Course Content & Structure

How is the course delivered?

This course is delivered over 5 full-day sessions (9:30am - 5pm) between 22nd November 2019 and March 2020. Classes can be attended in class or online via live streaming. Participants will be given access to our online learning management system, Canvas, where additional resources and acticities will be made available.

In order to gain the Diploma, participants must successfully complete an assessment at the end of the course.

Provisional Assessment Format:  Assignment & Final Exam
 

Course Content & Schedule

Please see below the provisional indicative content for the Diploma in Data Analytics:

 

SESSION 1:

SUBJECT: INTRO TO DATA AND DESCRIPTIVE STATISTICS
DATE: FRIDAY 22nd NOVEMBER 2019

  • Intro to Data Analytics, Population and Sampling, Inferential Statistics, Data Sources, Design of Survey Research and Google Forms, Data Collection Primary and Secondary Sources, Survey Design. 
  • Presenting Data, Data Visualization, Tables & Charting, Pivot Tables in Excel, Organising Numerical Data, Frequency Distributions in Excel, Preparing Reports using Bivariate and Univariate Data, Pivot Charts, Bar/Pie/Donut/Pareto representations, Errors in Presenting Data
  • Descriptive Statistics, Mean, Median, Mode, Dispersion, Skewness and Kurtosis, Symmetric and Skewed Distributions, Box and Whisker Plot. Standard Deviation, Portfolio Evaluation using the concepts of Mean and Variance in Excel
  • Accessing the Yahoo Finance & other Databases, Correlation & Covariance, Estimating the Variance Covariance Matrix, Basic Probability, Introduction to Continuous Assessment Project, Volatility Modelling in Finance.

SESSION 2:

SUBJECT: INTRODUCTION TO MODEL ESTIMATION AND EVALUATION
DATE: MONDAY 9th DECEMBER 2019

  • Probability & Sample Space, Contingency Tables, Compound probability, Discrete Probability Distributions, Binomial, Poisson & Hypergeometric Distributions, Sampling Distributions in Excel
  • Confidence Interval Distribution, the Normal Gaussian Distribution and student distribution, Hypothesis Testing, Simple Linear Regression, Ordinary Leat Squares in Excel, Google sheets and Rstudio, Visual Basic Code for OLS, Applications of Linear Regression from Econometrics academy, Implement models in both Excel, Rstudio and Python, Application of Linear Regression to Option Data
  • Single verus Multiple Regression, Coefficients & marginal effects, Goodness of fit (R-squared), Hypothesis testing for coefficient significance, t-test for a single coefficient significance, F-test for multiple coefficients significance, Anova Table
  • The Taylor Rule estimated using OLS, The Vasicek model estimated using Maximum Likelihood in Excel, The GARCH model estimated using Excel and Rstudio. Review Continuous assessment

SESSION 3:

SUBJECT: MODELS OF QUALITATIVE CHOICE
DATE: SATURDAY 18th JANUARY 2020

  • Event studies, Fuller Event Study: Impact of Earnings, Announcements on Stock Prices, using a Two-Factor Model of Returns for an Event Study, Using the Offset Function in Excel to Locate a Regression in a Data Set
  • Binary dependent variable, probit, and logit models functional forms and properties. Model coefficients and interpretations, Marginal effects (and odds ratios) and interpretations, Goodness of fit statistics (percent correctly predicted and pseudo R-squared)
  • Logit Model in RStudio and Google sheets, VBA code for Logit. An analysis of credit risk, linking scores, default probabilities and observed default behavior 1
    Estimating logit coefficients in Excel, Computing statistics after model estimation, Interpreting regression statistics, Prediction and scenario analysis
  • An application of OLS to Term Structure of Interest Rates and Yield Curve analytics, A review of Continuous Assessment

SESSION 4:

SUBJECT: AVALUE RISK AND MONTE CARLO
DATE: FRIDAY 21st FEBRUARY 2020

  • Value at Risk, A Really Simple Example, Defining Quantiles in Excel, A Three-Asset Problem: The Importance of the Variance-Covariance Matrix
  • Random Number generation in Excel, Stock Price simulation using Monte Carlo, the Lognormal Distribution, Simulating Lognormal Price Paths, the application Box Muller when generating stock price paths
  • Black Scholes (1973) in Excel VBA and Python based on the Normal Gaussian Distribution, and Black Scholes versus Monte Carlo, Userforms in Excel. Implementation of VBA macros
  • Ordered Outcome dependent variable, Ordered Probit and Logit Models in Rstudio, A review of Continuous Assessment and Exam

SESSION 5:

SUBJECT: TIME SERIES ANALYSIS
DATE: FRIDAY 20th MARCH 2020

  • Time series examples, White noise, autoregressive (AR), moving average (MA), and ARMA models in Rstudio
  • Stationarity, detrending, differencing, and seasonality, Autocorrelation function (ACF) and partial autocorrelation function (PACF)
  • Dickey-Fuller tests, The Box-Jenkins methodology for ARMA model selection, GARCH revisited, Advanced GARCH volatility modeling in Excel VBA and Rstudio
  • A review of Exam

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