Data Science Essentials: Analysis, Statistics, and ML

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. Unlock the power of data visualization with Python and Plotly Dash. This course walks you through building fully interactive, production-ready dashboards that bring your data to life. You’ll gain hands-on experience crafting visual analytics, using real-world datasets, and customizing your dashboards to suit your needs. Starting with a Python refresher, you’ll set up your environment and revisit essential programming concepts. Then, you’ll dive into Plotly Dash, learning to create layouts, integrate HTML and core components, and design callbacks that respond dynamically to user input. Each section builds toward practical application, with step-by-step guidance through building charts, forms, and interactions. The course features two major capstone projects—one based on avocado prices and another tracking life expectancy—plus two detailed case studies including a live financial dashboard and an interactive map. Bonus material introduces Jupyter Dash, extending your toolkit for development. Designed for intermediate Python users, this course is perfect for data analysts, business intelligence professionals, and developers who want to build interactive web-based data apps. Prerequisites include basic Python knowledge and familiarity with data structures and functions.

Updated in May 2025. This course now 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 course, you will gain a solid foundation in Machine Learning (ML) and Python programming, which are essential skills for any aspiring data scientist. By the end of the course, you'll have a deep understanding of ML fundamentals, statistical techniques, and how to use Python for real-world data analysis and model building. You'll be able to apply these concepts to a range of industries and data-driven problems. The course starts with an introduction to the core concepts of ML. You'll explore key terminology, different types of ML algorithms, and real-world use cases. This section will set the stage for more advanced topics by building your understanding of how ML can be applied in various industries. You'll also learn how to approach and solve problems with ML, laying the groundwork for your learning journey ahead. Following the introduction, the course delves into essential statistical techniques, including probability, hypothesis testing, and understanding data distributions. These concepts are crucial for designing and interpreting ML models accurately. You'll also learn how to evaluate model performance using these techniques, helping you to build robust and effective ML systems. The course also provides a comprehensive guide to Python programming. You will master essential libraries like NumPy and Pandas, which are pivotal for data manipulation and analysis in machine learning tasks. Additionally, you'll work with Jupyter Notebooks to practice coding, explore data, and implement machine learning algorithms efficiently. This course is ideal for beginners or professionals transitioning into data science; no prior experience is required, though basic programming familiarity is helpful.

Updated in May 2025. This course now 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. This course provides a solid foundation in Python for data science, focusing on NumPy, Matplotlib, Pandas, and a touch of machine learning. Learners will gain practical experience with essential data science tools, enhancing their ability to manipulate data, visualize it, and perform basic machine learning tasks. By the end of the course, students will be prepared to tackle more advanced data science topics with a strong understanding of how Python is used in real-world applications. In the first section, you will get an introduction to NumPy, focusing on its powerful array operations and speed advantages over traditional Python lists. You'll explore matrices, dot products, and linear systems to understand the foundation of numerical computing. Practical exercises will reinforce these concepts, making sure you are comfortable working with NumPy in data science. Next, you'll move to Matplotlib, where you'll learn how to visualize data effectively. Through hands-on practice with line charts, scatterplots, histograms, and image plotting, you'll become proficient in presenting data in various graphical formats. This section will equip you with the tools to visually analyze data and communicate insights clearly. In the final section, you'll dive into Pandas, one of the most widely used libraries for data manipulation. You'll master techniques like loading data, selecting rows and columns, and applying functions to dataframes. You'll also explore plotting capabilities within Pandas. As a bonus, you'll be introduced to SciPy and basic machine learning concepts to understand how these tools integrate into data science workflows. This course is ideal for anyone starting their data science journey or looking to strengthen their Python skills for data analysis. A basic understanding of Python is required, and the course is designed for beginners. If you are interested in learning how to use Python for data manipulation, visualization, and introductory machine learning, this course will set you up for success.

Updated in May 2025. This course now 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. This course equips you with essential statistical and mathematical tools to become proficient in data science and analytics. You will learn key concepts in descriptive statistics, probability theory, regression analysis, hypothesis testing, and more. By the end of the course, you will have a deep understanding of how statistical methods can be applied to solve real-world data problems and enhance data-driven decision-making. The course begins with an introduction to the basics of descriptive statistics, such as measures of central tendency, dispersion, and the differences between sample and population data. You will then explore distributions, including the normal distribution and Z-scores, and how to apply them in various scenarios. The journey continues with probability theory, where you will tackle concepts like Bayes' theorem, expected value, and the central limit theorem, building a solid foundation for statistical analysis. Next, you will dive into hypothesis testing and learn how to perform tests like t-tests and proportion testing. You will also understand the significance of confidence intervals, the margin of error, and Type I and Type II errors. The regression section teaches you how to predict data values using linear regression, explore correlation coefficients, and analyze model accuracy with metrics such as MSE and RMSE. This course is ideal for aspiring data scientists, analysts, and anyone who wants to use statistics to interpret data. No prior knowledge of statistics is required, though familiarity with basic mathematics will be helpful. The course is structured to be engaging and practical, offering exercises and real-world applications that allow you to practice your skills.