Foundations of Data Science and Statistical Methods

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

In this course, you will gain a comprehensive understanding of data science and its key statistical methods. Data science plays a crucial role in making data-driven decisions in today's business, technology, and research environments. You will develop essential skills such as data exploration, collection, and statistical analysis, helping you draw meaningful insights from datasets. By applying these skills, you will be well-equipped to handle complex data challenges in real-world scenarios. This course combines theoretical concepts with practical applications to ensure learners are prepared for hands-on data analysis tasks. Through practical exercises, you'll be able to apply these methods to solve real-world data problems effectively. Ideal for individuals new to the field, this course will benefit aspiring data scientists, analysts, and anyone interested in understanding the power of data. No prior experience is required, making it accessible to those starting their data science journey. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. From CompTIA DataX Study Guide Copyright © 2024 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Used by arrangement with John Wiley & Sons, Inc.

Syllabus

What Is Data Science?

In this section, we define data science, differentiate it from machine learning (ML) and artificial intelligence (AI), examine key real-world applications, and discuss best practices throughout the data science life cycle.

Mathematics and Statistical Methods

In this section, we cover essential mathematics for data science, including calculus, probability distributions, inferential statistics, and linear algebra, highlighting their roles in data analysis, modeling, and algorithm development.

Data Collection and Storage

In this section, we identify common data sources, explain data acquisition and ingestion methods such as batch and streaming, and evaluate structured and unstructured storage solutions for effective data management and analytics.

Data Exploration and Analysis

In this section, we explore Exploratory Data Analysis (EDA) techniques for various variable types, interpret common data visualizations, and address frequent data quality issues critical for robust data science analysis.