Overview

Data analytics is transforming the way businesses operate. But organizations often struggle with identifying opportunities for data-driven decision making and defining a clear analytics project plan to tackle them. This beginner-level specialization aims to help business leaders, managers, analysts, and data scientists become better at successfully ideating, refining, planning, and executing data analytics projects as a team. This specialization will help learners develop these skills through four courses that demonstrate the design sprint process for different types of analytics projects. Each course will have 3 key components: fundamental concepts, use cases, problem identification, and solutioning to develop analytics project ideas.

Syllabus

Course 1: Exploratory Analytics Project Ideation
Course 2: Predictive Analytics Project Ideation
Course 3: Causal Inference Project Ideation
Course 4: Prescriptive Analytics Project Ideation

Courses

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11 hours 44 minutes
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Organizations can harness the power of causal inference through randomized field experiments, uncovering the true effects of interventions and enabling data-driven decision-making. In this course, we will delve into how companies are currently utilizing A/B testing. Additionally, we will explore the ethical considerations of conducting experiments and the econometric methods for analyzing causal relationships in observational data. Learning objectives: - Examine how companies are using field experiments for causal inference. - Analyze the ethical dimensions of field experiments. - Construct an issue tree for conducting randomized experiments about designing online referral bonus programs - Select a solution approach and define an experimental setup
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14 hours 50 minutes
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Exploratory analytics is where data meets discovery, revealing hidden patterns and turning raw information into breakthrough insights. In this course we will first provide an overview of exploratory analytics methods such as clustering, association rule mining, anomaly detection, and study their business use cases. We will then consider a case study to learn how to use a design sprint framework to ideate about exploratory analytics project plan. Learning objectives: - Analyze how exploratory analytics concepts can be used to solve business problems - Construct an issue tree for an exploratory analytics project - Select a solution approach and define an exploratory analytics project
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13 hours 5 minutes
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Predictive analytics turns data into a crystal ball, empowering your organization to anticipate trends, seize opportunities, and stay ahead of the curve with every decision. In this course, we will begin with an overview of predictive analytics models, such as decision trees, kNN, and neural networks, and explore their business applications. Following this, we will examine a case study about customer churn to learn how to use a design sprint framework for brainstorming a predictive analytics project plan. Learning objectives: - Examine how predictive analytics principles can be applied to address business challenges. - Examine advanced ML/AI models for predictive analytics - Analyze business context and construct an issue tree for a predictive analytics project - Select a solution approach and define a predictive modeling project