Overview

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This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.

Syllabus

Course 1: Intro to Analytic Thinking, Data Science, and Data Mining
- Offered by University of California, Irvine. Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we ... Enroll for free.

Course 2: Predictive Modeling, Model Fitting, and Regression Analysis
- Offered by University of California, Irvine. Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will ... Enroll for free.

Course 3: Cluster Analysis, Association Mining, and Model Evaluation
- Offered by University of California, Irvine. Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will ... Enroll for free.

Course 4: Natural Language Processing and Capstone Assignment
- Offered by University of California, Irvine. Welcome to Natural Language Processing and Capstone Assignment. In this course we will begin ... Enroll for free.

Courses

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4 hours 56 minutes
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Welcome to Natural Language Processing and Capstone Assignment. In this course we will begin with an Recognize how technical and business techniques can be used to deliver business insight, competitive intelligence, and consumer sentiment. The course concludes with a capstone assignment in which you will apply a wide range of what has been covered in this specialization.
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4 hours 26 minutes
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Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
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3 hours 57 minutes
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Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
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7 hours 15 minutes
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Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts. A brief overview of Descriptive, Predictive, and Prescriptive Analytics will be provided, and we will conclude the course with an exploratory activity to learn more about the tools and resources you might find in a data science toolkit.