modelling. Its goal is

to help students understand the theoretical foundations of modern financial economics and risk management. The main theme is the allocation and pricing of risk. We analyze what motivates economic agents to trade, how they can manage their risky endowments and how financial assets are used for investment decisions, to hedge risk. We also analyze how insurance premiums, as well as stocks, bonds, and option prices are determined. The emphasis will be on theoretical issues rather than the institutional details of insurance markets and securities trading, but we will also apply the ideas to some real-world examples and relate the material to current financial news and problems relevant to the practitioner.

B BUS 490 SPECIAL TOPICS (Econometrics)                                                                          3.8

Econometrics is the application of statistical techniques to evaluate economic relationships. In this class, I will introduce you to the fundamentals of econometrics theory, and statistical tools to analyze data and quantify causal effects. My ultimate goal is to prepare you for your business career, especially in the field of data science and business analytics.

To this effect, this course combines theory with hands-on experience of estimating econometric models with statistical software; it emphasizes empirical applications to data and real-world problems to inform business decisions. Many of the assignments, as well as the empirical project, require using statistical software Stata. Watson Analytics and Tableau will also be used for data visualization. You will acquire a set of useful skills that are in high demand in the era of “big data” and business analytics.

We will cover three major themes:

1. Estimation and inference of linear and non-linear regression models.
2. Model specification, heteroskedasticity, serial correlation, multicollinearity, omitted variable bias, and endogeneity.
3. Advanced topics: panel data, binary dependent variable, instrumental variable, difference-in-difference, and propensity score matching.

B BUS 470 B POL & STRTGC MGMT                                                                                       3.9

Capstone course. Focuses on identification, analysis and resolution of managerial problems; creation and implementation of management policies in business organizations; and revision of policies over time.

B BUS 490 SPECIAL TOPICS (Game Theory)                                                                          3.7

The course focuses on the study of strategic interaction among agents (e.g. managers, firms, consumers, countries, etc.) in situations characterized by both complete and incomplete information. While the course is mathematical in nature, it is built under the premise that game theory is applicable in every-day situations. Game theoretical principles are taught through the study of examples taken from business, economics, political science and other fields. Topics studied include oligopolies, auctions, bargaining procedures, voting procedures, and asymmetric information.

BISMCS 333 MEDIA & COMMN STDYS                                                                                  3.1

Emphasizes the skills of critical media analysis and creative media production. Addresses media representations and the importance of media in structuring contemporary society.