This introductory course, designed specifically for financial professionals, will discuss key concepts needed to understand Python.
Who should take this course?
Ideal for analysts, portfolio managers, risk managers and quants wanting to sharpen their data science skills.
14 hours of live, instructor-led, hands-on learning with 4 labs and 5 case studies.
Tailored to investment professionals
Course content curated and created by financial practitioners for financial practitioners.
Small group format
Limited to 40 students per session, the smaller cohort allows for personal interactions and social learning.
Live virtual format
Live 4-module course delivered online. Choose the one convenient for you in your timezone (US, Europe, and Asia-Pacific regions). Hands-on labs and case studies delivered in the world through the QuSandbox.
Delivered by Sri Krishnamurthy, CFA the founder of QuantUniversity and sought out instructor in data science and machine learning applied to finance.
Certificate of completion
Receive a certificate of completion upon finishing all 4 courses.
Meet the instructor
Founder of QuantUniversity, a data and quantitative analysis company. He has more than 20 years of experience in analytics, quantitative analysis, statistical modeling, and designing large-scale applications. Previously, Mr. Krishnamurthy has worked for Citigroup, Endeca, and MathWorks and has consulted with more than 25 customers in the financial services and energy industries. He has trained more than 1,000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School, many of whom work in quant and data science roles at financial services firms. Mr. Krishnamurthy earned an MS in computer systems engineering and an MS in computer science from Northeastern University and an MBA with a focus on investments from Babson College.
What You'll Learn
The Data Science Revolution: Why you need to learn Data Science now.
Implementing an analytics library in Python for risk and performance calculations.
Exploring and visualizing techniques using plotly, seaborn and matplotlib
Predicting stock returns using machine learning techniques.
Integrating, fundamental, quantitative and data science techniques within your enterprise.
Portfolio management within Python.
|Americas||25 May 2022, 1 Jun 2022, 8 Jun 2022, 15 Jun 2022||9:30 AM EST (GMT -4)|
|EMEA||21 Jun 2022, 23 Jun 2022, 28 Jun 2022, 30 Jun 2022||1:30 PM CET (GMT +2)|
|Asia Pacific||27 Jul 2022, 3 Aug 2022, 10 Aug 2022, 17 Aug 2022||12:00 PM HKT (GMT +8)|
Member Enrollment FeeUSD 1,999
Standard Enrollment FeeUSD 2,099