date science for business intelligence

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

• 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