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Strategies and Concepts for Data Scientists and Business Analysts

Course Details
Code: BADS41
Tuition (USD): $2,475.00 • Classroom (3 days)
$2,475.00 • Virtual (3 days)
Course Details
GSA (USD): $2,244.33 • Classroom (3 days)
$2,244.33 • Virtual (3 days)

To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.

This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.

Skills Gained

  • express a business problem as a manageable analytical question
  • identify the appropriate analysis to address the question
  • visualize and explore data
  • select statistical analyses that help answer the question
  • translate complex statistical results into actionable business decisions.

Who Can Benefit

  • Statisticians, market researchers, information technology professionals, data scientists, and business analysts who want to make better use of their data

Prerequisites

  • Before attending this course, you should have taken a college-level course in statistics, covering distribution analysis, hypothesis testing, and regression techniques or have equivalent knowledge. You can gain this knowledge from the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.

Course Details

Overview

  • defining terms
  • concepts of data science
  • exploring and visualizing data

Segmentation

  • pitfalls and good practices
  • interpreting clusters

Predictive Modeling

  • fundamentals
  • decision trees
  • random forests
  • model comparison
  • mass-scale predictive modeling
  • model deployment and management

Design of Experiments

  • why experiment?
  • types of business experiments
  • incremental response modeling

Unstructured Data

  • text analytics
  • social networks

Communicating Your Results

  • knowing your audience
  • creating effective messages
  • demonstrating value