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Navigating Financial Data with Python
Elevate your financial analytics skills with Python for Finance, exploring cutting-edge techniques for data retrieval, time series analysis, and predictive modeling.
Trusted by 220,000+ people worldwide.
An outline of this training course
Unlock the potential of Python for financial analysis in this engaging course. Begin by acquiring data from top sources like Yahoo Finance and NASDAQ API, progress to time series analysis, and create custom financial functions. Learn forecasting with the Facebook Prophet model through hands-on exercises in Google Colab. Elevate your skills in financial data prediction and analysis. Join us to harness Python's powerful capabilities!
What is needed to take this course
Participants should have a basic understanding of Python programming, including variables, loops, and functions, as well as some familiarity with fundamental financial concepts. Access to a computer with internet connectivity is essential for accessing course materials and using APIs. Participants should have Python installed, preferably the latest version, and be comfortable with an Integrated Development Environment (IDE) like Jupyter Notebook or Google Colab. Knowledge in using Python libraries such as pandas, NumPy, and matplotlib, commonly employed in financial data analysis, is also beneficial. Above all, a keen interest and willingness to learn and experiment with Python in the realm of finance is crucial for making the most out of this course.
Who is this course for
Tailored for a diverse audience, including financial analysts, data scientists, investment bankers, portfolio managers, and students in finance or economics. It is also well-suited for Python programmers looking to venture into finance, DIY investors and traders seeking to enhance their portfolio analysis skills, and anyone with a foundational understanding of Python and basic finance concepts. This course caters to those interested in leveraging Python's powerful data analysis capabilities for financial applications, making it ideal for professionals and enthusiasts at the intersection of finance and technology.
Details of what you will learn in this course
By the end of this course, you will:
- Master the retrieval and manipulation of financial data from sources like Yahoo Finance and NASDAQ API using Python.
- Conduct in-depth time series analysis and implement financial indicators such as moving averages.
- Utilize Python’s financial libraries for advanced data visualization and analysis.
- Develop custom functions and metrics for detailed financial analysis.
- Learn to create and fine-tune predictive models for financial forecasting, focusing on the Facebook Prophet model.
- Apply these skills in real-world scenarios through practical exercises in Google Colab, enhancing hands-on experience in financial data analysis.
What you get with the course
- Over two hours of intensive training videos
- Resource pack for practical application
Program Level
Intermediate to Advanced
Field(s) of Study
Data Science and Analysis, Econometrics, Business Intelligence
Instruction Delivery Method
QAS Self-study
***This course was published in January 2024
Enterprise DNA is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org
What our
Students Say
Curriculum
Course Overview
Resources
Foundations of Financial Analysis with Python
Data Retrieval and Initial Processing
Advanced Data Analysis Techniques
Comparative Analysis and Metrics
Forecasting Financial Trends
Conclusion and Next Steps
Your
Instructor
Gaelim Holland
Enterprise DNA Expert
- Innovative Data Analyst and Digital Channel Optimization Specialist with thorough knowledge of Omni channel analytics and incorporating online and offline data in funnel analysis.
- Skilled in maximizing online sales, revenue, and call-to-actions through conversion rate optimization, statistical science, and A/B testing. Deep expertise in statistical testing tools, data extraction, and data science.
- My 15 year career has allowed me to work in multiple data science roles in several industries at organizations from the startup level to Fortune 500 companies across 3 continents.
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