Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will gain a solid foundation in the field of data science, exploring various roles and tasks that data scientists perform. You will learn the distinction between data analysis and data science, gaining insight into the essential skills and activities that make data science so powerful. This course will help you develop the knowledge and skills to understand data and its real-world applications. You’ll begin by diving into exploratory data science, including understanding data distributions, visualizations, and the steps to clean and structure data. As you advance, you'll explore unstructured data, working with complex formats such as text and video, and discover the methods to extract valuable information from them. Additionally, you’ll engage with key concepts such as associative rules and decision trees, honing the skills to model data effectively. This course will also introduce you to advanced topics like linear and logistic regression, neural networks, and the Lambda architecture, empowering you with the tools to analyze both structured and unstructured data. Whether you are looking to build a data pipeline or evaluate machine learning models, you’ll explore how these techniques apply to real-world scenarios.

Syllabus

What is Data Science

In this module, we will introduce the foundational concepts of data science, exploring the various activities data scientists engage in, the roles within data science teams, and the necessary skills required for success in the field. You will also learn about the distinction between data science and data analysis, which will help you understand the scope of this field.

Exploratory Data Science

In this module, we will cover the process of exploring data, from calculating basic statistics to visualizing data for better understanding. We will also dive into data cleaning and the selection of relevant data factors, which are critical for any successful data science project. You will gain essential knowledge on how to prepare and explore data before diving into deeper analysis.

Unstructured Data

In this module, we will explore unstructured data, discussing the challenges it presents and the methods used to store and query it effectively. You will also learn how to apply term-document matrices to analyze textual data, enabling the extraction of meaningful insights from unstructured data sources like text and video.

Associative Rules

In this module, we will delve into associative rules, a key technique for identifying patterns in data. You will learn how to generate association rules and assess their quality, ensuring that the rules provide actionable insights for decision-making.

Decision Trees

In this module, we will focus on decision trees, a powerful tool for classifying data. You will learn how to build basic decision trees and explore advanced techniques like boosting and random forests to improve classification accuracy and performance.

Linear Regression

In this module, we will dive into linear regression, one of the most widely used techniques for making predictions. You will learn how to create linear models, evaluate their quality, and apply them to solve real-world data problems.

Logistic Regression

In this module, we will explore logistic regression, a crucial technique for predicting binary outcomes. You will learn how to construct logistic models, assess their accuracy, and interpret the results to predict success/failure events.

Neural Networks

In this module, we will explore neural networks and their application in tasks like natural language processing. You will also learn about word embeddings and the Skip-Gram algorithm, which allow machines to understand and generate text based on contextual relationships between words.

The Lambda Architecture

In this module, we will introduce the Lambda Architecture, a scalable and fault-tolerant framework for processing both batch and real-time data. You will learn how Kafka, batch processing, speed layers, and serving layers work together to form an efficient data pipeline.

Data Science Roles

In this module, we will provide an overview of various roles within a data science team. You will learn the responsibilities and skill sets for each role, helping you understand where you might fit within a data science organization and which skills to develop for success in the field.