This program is composed of eight (8) modules that consist of lectures and workshops on the concepts and applications of business analytics. A participant that completes the first five modules and passes the certification exam will be certified as a Certified Business Analytics Associate while completing all seven modules and passing the professional certification exam will be certified as a Certified Business Analytics Professional.

Price ₱13,000

Description

Duration: 3 Days per module
Training Fee: Php 13, 000 per module

 

Who Should Attend:
This program will benefit business analysts, IT managers, data systems modelers, database administrators, data architects, CIOs, educators and others who need to take and analyze massive amounts of data from different sources to produce dashboards, generate summary reports and glean hidden information or patterns needed for managerial decision support. This program will also be advantageous for those who collect lots of data in order to analyze trends and systems.

Objectives:
The following points summarize the objectives of the training.
• By the end of this program, the participant should have a solid understanding of the fundamentals, concepts, business and technical architecture and best practices related to Business Analytics
• The participant should take away a broad understanding of the tools, underlying data management, techniques, industry applications and future directions of business analytics and data mining.
• The participant should be able to apply the right Analytics tool to different industry problems and interpret results correctly.
• The participant would be able to use modern business analytics software and implement an end-to-end business analytics project in their own respective field.

Course Outline:
This program is composed of seven modules that consist of lectures and workshops on the concepts and applications of business analytics. A participant that completes the first five modules and passes the certification exam will be certified as a Certified Business Analytics Associate while completing all seven modules and passing the professional certification exam will be certified as a Certified Business Analytics Professional.

 

Module A: Introduction to Business Analytics

Course Description: This course provides an introduction to business analytics which starts from the identification of business analytics problems to finding and implementing solutions to these problems. An introduction to the design of dimensional models and data warehouses is also included. Visualization techniques are also discussed. This course also gives an overview of the various business analytics models for classification, regression, time series, association, clustering, text mining and optimization problems.

Prerequisite: None

Course Content
1. Introduction to Business Analytics
2. Case Study on Business Analytics Project Identification
3. Data Warehousing
4. Case Study on Data Extraction and Report Generation using R
5. Introduction to Descriptive Analytics
6. Case Study on Data Analysis using R
7. Visualization
8. Case Study on Dashboard Design Visualization using R
9. Introduction to Predictive Analytics: Classification
10. Case Study on Classification Analysis using R
11. Introduction to Predictive Analytics: Regression and Time Series Analysis
12. Case Study on Regression Analysis
13. Predictive Analytics: Modern Tools and Unsupervised Learning
14. Case Study on Text Mining
15. Prescriptive Analytics
16. Case Study on Linear Programming

 

Module B: Data Warehousing

Course Description: This course teaches students to combine information from different sources and merge them into a single data warehouse for decision making. This course provides both introductory concepts and techniques for developing effective dimensional models to answer business questions. Learn how to design dimensional models for extensibility, employ a proven dimensional design process, apply the process to representative situations, and understand a variety of advanced dimensional modeling techniques. Furthermore, a data warehouse will be developed through workshops.

Prerequisite Concepts: None

Course Content:
1. Introduction to Databases and Enterprise Data Management
2. Introduction to Data Warehousing and the Data Warehousing Life Cycle
3. Project Planning and Management for Data Warehousing
4. Case Study on the Development of a Project Plan, Identification of Business Requirements and Project Planning
5. Introduction to Dimensional Modeling, Normalization and Denormalization
6. Dimension and Fact Table Designs Best Practice Designs
7. Case Study: Dimensional Modelling
8. ETL: Extraction, Transformation and Loading
9. ETL Planning Case Study
10. ETL: Transformation and Loading Best Practices
11. ETL Case Study

12. Processing Unstructured Data: Text, Audio, Images Video and Web Data

13. Case Study on Processing Variety of Data

 

Module C: Descriptive Analytics

Course Description: This course aims to provide tools for processing raw data into formats that can facilitate drawing of summarizing statics. The statistics drawn, can accurately and adequately describe historical information gathered which can be easily interpreted. Visualization techniques complemented and or generated from spreadsheet data processing shall be presented to facilitate ease of data interpretation. All tools and techniques presented in the course shall enable the participants to learn how to properly interpret past behaviors from recorded data, and understand how to they may influence outcomes in the future.

Prerequisite Concepts: None

Course Content:
1. Introduction to Descriptive Statistics
2. Types of Data/Scales of Measurements
3. Data Processing in Spreadsheets
4. Creating Distributions from Data
5. Data Processing & Formatting Case Study
6. Measures of Location (Continuous/Discrete Data)
7. Measures of Variability (Continuous/Discrete Data)
8. Analyzing Distributions
9. Measures of Correlations
10. Introduction to Data Visualization
11. Tables
12. Charts
13. Advanced Data Visualization Techniques
14. Descriptive Analytics Case Study & Presentation

 

Module D: Predictive Analytics

Course Description: This course surveys the different algorithms for data mining specifically: classification, regression, feature selection, association and clustering algorithms for business analytics. Learn to utilize a data warehouse and the various data mining algorithms to aid in business decisions. Learn concepts such as problem types, data structure requirements, over and under fitting, and how to evaluate a business analytics model. Advantages and disadvantages of methods are also discussed.

