Master in Business Analytics Academics

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba885

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise-scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. Whenever possible, we will draw examples related to global challenges such as climate crisis.

COURSE CODE: ba888

The capstone project course will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester efforts, however, the vast majority of the work will be done during the spring semester. The final product will be presented to a faculty panel at the end of the spring semester, followed by a poster session which will be open to the public.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba843

COURSE CODE: ba865

This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).

COURSE CODE: ba885

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise-scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. Whenever possible, we will draw examples related to global challenges such as climate crisis.

COURSE CODE: ba888

The capstone project course will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester efforts, however, the vast majority of the work will be done during the spring semester. The final product will be presented to a faculty panel at the end of the spring semester, followed by a poster session which will be open to the public.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba860

This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.

COURSE CODE: ba881

TBA

COURSE CODE: mk852

This course will focus on developing marketing strategies driven by marketing analytics. Topics covered include market segmentation, targeting, and positioning, new product test marketing, market response models, customer profitability, social media, and marketing resource allocation. The course will draw on and extend students’ understanding of issues related to quantitative analysis and principles of marketing. The course will use a combination of cases, lectures, simulations, and a hands-on project to develop these skills.

COURSE CODE: ba885

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise-scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. Whenever possible, we will draw examples related to global challenges such as climate crisis.

COURSE CODE: ba888

The capstone project course will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester efforts, however, the vast majority of the work will be done during the spring semester. The final product will be presented to a faculty panel at the end of the spring semester, followed by a poster session which will be open to the public.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba843

COURSE CODE: ba865

This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).

COURSE CODE: ba882

This course will equip students with the essential skills for transitioning data analysis and machine learning tasks to the cloud, supporting production workloads. It covers the creation and deployment of data and ML pipelines, including those for generative AI applications, with a focus on data integration strategies, cloud data warehousing, BI, and ML-Ops. Leveraging prior coursework in data management and machine learning, students will learn to implement ETL/ELT processes, monitor data quality, and deploy models as APIs using cloud services.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: ba860

This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.

COURSE CODE: ba881

TBA

COURSE CODE: mk864

This course focuses on the practical needs of the marketing manager making pricing decisions. Students learn the techniques of strategic analysis necessary to price more profitably by evaluating the price sensitivity of buyers, determining relevant costs, anticipating and influencing competitors' pricing and formulating an appropriate pricing strategy.

COURSE CODE: ba780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: ba810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: ba815

COURSE CODE: es710

This course introduces the challenges of leading and participating in teams and project groups. It emphasizes the roles of team members and leaders, how to motivate within a team environment, and how to create an environment in which teams and their members increase their capabilities. This course also provides support for students as they work on program projects and helps students to gain both knowledge of team dynamics and the skills to shape them.

COURSE CODE: es729

COURSE CODE: ba775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: ba840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: ba820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: ba830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: hm848

This course examines an array of compelling opportunities for innovation, incremental and disruptive, across products and services, created within existing organizations or by starting new businesses. It bridges design and implementation, examining the unique and complex array of elements that make successful innovation in the health sector so difficult, and developing the skills and knowledge needed to effectively address those challenges. The course provides a conceptual framework, and then emphasizes hands-on engagement, concrete exercises, written cases, and in-class speakers who are engaged in real-world innovation initiatives. Students will have the opportunity to focus on areas of particular interest and relevance to current or future work. They will leave better equipped to drive or support the viable, value-creating innovation so desperately needed in the health sector.

COURSE CODE: ba878

This course is designed to provide students with a deeper understanding of the key concepts, methods, and tools in data science, machine learning, and data infrastructure applied to the world of health care. The course will cover both theoretical foundations and practical applications of these topics, with a focus on the integration of data science techniques with data infrastructure. The course will include hands-on examples from real world data sets the will enhance skills and experiences in health care. In addition to reviewing key steps in the data science process (i.e. data preparation, exploratory data analysis, feature engineering, model selection, model evaluation, and model deployment) and machine learning techniques, we’ll explore how to use, apply, and deploy them in various healthcare settings. Students will learn about data architectures, distributed data processing systems, data pipelines, data transformation, and data visualization tools, and how different healthcare players are solving data challenges at scale. By the end of the course, students will have developed a deeper understanding of data science, machine learning, and data infrastructure, and will be able to apply these concepts to solve complex problems in a variety of healthcare domains across a multitude of data types.