Computation and Visualization for Analytics Part 1

Northeastern University via Coursera

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Overview

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Offers students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. Covers basics of Python and R for data mining and visualization. Introduces students to static and interactive visualization charts and techniques that reveal information, patterns, interactions, and comparisons by focusing on details such as color encoding, shape selection, spatial layout, and annotation.

Syllabus

Module 1: Introduction - Data - Part 1

In this module, we will delve into the fundamental aspects of data, exploring its definition, significance, and the transformative journey from raw information to actionable insights. Through a series of engaging videos, we will unravel the mysteries of structured and unstructured data, unveiling their unique characteristics and applications. As we progress, the module unfolds the intricate steps of the data workflow, guiding through the pivotal stages of framing objectives, preparing data, analysis, interpretation, and effective communication of findings. Additionally, our exploration extends to the vast landscape of Big Data, unraveling its complexities through the lens of the Five Vs: Volume, Velocity, Variety, Veracity, and Value. By the end of this module, We will not only have a comprehensive understanding of the foundational concepts of data but also possess the essential skills to navigate the data-driven landscapes of today's digital era. Get ready to unlock the power of data and discover its profound impact on our world!

Module 2: Introduction - Data- Part 2

In this module, we will dive into the world of data analytics. We'll learn how to find the right data for data analysis, considering factors like relevance and timeliness. Then, we'll explore the crucial step of preprocessing, where we’ll learn to clean and organize raw data effectively. From handling missing values to spotting outliers, we'll pick up essential skills to ensure the analysis is accurate and reliable. By the end of this module, we'll be all set to confidently select, process, and analyze data like a pro. Let's get started!

Module 3: Introduction - Visualization

In this module, we'll explore how data visualization turns complex data into engaging stories. Building on our understanding of data's significance, we'll discover how visualization simplifies information and connects with diverse audiences. We’ll delve into creating various visualizations, from statistical plots to geographical graphs. By grasping different statistical graphs and their applications, you'll enhance your skills in sharing meaningful insights. Get ready to unlock the potential of visualization and enhance your ability to tell compelling data stories. Let's dive into this visually enlightening journey!

Module 4: Basics of Python

In this module, we'll delve into the fundamentals of Python coding. We'll explore key concepts such as variables, data types, and structures — crucial components in creating robust code. Throughout your Python learning journey, you'll acquire the skill of decision-making through if-else statements, navigate data using loops, and enhance your code with custom functions. Whether you're a coding novice or have some prior knowledge, this course ensures hands-on, practical experience. Let's explore, learn, and become experts in the key principles of Python programming. Get ready to bring your coding ideas to life!

Module 5: Handling the Missing Values and Data Integration

In this module, we're tackling two key skills for data analysis: dealing with missing data and mastering Pandas functions for data iteration and merging DataFrames. Real-world data is rarely perfect, and we'll learn to clean it up by identifying and customizing missing values. Then, we'll explore handy Pandas functions like items(), iterrows(), and itertuples() for efficient data navigation. Moving on, we'll dive into advanced DataFrame merging techniques, including merge(), join(), append(), and concat(). By the end, you'll have the skills to clean, iterate, and merge data seamlessly using Python and Pandas. Let's get started on this practical journey!

Module 6: Data Visualization with Matplotlib - Part 1

In this module, we will explore the dynamic synergy between Pandas and Matplotlib, empowering you to transform raw data into impactful visual narratives. Through engaging hands-on exercises, you'll delve into a diverse range of plots, from bar charts to scatter matrices. With a focus on Matplotlib, the go-to visualization engine in Python, you'll build a strong skill set to narrate captivating stories through data. This module promises an exciting journey deep into the world of data visualization, equipping you with the tools and know-how to unlock your creative potential. Get ready for a learning adventure that transforms the way you see and present data!

Module 7: Data Visualization with Matplotlib - Part 2

In this module, we're going beyond the basics of Matplotlib, diving into cool and advanced techniques for customizing plots to create meaningful and impactful visualizations. From mastering the intricacies of customizing axes and layouts to fine-tuning tick frequency, labels, and date labels, you'll gain valuable insights that will elevate your data visualizations. We'll also explore the art of plotting vertical lines to emphasize critical data segments. Through hands-on demonstrations and real-world examples, we will delve into the depths of advanced data visualization customizations with Matplotlib. Let's embark on this journey to empower you with the skills to customize your visualizations, turning them into compelling storytelling tools!