Programming for Python Data Science: Principles to Practice
Duke University via Coursera Specialization
Overview
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline.
Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems.
In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
Syllabus
Course 1: Python Programming Fundamentals
- Offered by Duke University. This introductory course is designed for beginners and individuals with limited programming experience who want ... Enroll for free.
Course 2: Data Science with NumPy, Sets, and Dictionaries
- Offered by Duke University. Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive ... Enroll for free.
Course 3: Pandas for Data Science
- Offered by Duke University. How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas ... Enroll for free.
Course 4: Designing Larger Python Programs for Data Science
- Offered by Duke University. Modern programs are complicated structures, with hundreds to thousands of lines of code, but how do you ... Enroll for free.
Course 5: Data Visualization and Modeling in Python
- Offered by Duke University. Put the keystone in your Python Data Science skills by becoming proficient with Data Visualization and Modeling. ... Enroll for free.
- Offered by Duke University. This introductory course is designed for beginners and individuals with limited programming experience who want ... Enroll for free.
Course 2: Data Science with NumPy, Sets, and Dictionaries
- Offered by Duke University. Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive ... Enroll for free.
Course 3: Pandas for Data Science
- Offered by Duke University. How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas ... Enroll for free.
Course 4: Designing Larger Python Programs for Data Science
- Offered by Duke University. Modern programs are complicated structures, with hundreds to thousands of lines of code, but how do you ... Enroll for free.
Course 5: Data Visualization and Modeling in Python
- Offered by Duke University. Put the keystone in your Python Data Science skills by becoming proficient with Data Visualization and Modeling. ... Enroll for free.
Courses
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This introductory course is designed for beginners and individuals with limited programming experience who want to embark on their software development or data science journey using Python. Throughout the course, learners will gain a solid understanding of algorithmic thinking, Python syntax, code testing, debugging techniques, and modular code development--essential skills for a successful career in software engineering, development, or data science. By the end of this course, you will learn to: - Gain a stepwise approach to problem-solving using algorithms and programming logic. - Apply common functions, conditional statements, and loops to build Python scripts and programs. - Work with the VS Code programming environment to enhance coding proficiency. - Use testing and debugging strategies to ensure code reliability. - Perform logical and mathematical operations on datasets. In the final week of the course you will apply your new algorithm design and programming skills to a data analysis problem: analyzing heart rate data.
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Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators. Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets. By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data. Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently. Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!
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How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas — a common library in Python used for data science — can ease their workflow. We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course. In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.
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Modern programs are complicated structures, with hundreds to thousands of lines of code, but how do you efficiently move from smaller programs to more robust, complicated programs? How do data scientists simulate the randomness of real world problems in their programs? What techniques and best practices can you leverage to design pieces of software that can efficiently handle large amounts of data? In this course from Duke University, Python users will learn about how to create larger, multi-functional programs that can handle more complex tasks. We don't recommend that this be the first Python course you take, as we'll be covering a decent amount of specific programming syntax. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and the Pandas library, you should be able to complete the material in this course. In the first module, we’ll discuss top-down design for larger programs, including the programming syntax and techniques that are useful to stitch together larger programs. Then in the following modules, we’ll transition into discussing Monte Carlo simulations and introduce you to the Poker project, the larger program you’ll create by the end of the course. By the end of this course, you should be able to decompose a programming problem into manageable pieces, explain the basics of Monte Carlo Methods, and efficiently integrate smaller pieces of code into a larger complete program. This will prepare you to take the next step in your data scientist journey, creating complex programs that can more creatively simulate real-world problems.
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Put the keystone in your Python Data Science skills by becoming proficient with Data Visualization and Modeling. This course is suited for intermediate programmers, who have some experience with NumPy and Pandas, that want to expand their skills for any career in data science. Whether you come to data science through social sciences and Statistics, or from a programming background, this course will integrate the two perspectives and offer unique insights from each. You’ll begin by becoming adept with matplotlib, an essential plotting library in Python that will enable you to discover and communicate insights about data effectively. You’ll progress to classification algorithms by creating a K-Nearest Neighbors (KNN) classifier, a foundational algorithm used in data science and machine learning. Finally, you will write Python programs that leverage your newfound data science skills based on inferential statistics, and be able to describe relationships between variables in your data. By the end of the course, you’ll be able to quickly visualize a dataset, explore it for insights, determine relationships between data, and communicate it all with effective plots. In the last module of this course, you’ll produce a publication-quality figure based on data that you’ve prepared and cleaned yourself; the first artifact in your data science portfolio. Throughout this course you’ll get plenty of hands-on experience through interactive programming assignments, live coding demos from data scientists, and analyzing the data behind important real-world problems (like carbon emissions, real estate prices, and infant mortality). Guided activities throughout each module will reinforce your proficiency with data science techniques and analytical approach as a data scientist. Solidify your understanding of these critical data science concepts and begin your data science portfolio by mastering visualization and modeling. Start this integrative and transformative learning journey today!
Taught by
Andrew D. Hilton, Genevieve M. Lipp, Kyle Bradbury and Nick Eubank