Certificate in Data Science

Course content

Download PDF File

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

Certificate in Data Science

Course Date

Course content

United Kingdom

Netherlands

Georgia