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

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