Certificate in Data Science

About the Course

Name: Certificate in Data Science

Code:

Sectors: Live/Online Class , Business Intelligence & Data Analysis

  Date   Days   Venue   Fees
07 - 11 Nov 2021 5 Live Online Classroom $1,450 BOOK NOW

Introduction

Data is one of the biggest assets of any organization. It helps firms understand and enhance their processes, thereby saving time and money.  The efficient use of data helps businesses to reduce wastage by analyzing different marketing channels’ performance and focusing on those offering the highest ROI. However, data is useless unless it is converted into valuable information. Data Science plays a big part in this conversion as it involves mining large datasets containing structured and unstructured data and identifying hidden patterns to extract actionable insights. 

This training course is designed to provide participants with the relevant best practices, and the essential concepts of the Big Data ecosystem, as well as the opportunities for Artificial Intelligence. Additionally, this course will discuss the disciplines to which modern data relates. This will allow participants to gain the skills and knowledge in order to become specialists in techniques and technologies and deal professionally with experts in all advanced data management fields.

Objectives

By the end of the course, participants will be able to:

  • Gain a comprehensive understanding of the concept of data science and designing data for efficient analysis.
  • Effectively manage Data Science projects with project management best practices
  • Identify the difference between predictive models and pattern finding ones
  • Compare solutions related to Data Analysis vs. Machine Learning
  • Learn about the concept of “proprietary” and “open source” technologies
  • Create a modern data flow outline from sources to reports.

Training Methodology

This training course is designed to be highly interactive and participatory. To ensure maximum comprehension and retention, this training will utilize a variety of proven virtual learning methods such as break-out sessions for group discussions and brainstorming, virtual icebreakers, recorded videos, case studies, and readings.

Outline

Day 01

  • Introduction: 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

Day 02

  • 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

Day 03

  • 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

Day 04

  • 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

Day 05

  • 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

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