Data Analytics for Digital Transformation

Dartmouth College via edX Professional Certificate

Overview

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Dartmouth Engineering’s Data Analytics for Digital Transformation Certificate offers a comprehensive program to equip professionals with the tools and knowledge to drive innovation, efficiency, and strategic decision-making in today’s dynamic digital landscape. Professors Vikrant Vaze and Reed Harder lead the program, which includes four specialized courses—Fundamentals of Digital Transformation, Predictive Analytics, Simulation for Digital Transformation, and Prescriptive Analytics—each focusing on a critical component of modern analytics.

DT101x Fundamentals of Digital Transformation: This foundational course explores digital transformation's key concepts, challenges, and opportunities. You’ll gain an understanding of how emerging technologies, data-driven strategies, and innovation intersect to reshape industries and organizations. As a prerequisite, this course must be completed before progressing to the next course.
DA-DT.1x Predictive Analytics: Learn to transform raw data into actionable insights using tools like Python and cloud-based platforms. This course covers essential techniques, including linear and logistic regression, advanced modeling, and cross-validation methods, preparing you to diagnose and solve real-world business problems.
DA-DT.2x Simulation for Digital Transformation: Dive into discrete event simulation to model, analyze, and optimize complex systems. Using Python and SimPy, you’ll build simulations to handle uncertainty, evaluate workflows, and support decision-making across various industries, bridging predictive and prescriptive analytics.
DA-DT.3x Prescriptive Analytics: Advance your skills in optimization by building and solving models that recommend the best course of action for intricate business scenarios. With tools like Pyomo, you’ll tackle resource allocation, project selection, and logistics planning, integrating data-driven strategies into actionable recommendations.

Each course combines theoretical foundations with hands-on experience through Python-based exercises, case studies, and real-world applications. By the end of the program, you’ll have a robust analytics toolkit and the confidence to lead data-centric initiatives that align with your organization’s digital transformation goals. Whether new to analytics or seeking to deepen your expertise, this certificate empowers you to navigate complexity, manage uncertainty, and drive innovation.

Syllabus

Courses under this program:
Course 1: Fundamentals of Digital Transformation

The Fundamentals of Digital Transformation course provides a comprehensive introduction to the technologies, strategies, and business models driving digital innovation. Participants will explore key concepts such as cloud computing, artificial intelligence (AI), the Internet of Things (IoT), automation, and data-driven decision-making, learning how these tools reshape industries and enhance customer experiences.

Course 2: Predictive Analytics

With Dartmouth Engineering, you’ll learn to turn data into actionable insights with Predictive Analytics for Digital Transformation. This hands-on course equips you with Python skills, predictive modeling techniques, and analytics strategies to drive innovation and efficiency in digital transformation.

Course 3: Simulation for Digital Transformation

With Dartmouth Engineering, discover how to tackle complex challenges with Simulation for Digital Transformation. Learn to use Python and SimPy to model, analyze, and optimize systems, empowering you to make data-driven decisions and lead impactful digital transformation initiatives.

Course 4: Prescriptive Analytics

With Dartmouth Engineering, learn to transform data into actionable strategies in Prescriptive Analytics for Digital Transformation. Use Python to build and solve optimization models, tackle complex decisions, and leverage prescriptive tools to drive efficient, data-driven innovations.

Courses

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2 weeks, 2-5 hours a week, 2-5 hours a week
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This course is a prerequisite for the Data Analytics for Digital Transformation Professional Certificate and must be completed before enrolling in the core courses: Predictive Analytics, Simulation for Digital Transformation, and Prescriptive Analytics.

The Fundamentals of Digital Transformation course provides a comprehensive introduction to the technologies, strategies, and business models driving digital innovation. Participants will explore key concepts such as cloud computing, artificial intelligence (AI), the Internet of Things (IoT), automation, and data-driven decision-making, learning how these tools reshape industries and enhance customer experiences.

Through case studies, interactive activities, and role-play exercises, students will analyze real-world examples of digital transformation in companies like Microsoft, Starbucks, JPMorgan Chase, and Dell. The course also examines ethical considerations, privacy concerns, and organizational challenges, helping participants develop strategies for implementing digital solutions while addressing leadership resistance and compliance requirements.

