Programming for Python Data Science: Principles to Practice Specialization. This course teaches participants how to use Python to effectively analyze, process, and visualize data. Designed for beginners and experts, this course covers essential skills such as data management, algorithm development, and working with key Python libraries such as NumPy, Pandas, and Matplotlib. Topics such as data structures, coding in VS Code, and building data science applications are covered across five courses. The final two courses focus on data modeling, prediction, and visualization, providing an introduction to machine learning and inferential statistics. By the end, participants will be able to clean, analyze, and visualize complex data and provide valuable insights. Practical projects in this course include analyzing real-world problems such as economic indicators and carbon emissions, which help strengthen problem-solving skills and build professional portfolios. By receiving the certification, your skills in transforming raw data into actionable results are confirmed.
What you will learn
- Using 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 publication-quality classification models and visualizations with your own datasets.
This course is suitable for people who:
- People looking to add Python to their data science toolbox.
- Complete beginners in data science who are looking to learn the principles and application of Python in this field.
- Those who want to develop skills in managing, analyzing, and conveying insights from data with Python.
- People who are interested in working with practical projects and frameworks related to the full data science pipeline.
- Students and professionals who want to gain a deep understanding of computer science concepts such as algorithm development and data structures.
- Those who want to work in depth with core Python libraries for data science such as NumPy, Pandas, and Matplotlib.
- People who are interested in learning data modeling, forecasting, and visualization to prepare for more advanced topics such as machine learning and inferential statistics.
- Those looking for the ability to clean and analyze data, discover compelling insights, and create applications and visualizations for their data science portfolio.
- People who want to demonstrate their ability to generate impactful insights from raw data in a data-driven world.
- Those interested in creating programs to analyze real-world data and generate insights for important societal issues.
- People who want to learn the process of translating abstract problems into applications that produce reproducible analyses.
- Those looking for hands-on practice in debugging Python code in industry-standard libraries or optimizing and evaluating predictive models.
- Individuals who want to develop the analytical and technical skills necessary to complete a complete data science pipeline – from a raw dataset to a publication-quality visualization.
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