• Develop a solid understanding of key quantitative analysis methods and techniques.

• Learn to apply statistical tools and mathematical models for data interpretation and prediction.

• Understand the foundations of regression analysis, time series analysis, and optimization methods.

• Gain insight into multivariate analysis and its applications in data-driven decision-making.

• Understand the principles of data sampling, hypothesis testing, and probability theory.

• Apply theoretical knowledge to complex problems in various fields such as business, economics, engineering, and science.

This course is designed for all:

• Data Analysts

• Statisticians

• Researchers and Scientists

• Economists and Financial Analysts

• Engineers involved in process optimization and modeling

• Professionals in industries requiring data-driven decision-making

• Managers and decision-makers working with data insights

Course Outline:

Day 1: Introduction to Quantitative Analysis and Statistical Foundations

• Overview of Quantitative Analysis:

  • Definition and significance of quantitative analysis in different fields

  • The role of data in decision-making and problem-solving

  • Types of data: Qualitative vs. Quantitative

• Basic Statistical Concepts:

• Descriptive statistics (mean, median, mode, variance, standard deviation)

• Probability theory and distributions (normal, binomial, Poisson)

• Sampling techniques and sample size determination

• Central Limit Theorem

Day 2: Probability Theory and Hypothesis Testing

• Probability Concepts and Theorems:

  • Conditional probability and Bayes’ theorem

  • Random variables and probability distributions

  • Expected value and variance of random variables

• Hypothesis Testing:

• Null and alternative hypotheses

• Type I and Type II errors

• Z-tests, t-tests, and chi-square tests

• P-value interpretation and significance testing

• Confidence intervals and their interpretation

Day 3: Regression Analysis and Model Building

• Simple Linear Regression:

  • Concept and application of linear regression

  • Estimation of regression parameters (least squares method)

  • Assumptions and diagnostics of linear regression models

  • Interpretation of regression coefficients

• Multiple Regression Analysis:

• Multiple linear regression and its applications

• Multicollinearity and its effects on regression models

• Model selection techniques (stepwise, AIC, BIC)

• Evaluating model performance and residual analysis

Day 4: Time Series Analysis and Forecasting

• Introduction to Time Series Data:

  • Time series components: Trend, seasonality, and noise

  • Stationarity and transformations of time series data

• Time Series Forecasting Models:

• Moving averages and exponential smoothing

• Autoregressive (AR), Moving Average (MA), and ARIMA models

• Forecast accuracy measures (MAPE, RMSE)

• Model selection and evaluation for time series forecasting

Day 5: Optimization and Multivariate Analysis

• Optimization Methods:

  • Linear programming and simplex method

  • Integer programming and constraint optimization

  • Objective functions and constraint formulation

• Multivariate Analysis:

• Introduction to multivariate analysis techniques

• Principal Component Analysis (PCA) and Factor Analysis

• Cluster analysis and its applications

• Discriminant analysis and classification methods

A variety of methodologies will be used during the course that including:

• (30%) Based on Case Studies

• (30%) Techniques 

• (30%) Role Play

• (10%) Concepts

• Pre-test and Post-test

• Variety of Learning Methods

• Lectures

• Case Studies and Self Questionnaires

• Group Work

• Discussion

• Presentation

International Center for Training & Development (ICTD) will award an internationally recognized certificate(s) for each delegate on completion of training.

To be advised as per course location. This rate includes participant’s manual, Hand-Outs, buffet lunch, coffee/tea on arrival, morning & afternoon of each day.

Daily Course Timings:

    08:00 - 08:20       Morning Coffee / Tea

    08:20 - 10:00       First Session

    10:00 - 10:20       Coffee / Tea / Snacks

    10:20 - 12:20       Second Session

    12:20 - 13:30       Lunch Break & Prayer Break

    13:30 - 15:00       Last Session