As data continues to grow larger and more important, Excel and spreadsheets are becoming a slower and outdated solution to analyze large amounts of data. This is the main reason that most financial services companies have implemented Python when working with datasets.

In this comprehensive course, students will be taught Python with an emphasis on data extraction, analysis, and visualization. More specifically students will learn a foundational understanding of the Python programming language.

After students master the intricacies of the Python language, the class will move onto using Python for data science. This includes a deep understanding of Python libraries of NumPy, Pandas, Matplotlib. Additionally, students will learn how to pull data from different sources such as Excel, website, and APIs. At the end of the course, students will feel comfortable trading in Excel spreadsheets for Jupyter notebook. Students will leave this course having worked on multiple projects using Python to pull, clean, analyze, and visualize financial data from all different sources.

Open to Beginners

This course is designed to give beginners the practical skills they need to start using data science with Python for financial analysis. Examples and exercises will emphasize how these techniques can be applied to real-world situations and use cases. Previous math or coding experience is not required.

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  • Hands-on training in small groups

    Work on projects proven to boost retention

  • Time-tested curriculum and supporting materials

    Refined over many cohorts for an optimal learning experience

  • Learn from industry experts with teaching experience

    Experienced educators who are driven to help you succeed

  • Retake the course for free within one year

    Refresh the materials and gain additional practice

Learn the concepts and skills covered in this course or your tuition is on us. See details and terms & conditions.

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What You’ll Learn

  • Foundational programming concepts including loops, functions, and objects
  • Handle different types of data, such as integers, floats, and strings
  • Control the flow of your programs with conditional statements
  • Reuse and simplify code with object-oriented programming
  • Analyze tabular data with Numpy and Pandas
  • Create graphs and visualizations with Matplotlib
  • Make predictions with linear regression, using scikit-learn