Prerequisite Concepts: College Level Statistics

Course Content:
1. Introduction to Data Mining and CRISP-DM
2. Introduction to Data Preprocessing
3. Case Study on Data Preprocessing
4. Supervised Learning: Classification Analysis

a. Frequency Models: ZeroR, OneR, Decision Trees, Naïve Bayes, Rule Based Classifiers
b. Neural Networks and Support Vector Machines
c. Ensembles: Bagging, Boosting and Random Forests
d. Model Evaluation Techniques

5. Classification Case Study on Churn Analysis
6. Supervised Learning: Regression Analysis

a. Simple and Multiple Regression
b. Model Verification and Validation
c. Variable Selection and Model Building

7. Regression Case Study on Predictive Advertising Revenue
8. Unsupervised Learning

a. Market Basket Analysis
b. Sequential Pattern Analysis
c. Clustering
d. Text Mining
e. Social Media Sentiment Analysis

9. Social Media Sentiment Analysis Case

 

Module E: Prescriptive Analytics

Course Description: This course provides an introduction to designing a business analytics system with optimization algorithms. Concepts include data gathering and analysis, model formulation and designing what-if decision support systems that incorporates optimization and Monte Carlo simulation algorithms.

Prerequisite Concepts: College Level Algebra

Course Content:
1. Review of Data Warehousing
2. Introduction to Linear Programming
3. Problem Formulation and Constraint Modeling

a. Decision Variables
b. Objective Functions
c. Constraints

4. Software Use and Case Study
5. Sensitivity Analysis
6. Software Use and Case Study
7. Introduction to Integer Programming and Problem Formulation
8. Logistics Transportation Problems and Formulations
9. PERT/CPM Network Models
10. Software Use and Case Study
11. Introduction to Simulation

a. Review of Probability Distributions
b. Types of Simulation
c. Design of a Data Warehouse for Simulation

12. Verification and Validation of Simulation Models
13. Spreadsheet Simulation
14. Industry Case Studies
15. Putting it all together

 

Module F: Forecasting and Time Series Analysis

Course Description: This course provides a basic overview of time series analysis and forecasting methodologies. An overview of smoothing and decomposition models will be discussed. Furthermore, forecasting algorithms will be analyzed using performance metrics.

Prerequisite Concepts: College Level Descriptive and Inferential Statistics,

Course Content:
1. Types of Time Series Analysis
2. Data Transformations and Time Series Components
3. Smoothing Methodologies

a. Simple, Moving Averages
b. Exponential Moving Average

4. Smoothing Workshops
5. Decomposition

a. Trends Component
b. Seasonal Component
c. Regression (linear + non-linear)

6. Decomposition Workshop
7. Autocovariance and Autocorrelation
8. White Noise and Random Walks
9. Basic Statistical Tests
10. AR(p), MA(q), ARMA(p,q)
11. ARIMA Models
12. Model selection & multimodel inference
13. Forecasting Performance Metrics
14. Analyzing Forecasts

 

Module G: R for Business Analytics

Course Description: R is the top choice tools for Business Analytics Analysts and Data Scientists. In a survey by KDNuggets in 2015, R is used by 46.9% of the surveyed business analytics professionals. Both are open source languages, have a wide community support system and lots of prewritten scripts for any Business Analytics task.

This training gives an overview of how to use R and R Studio to extract, manipulate and present massive data for management decision making. At the end of the training, the participant would be confident enough to produce any type of Business Analytics Analysis with R. Training is composed of lectures, hands-on exercises and real world data case studies.

Prerequisite: None

Course Content:

  1. Introduction to R: The R Environment, Software, Introduction to R Studio, Sessions, Commands
  2. Data Types and Basic Operations: Vectors, Assignments, Arithmetic, Logical Types, Missing Data
  3. Case Study: Data Manipulation
  4. Reading and Writing Data
  5. Case Study: Reading and Writing Data
  6. Control Structures
  7. Case Study: Control Structures
  8. Introduction to Graphics
  9. Deploying R Applications: Dashboards using Shiny
  10. Case Study: Dashboard Generation

 

 Module H: Python for Data Science and Analytics

 Course Description: This 3-day course will be your guide to learning how to use the power of the Python programming to analyze data, create beautiful visualizations, and utilize powerful predictive analytics algorithms.

This training gives an overview of how to use Python and Jupyter notebooks to extract, manipulate and present massive data for management decision making. At the end of the training, the participant would be confident enough to produce any type of Business Analytics Analysis with Python. Training is composed of lectures, hands-on exercises and real world data case studies.

Prerequisite Concepts: None.

Course Content:

  1. Introduction to Python: The Python Environment, Software, Jupyte, Sessions, Commands
  2. Python Programming
  3. Case Study: Python Programming
  4. Data Processing With NumPy
  5. Case Study: NumPy
  6. Data Processing With Pandas
  7. Case Study: Pandas
  8. Introduction to Graphics with Matplotlib and Seaborn
  9. Case Study: Visualization
  10. Machine Learning: Scikit-Learn
  11. Case Study: Predictive Analytics
  12. Other Python Applications