Certificate in Data Science (CDS)

Why Attend

Certificate in Data Science will expose participants to Data Science best practices, introduce them to the essentials of the Big Data ecosystem and opportunities for Artificial Intelligence. It doesn’t limit itself to analytics, but to all disciplines to which modern data relates to as well. By the end of this course, participants will become specialists in techniques and technologies that will allow them to get meaningful knowledge from their data, and deal professionally with experts in all advanced data management fields.

Overview

Course Outline

Schedule & Fees

Course Methodology

All analytical methods and solutions are elaborated with step-by-step case studies with practical, hands on experiences. An exhaustive documentation will cover analytical topics with an exclusive face-to-face comparison between SAS, SPSS, STATISTICA, Excel, R and Python.

Course Objectives

By the end of the course, participants will be able to:

Understand and design data for efficient analysis
Compare solutions related to Data Analysis vs. Machine Learning
Differentiate between predictive models and pattern finding ones
Decide between “proprietary” and “open source” technologies
Outline the modern data flow from sources to reports
Manage Data Science projects with project management best practices

Target Audience

This course is for specialists who aspire to become accustomed with data science components, and how they can be applied coordinately to solve data and business problems, as well as research issues. The course is specifically suited for managers and persons involved in marketing, CRM, research, manufacturing, quality control, app developers and IT analysts from almost any sector, such as banks, insurance companies, retail, governments, manufacturers, healthcare, telecom, transport and distributors.

Target Competencies

Business data analysis
Data analytic validity
Judging AI algorithms
Evaluating IoT platforms
Comparing big data results

Data Analysis and Visualization

Types of data and data visualization
Evaluating the representative quality of data
Using descriptive statistics to summarize data
Profiling two or more groups with statistical tests
Visualizing multiple analytics with powerful smart charts
Simple Linear Regression
Simple Logistic Regression
Managing and removing outliers

Machine Learning – Supervised

Multiple linear regressions
Multiple logistic regressions
Discriminant analysis: Functions and probabilistic models
Decision trees: CART – CHAID and Random Forests
Support vector machines
K-nearest neighbors
Naïve Bayes
Neural networks, deep learning and AI possibilities

Business Intelligence Forecasting – R vs. Python

Business Intelligence

Databases: collection and sources
ETL
Storage: Data warehouses, data marts and data lakes
Analytics: BI Tools, OLAP, Dashboards, etc.

Forecasting

Trends
Exponential smoothing: Additive and multiplicative methods
Time Series: Additive and multiplicative methods
ARIMA models

R vs. Python

Statistical Tests
Machine Learning algorithms

Machine Learning: Unsupervised

Principle Component Analysis
Clustering: Hierarchical and K Means
Simple correspondence analysis
Multi-dimensional scaling
Quadrant analysis

PMP for Data Scientists

PMP
Integration, Cost, Scope
Time, Cost, Quality, Communication
Risk, Procurement and Stakeholders

IoT and Big Data Ecosystem

IoT essentials - M2M and Embedded Systems
Basic IoT protocols
Big Data: “where” and “when”
Big Data distributed files with HDFS
Map Reduce vs. Spark Data Sharing
Big Data Ecosystem bird's eye view: Spark, Mongo DB, Cassandra, Flume, Cloud era, Oozie, Mahout

Manila

Manila (Philippines)2025-09-22$3775

Kuala Lumpur

Kuala Lumpur (Malaysia)2026-06-22$3775