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.

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.

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.

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.

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.

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.

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.

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.

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.

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.