Certificate in Data Science

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.

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