How to Use Data

University of Pennsylvania via Coursera Specialization

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Overview

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“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.

Syllabus

Course 1: Intro to Data Analytics, SQL, and EDA Using Python
Course 2: Intro to Predictive Analytics Using Python
Course 3: Data Viz Using Tableau & Presenting With Storytelling

Courses

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7 hours 50 minutes
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Data visualization is a crucial aspect of data analysis and decision-making in today's data-driven world. In this course, you will delve into the fascinating realm of data visualization and harness the power of Tableau, a leading data visualization tool. You'll learn how to transform raw data into insightful visuals that convey complex information effectively. This course will empower you to create compelling visualizations that aid in decision-making, storytelling, and conveying insights to both technical and non-technical stakeholders. Data analysis is only as impactful as your ability to communicate the findings to others. This course will equip you with the skills and techniques necessary to craft compelling data stories and deliver persuasive presentations that resonate with diverse audiences.
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16 hours 46 minutes
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The ability to understand and work with data has become increasingly important in today's world, where data is ubiquitous and valuable. This course covers a range of topics, including what data is and its different types, what big data looks like, and how companies are using it. It also explores the fields of data analysis and data science and how the two come together. To help form the field of data analytics, we'll look at the entire data analytics process and how it works, from defining the analytics problem to interpreting and presenting the results. We'll also look at some case studies, which are presented to illustrate the application of data analytics in real-world scenarios.
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10 hours 52 minutes
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"Introduction to Predictive Analytics and Advanced Predictive Analytics Using Python" is specially designed to enhance your skills in building, refining, and implementing predictive models using Python. This course serves as a comprehensive introduction to predictive analytics, beginning with the fundamentals of linear and logistic regression. These models are the cornerstone of predictive analytics, enabling you to forecast future events by learning from historical data. We cover a bit of the theory behind these models, but in particular, their application in real-world scenarios and the process of evaluating their performance to ensure accuracy and reliability. As the course progresses, we delve deeper into the realm of machine learning with a focus on decision trees and random forests. These techniques represent a more advanced aspect of supervised learning, offering powerful tools for both classification and regression tasks. Through practical examples and hands-on exercises, you'll learn how to build these models, understand their intricacies, and apply them to complex datasets to identify patterns and make predictions. Additionally, we introduce the concepts of unsupervised learning and clustering, broadening your analytics toolkit, and providing you with the skills to tackle data without predefined labels or categories. By the end of this course, you'll not only have a thorough understanding of various predictive analytics techniques, but also be capable of applying these techniques to solve real-world problems, setting the stage for continued growth and exploration in the field of data analytics.