By the end of this course, learners will gain a practical understanding of digital transformation frameworks, enabling them to drive innovation, optimize operations, and remain competitive in an increasingly digital economy.
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10 weeks, 3-5 hours a week, 3-5 hours a week
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Welcome to Thayer School of Engineering at Dartmouth’s Simulation for Digital Transformation. This in-depth course will equip you with the skills and tools needed to model, analyze, and optimize complex systems, helping organizations navigate uncertainty and make impactful, data-driven decisions. As part of the Digital Transformation for Data Analytics Certificate, this course focuses on discrete event simulation and other techniques essential for addressing the challenges of modern digital transformation.

Simulation plays a critical role in bridging predictive and prescriptive analytics by forecasting outcomes and identifying optimal actions to achieve desired results. Whether improving operational workflows, optimizing resource allocation, or designing better customer experiences, simulation provides the foundation for informed decision-making in dynamic environments.

This course introduces you to the fundamentals of simulation, starting with the key concepts of probability and uncertainty modeling. You’ll learn to generate random variables using techniques like the inversion and rejection methods, building robust models that reflect real-world variability. By mastering discrete event simulation, you can design event-driven models that incorporate simulation clocks, system states, events, transitions, and end conditions, allowing you to analyze and optimize systems across industries.

You’ll use tools like Python and SimPy through hands-on exercises to build and implement simulation models for real-world scenarios. From optimizing traffic flows to managing inventory and scheduling, the course demonstrates how these techniques apply to various business challenges. You’ll also explore how to validate and verify simulations, ensuring they provide trustworthy and actionable insights.

As the course progresses, you’ll tackle more complex problems, incorporating advanced methods for simulating random variables and addressing multi-objective goals. Real-world case studies, such as coffee shop customer flow and repair facility optimization, will challenge you to think critically and apply your skills to practical scenarios. By the end of the course, you’ll be equipped to use simulation as a predictive tool and a prescriptive analytics framework to recommend the best courses of action in uncertain environments.

The capstone practicum consolidates your learning, allowing you to develop and analyze a complete simulation project. You’ll apply all the techniques learned, from random variable generation to sensitivity analysis, culminating in a professional report with actionable recommendations for stakeholders.

Guided by Professors Vikrant Vaze and Reed Harder, this course blends theory, coding, and real-world applications to prepare you to lead data-centric initiatives. Whether you're a seasoned professional or new to analytics, Simulation for Digital Transformation will empower you to make smarter decisions, manage risk, and drive innovation in today’s fast-changing digital landscape.
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9 weeks, 3-5 hours a week, 3-5 hours a week
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Welcome to Thayer School of Engineering at Dartmouth’s Prescriptive Analytics for Digital Transformation. This comprehensive course is designed to equip you with the tools and methodologies needed to transform raw data into actionable strategies for decision-making in complex, real-world scenarios. By the end of this course, you will be able to design and implement optimization models that solve intricate business problems and align with digital transformation initiatives.

This course provides a deep dive into optimization principles and practical applications, beginning with foundational concepts such as decision variables, objective functions, and constraints. You’ll learn to differentiate between linear and non-linear optimization problems, gaining insight into when and how to transform non-linear models into linear ones for more efficient problem-solving. Through hands-on activities and Python-based exercises, you will implement linear optimization models to address challenges like inventory management, resource allocation, and advertising optimization.

As you progress, the course introduces more complex scenarios that require mixed-integer linear optimization. By incorporating integer variables into your models, you’ll unlock the ability to tackle discrete decision-making problems, such as determining warehouse locations, project selection, and resource distribution. These advanced techniques will help you formulate and solve optimization problems that mirror the complexities of modern business environments.

The course also covers practical tools like Pyomo and cloud-based platforms, ensuring you gain scalable, real-world skills. You’ll explore advanced methods such as branch-and-bound for binary integer optimization, enabling efficient solutions for large-scale problems. Applying these techniques to examples like portfolio optimization and logistics planning lets you see how prescriptive analytics drives operational efficiency and strategic decision-making across industries.

You'll consolidate your learning by applying prescriptive analytics to a capstone project. You’ll develop optimization models, analyze results, and prepare a professional report with actionable recommendations tailored to stakeholders. This hands-on experience will prepare you to lead data-driven innovations and effectively communicate the value of prescriptive analytics in decision-making.

Guided by Professors Vikrant Vaze and Reed Harder, this course blends rigorous academic instruction with practical, real-world applications. Whether a seasoned professional or new to analytics, you’ll leave this course with the skills and confidence to tackle complex decisions and contribute to your organization’s digital transformation.