Time Series for Finance and Business

Friday, 9:00 am - 5:00 pm
Location: Biltmore Court 
CFALA Member Center
520 S Grand Ave. Suite 655 
Los Angeles, CA 90071
Instructor: TBA
Time: 9:00 am - 1:00 pm: Morning Session
1:00 pm - 2:00 pm: Lunch
2:00 pm - 5:00 pm: Afternoon Session
Dress: Casual
Add to Calendar 05/04/2018 09:00 AM 05/04/2018 5:00 pM America/Los_Angeles Time Series for Finance and Business This hands-on data science course teaches the fundamentals of time series analysis and how to do this in Python. Time Series is a series of data points measured over a specific time period. Biltmore Court CFALA Member Center 520 S Grand Ave. Suite 655 Los Angeles, CA 90071 CFALA info@cfala.org false MM/DD/YYYY
An Education Committee Sponsored Event

Follow on course to Introduction to Data Science for Finance


This hands-on data science course teaches the fundamentals of time series analysis and how to do this in Python. Time Series is a series of data points measured over a specific time period.

Whether you are trying to predict asset prices or understand the effects of air pollution over time, time series can help you. At the end of the workshop, participants will be comfortable applying the Python programming language to visualize and execute time series analysis to see if there is predictive power in your data.

What This Course Offers
  • An overview of core classification methods and how to use them to solve real-world problems
  • Hands-on Python programming experience
  • Course notes, certificate of completion, and post-seminar email support for 3 months
  • An engaging and practical training approach with a qualified instructor with relevant technical, business, and educational experiences
  • A Computer Science 101 pre-course webinar

Who Is This For

This course is relevant for individuals working with or needing to understand times series. The most common participants are: investment professionals, traders, economists, biologists, chemists, physicists, entrepreneurs, consultants, and technology individuals. Cognitir’s Introduction to Data Science course or the equivalent is required.

Course and Contact Information
Course Prerequisites:
+1 908 505 5991 (US); +44 75 0686 49 85 (UK)

Course Curriculum

  • Overview of Time Series Analysis
    • What is it, wide variety of use cases, time series analysis vs. time series forecasting, common statistical problems in time series (leptokurtic, heteroskedasticity, serial correlation) and common tests to test for these issues (look at error residuals and Durbin-Watson) This hands-on data science course teaches the fundamentals of time series analysis and how to do this in Python. Time Series is a series of data points measured over a specific time period. 
  • Organizing and Visualizing Time Series Data
    • Exploring Your Time Series Data
      • Start, end, frequency, number of data points
    • Basic Time Series Plots
    • Discrete vs. Continuous Data
    • Sampling Frequency
    • Missing Values
    • How to do this in Python – with an example
    • Organizing and Visualizing Time Series Coding Challenge
  • Time Series Predictions
    • Trends
    • Random or Not
    • Stationary vs. Non-Stationary
      • Unit/root test
    • Removing variability trends through logarithmic transformation
    • Differencing
    • White Noise Model
    • Random Walk Model
    • How to do this in Python – with example
    • Time Series Prediction Coding Challenge
  • Correlation and Autocorrelation
    • Scatterplots
    • Prices vs. Returns
    • Financial Time Series
    • Plotting Pairs of Data
    • Correlation and Covariance
    • Autocorrelation and Calculation
    • Autocorrelation Function
    • How to do this in Python
    • Correlation and Autocorrelation Coding Challenge
  • Autoregression
    • What is it?
    • Persistence vs. Anti-Persistence
    • Autocorrelation and Autoregression
    • Random Walk vs. AR
    • AR model Estimate and Forecasting
    • Estimate AR Models
    • Forecasts from AR Models
    • How to do this in Python
    • AR Coding Challenge
  • Simple Moving Averages
    • What are they?
    • Autocorrelation and simple moving averages
    • MA model Estimate and Forecasting
    • Estimate MA Models
    • AR vs. MA models
  • Final Project
Course Content Developers

David Haber | david@cognitir.com
David heads Cognitir's products and technology. He has led programming workshops at the undergraduate and graduate levels, at blue chip companies, and world renowned management consulting firms. David has experience working with both startups and large corporations. Previously, he was a lead software and machine learning engineer at Soma Analytics, an investor­backed and award­winning health­tech startup in London. David also worked on optimizing large­scale payment processing systems at Deutsche Bank in Singapore. Outside of Cognitir, he currently advises HiDoc, an early stage digital health startup in Germany. David holds an MEng (First­Class Honours) in Computer Science from Imperial College London (UK) where he focused on statistical machine learning. He presented his work at 2/3 international conferences and won several awards for his work. During his studies, he also served as a teaching assistant at Imperial College where he helped undergraduate students master fundamental computer science concepts.

Neal Kumar | neal@cognitir.com
At Cognitir, Neal leads strategy and business development initiatives and advises on new product development. Outside of Cognitir, Neal consults C­level teams and senior business managers on a variety of strategic topics ranging from M&A to marketing. He also leads training seminars for Wall Street Prep and has consistently received top reviews from attendees and created two training courses that were used in seminars worldwide. Before his consulting and training careers, Neal taught secondary mathematics in St. Louis Public Schools (USA) as a Teach for America Corps Member. Prior to joining Teach For America, Neal worked in investment banking at JPMorgan and Houlihan Lokey. Neal received his MBA from London Business School (UK) and BBA in Finance from the University of Notre Dame (USA). He is also a CFA Charterholder and a Member of the CFA Institute Education Advisory Committee (EAC) Working Body where he helps shape CFA Program Content.
Early Registration Fees (Expires April 20th, 2018)
$399 (Members) | $499 (Non-Member)

Registration Fees
$499 (Members) | $599 (Non-Member)
Register for the Complete Data Science Series and Save $100
$1097 (Members) | $1397 (Non-Member)

Additional Courses
- March 9th: Data Science for Finance
- April 6th: Advanced Machine Learning for Finance and Business: Classification Techniques
Payment Information
We accept the following:

If you prefer to pay by check please register online and select "purchase order" as your payment option and enter your last name as the purchase order number.

Mail check to: 
CFA Society of Los Angeles, 520 S. Grand Ave, Suite 655, Los Angeles CA 90071.

*Credit card payments will only be accepted through the secure online registration, and not by phone or email.

Enrollee cancellations must be made in writing and received at least 5 business days before the first day of class. All cancellations will incur a $30.00 processing fee. If enrollment is canceled after the 5-day deadline, a 50% cancellation fee will be charged.
Rama Malladi, CFA
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