Course Objectives
Certificate in 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 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
Course Outline
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
MapReduce vs. Spark Data Sharing
Big Data Ecosystem bird's eye view: Spark, Mongo DB, Cassandra, Flume, Cloudera, Oozie, Mahout