Data Science

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 structure data for effective analysis
Evaluate solutions for Data Analysis versus Machine Learning
Distinguish between predictive models and pattern-detection models
Make informed choices between proprietary and open-source technologies
Map the modern data workflow from raw sources to finalized reports
Oversee Data Science projects using 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

Understanding data types and visualization techniques
Assessing the representativeness of data
Summarizing data using descriptive statistics
Profiling multiple groups with statistical tests
Creating advanced visualizations with smart charts
Simple Linear Regression and Logistic Regression
Identifying and addressing outliers

Machine Learning – Supervised

Multiple Linear and Logistic Regression
Discriminant Analysis: Functions and probabilistic models
Decision Trees: CART, CHAID, and Random Forests
Support Vector Machines and K-Nearest Neighbors
Naïve Bayes
Neural Networks, Deep Learning, and AI applications

Business Intelligence Forecasting – R vs. Python

Fundamentals of Business Intelligence
Data collection and database sources
ETL processes (Extract, Transform, Load)
Data storage: Warehouses, marts, and lakes
Analytics tools: BI platforms, OLAP, dashboards, etc.
Forecasting methods and trend analysis
Exponential smoothing (additive and multiplicative)
Time Series Analysis and ARIMA models
Comparison of R and Python in statistical tests and ML algorithms

Machine Learning: Unsupervised

Principal Component Analysis (PCA)
Clustering techniques: Hierarchical and K-Means
Simple Correspondence Analysis
Multidimensional Scaling
Quadrant Analysis

Project Management for Data Scientists (PMP)

Introduction to PMP for Data Science projects
Managing integration, scope, and cost
Handling time, quality, and communication
Risk management, procurement, and stakeholder engagement

IoT and Big Data Ecosystem

Essentials of IoT, M2M, and embedded systems
Basic IoT communication protocols
Big Data fundamentals: “Where” and “When”
Distributed file systems with HDFS
Comparing MapReduce and Spark for data sharing
Overview of the Big Data ecosystem: Spark, MongoDB, Cassandra, Flume, Cloudera, Oozie, and Mahout

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Abu Dhabi