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.

Data-driven decision-making is an essential component of emerging engineering systems that generate and consume very large amounts of sensing data from autonomous vehicles to digital pathology. This course covers technologies and methodologies necessary for inferring useful information and identifying underlying patterns from often raw, incomplete, noisy and corrupted data that is present in real-life applications.

This course will help Engineers and Technicians build their ability to assemble and analyze data needed to face challenges in engineering systems. They will also learn to evaluate advanced computing tools, simulations, modelling, and engineering optimization. Additionally, they will develop and polish your skills in project management, team leadership, and effective communication.

What you will learn in this program:

• Machine learning and predictive analytics
• Statistical methods and decision science
• Visualization tools and techniques
• Optimization of products, processes, research, design, testing and operations
• Leadership and communication skills to effectively manage change

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. 

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

• Basic IoT protocols
• Big Data: “where” and “when”
• Big Data distributed files with HDFS
• MapReduce vs. Spark Data Sharing
• Big Data Ecosystem bird's eye view: Spark, Mongo DB, Cassandra, Flume, Cloudera, Oozie, Mahout