• Classroom
  • Online, Instructor-Led

Geared for experienced Python users with basic data science skills, Python for Data Science is a comprehensive hands-on course that deep dives the advanced skills and tools used to perform exploratory data analysis, create complex visualizations, and perform large-scale distributed processing on Big Data.

Throughout the course, guided by our expert instructor, you'll learn, gain the advanced skills required to leverage Python to effectively solve real-world problems and contribute to data-driven projects in a professional setting. Working in a workshop style, hands-on environment, you’ll hone your skills in numerical operations using NumPy and delve into advanced data manipulation techniques with Pandas. From applying complex mathematical functions in SciPy to mastering data visualization through Matplotlib and Seaborn, this program equips you for a broad spectrum of data science tasks. You'll also get practical experience in merging, joining, and concatenating data sets, while gaining an understanding of machine learning fundamentals via scikit-learn. These technical abilities are framed within a problem-solving context, empowering you to contribute effectively to data-driven initiatives in your professional role.

With these advanced Python and data science skills, you'll be equipped to lead complex data analysis projects that transform raw data into actionable insights for strategic decision-making. You'll also have the capability to design and implement machine learning models, allowing your organization to harness the power of predictive analytics for enhanced operational efficiency and competitive advantage. You’ll exit this course with advanced skills tailored specifically for applications in data science, able to handle complex data sets, understand machine learning algorithms, and translate data into actionable insights.

Learning Objectives

  • Gain proficiency in handling large numerical data sets, performing array operations, and using vectorized computation for increased efficiency.
  • Acquire the ability to clean, filter, and manipulate complex data sets using Pandas, allowing for more insightful data analysis.
  • Learn to apply complex mathematical functions and algorithms in Python using SciPy, thereby broadening your toolbox for scientific computing tasks.
  • Develop advanced data visualization skills for creating comprehensive, interpretable visual representations of complex data sets.
  • Understand and implement advanced techniques to merge, join, and concatenate data sets effectively, enabling better data integrity and usefulness.
  • Become proficient in basic image processing tasks like loading, transforming, and saving images using the Pillow library, thus widening the range of data types you can manipulate.
  • Understand the fundamentals of machine learning algorithms and how to implement them using scikit-learn for tasks such as classification, regression, and clustering.
  • Cultivate the ability to apply the acquired technical skills to solve real-world problems, enhancing your capacity to contribute effectively to data-driven projects in a professional setting.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):