Coursera - Programming For Python Data Science Principles To Practice Specialization

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Coursera - Programming For Python Data Science: Principles To Practice Specialization
Released 1/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 67 Lessons ( 8h 13m ) | Size: 2.22 GB
Harness the Potential of Python for Data Science. Optimize, analyze, and visualize data effectively​

What you'll learn
Leverage a Seven Step framework to create algorithms and programs.
Use NumPy and Pandas to manipulate, filter, and analyze data with arrays and matrices.
Utilize best practices for cleaning, manipulating, and optimizing data using Python.
Create classification models and publication quality visualizations with your datasets.
Skills you'll gain
Algorithm Design
Data Visualization
Matplotlib
Predictive Modeling
Debugging
Program Decomposition
NumPy
Data Analysis
Monte Carlo Methods
Python Programming
Pandas (Python Package)
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.
Applied Learning Project
Throughout this specialization, you'll create programs to analyze real-world data and produce insights to the most important issues facing society (e.g., infant mortality, economic indices, and carbon emissions). You'll learn a process to translate abstract problems into functional programs that will create reproducible analyses. Each course emphasizes discrete parts of a data scientist's toolkit. All courses focus on practical applications, whether you're debugging basic Python code in industry-standard libraries or optimizing and evaluating predictive models. By completing the programming exercises in this specialization, you'll develop the analytical and technical skills necessary for completing a full data science pipeline- starting with a messy dataset and resulting in a publication-quality visualization.
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