Data Science for Managers Essential Tools and Strategies | IABAC

In order to facilitate data-driven decision-making, predictive insights, and efficient resource management, this presentation gives managers the fundamental data science tools and techniques they need. In order to convert unprocessed data into useful business outputs, it places a heavy focus on analytics, visualization, and practical methods.

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Data Science for Managers:
Essential Tools and
Strategies

2. Data-driven decisions outperform intuition-based
decisions.
Managers can identify trends, risks, and opportunities
efficiently.
Examples: Forecasting sales, optimizing resources,
improving customer experience.
Visual: Infographic showing “Intuition vs Data-Driven
Decisions”
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Why Data Science Matters for
Managers

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Data Types: Structured, Unstructured, Semi-structured.
Analytics Levels: Descriptive, Diagnostic, Predictive,
Prescriptive.
Machine Learning Basics: Supervised vs Unsupervised
learning.
Visual: Hierarchy diagram of analytics levels.
Core Data Science Concepts

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Data Analysis & Visualization: Excel, Tableau, Power BI.
Statistical Tools: R, Python (pandas, numpy).
Collaboration & Workflow: Jupyter, Google Data Studio,
Slack integration.
Visual: Tool logos in an organized grid.
Essential Tools for Managers

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Establishing clean and reliable data pipelines.
Use of CRM, ERP, and cloud databases.
Importance of data governance & compliance.
Visual: Flowchart of “Data Collection → Storage →
Analysis → Action”.
Data Collection & Management
Strategies

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Focus on key metrics aligned with business goals.
Avoid common pitfalls: correlation vs causation, biased
data.
Use dashboards for actionable insights.
Visual: Sample dashboard with KPIs.
Interpreting Data Effectively

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Forecasting demand, sales, and resource allocation.
Risk analysis and scenario planning.
Example: Predictive churn analysis for customers.
Visual: Line chart showing prediction vs actual.
Predictive Analytics for
Managers

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Case study: How data improved decision quality.
Balancing human intuition with analytics.
Continuous improvement via feedback loops.
Visual: Decision-making flowchart with data inputs.
Data-Driven Decision Making

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Common challenges: Data quality, tool adoption,
resistance to change.
Best practices: Clear KPIs, iterative approach, manager-
data scientist collaboration.
Encouraging a data-driven culture in teams.
Visual: Table of “Challenges vs Solutions”.
Challenges & Best Practices

10. Thank You
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