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Using Data Science to Drive Business Decision-Making

ICT/ ICT MANAGEMENT/
Using Data Science to Drive Business Decision-Making
SCHEDULED DAYS

coming soon

LANGUAGE

Greek

NUMBER OF PARTICIPANTS

max 12

PREREQUISITES

About this training

This course teaches the value of data science and how to apply it to solve business problems by extracting actionable insights from data. Using Excel, R and Python, we cover all the essential steps including data analysis, visualization, pattern identification and hypothesis testing. Participants will be exposed to real case studies and work on a case study of their choice, present their findings, and learn how to communicate insights effectively to business leaders. The ultimate focus of this course is using data science to drive better business decision-making.

Intended audience:

This course is designed for junior professionals or those new to data science who want to enhance their analytical and communication skills and boost their career path. It’s ideal for individuals interested in learning how to approach and solve business problems through the use of data and presenting their insights to business leaders.

Learning Objectives

Course Outline:

  • Improving business outcomes with data science: from executive questions to questions, data, analysis and recommendations.
  • The good thing: your analysis can be structured.
  • Presenting and supporting your recommendations: preparing reports and presentations.

 

Part 1: Introduction to Data Science:

  1. Why data science:
  • It is not about complicated models or fancy code.
  • The ultimate goal is to transform raw data into valuable insights (by uncovering patterns, making predictions, or optimizing systems).
  • Data Science is a career booster.
  1. How to implement Data Science:
  • Tools: Excel, R and Python
  • Methods: operationalize the problem, find the data, clean, preprocess and explore the data to identify patterns. Then apply appropriate models or techniques to make predictions or recommendations.
  1. What do we do (in practice)?
  • Real-world example: customer churn problem (or other, tbd) – approaching the business problem and using Data Science tools and methods to give insights to the business.

 

Part 2: Data Analysis and Problem Categories:

  1. Data Analysis:
  • Present an analysis of a dataset, focusing on the insights that can be uncovered through effective summary statistics and data visualization.
  • Highlight the importance of fully understanding your data before diving into further modeling or advanced algorithms.
  1. Types of Data Science Problems:
  • Discuss four main categories of data science problems:
    • Classification
    • Regression
    • Optimization
    • Clustering
  • For each category, a relevant case study will be presented (tbd), illustrating the business problem and how data science methods are applied to provide answers and insights.
  1. Brief assignment instructions (just give the assignment to the participants)?
  • Each person/team needs to make a brief presentation (elevator pitch) based on a data set and business problem of their choice.
  • The participants can use any tool and the problem can be a simple data analysis or a complex machine learning algorithm.
  • The point is to present a business problem and use the necessary tools to give guidance towards solving it (which is the purpose of Data Science).

 

Part 3: Assignment and Report Preparation:

  1. Presentations:
  • Each participant or team will present their findings and approach to the assigned business problem.
  • After each presentation, a brief discussion will follow, where feedback and suggestions for improvement will be provided.
  1. Guidance about reporting and presenting / supporting your findings:
  • The importance of storytelling in Data Science.
  • Present findings in a clear, impactful way, tailored for business and executive managers, ensuring that insights gained drive actionable decisions.

TRAINING CATEGORIES