Data Science for Business Decision-Makers – For more AI Careers

About Course

Level: Beginner | Duration: 8 Weeks | Mode: Self-paced

Target Audience:

Students from Management, Commerce, Finance, Economics, and International Business backgrounds.

Learning Approach:

Concept-first, tool-supported.
No coding expertise required (basic use of Excel, Power BI, Tableau, or ready-made Python dashboards).
Focus on how Data Science supports business decision-making.

Learning Materials:

Short video lectures (2–5 min)
Interactive quizzes
Real-world case studies
Ready-to-use data setsSimple tools tutorials (Excel, Power BI)

Certification:

Certificate of Completion

Special Mention Certificate for top Grade Student

Course Content

Module 1 : Introduction to Data and Business Intelligence
This course is designed to equip business decision-makers from non-technical backgrounds with a foundational understanding of data science. In today's data-driven world, the ability to comprehend, interpret, and leverage data insights is crucial for strategic decision-making. This program will demystify data science concepts, tools, and methodologies, focusing on their practical application in business contexts. Participants will learn how to ask the right questions, understand the potential and limitations of data, effectively communicate with data science teams, and drive data-informed strategies without needing to become data scientists themselves. The goal is to empower leaders to confidently navigate the data landscape and harness its power for competitive advantage.

1.1 What is Data? Types of data (structured, unstructured), data sources, and the data lifecycle.
1.2 The Rise of Data in Business: Why data is critical for modern business, competitive advantage, and digital transformation.
1.3 Understanding Business Intelligence (BI)- Difference between BI and Data Science, common BI tools and dashboards, and their role in decision-making.
1.4 Data Literacy for Leaders- Key terms, concepts, and how to speak the language of data.
Test 1: Introduction to Data and Business Intelligence – Multiple Choice Questions

Module 2 : Core Concepts of Data Science
This module introduces the fundamental principles of data science, its typical workflow, the various roles involved, and crucial ethical considerations.

Module 3 : Data Collection, Storage, and Preparation
This module focuses on the practical aspects of handling data, from where it comes from to how it's stored and made ready for analysis.

Module 4: Exploratory Data Analysis (EDA) and Visualization
This module delves into the crucial process of understanding your data through exploration and presenting insights effectively through visualization.

Module 5: Predictive Analytics and Machine Learning Fundamentals
This module introduces the exciting world of predictive analytics and machine learning, explaining how businesses can use data to forecast future events and automate decision-making.

Module 6: Advanced Analytics Concepts and Applications
This module explores more advanced analytical techniques and their diverse applications, helping business leaders understand the broader spectrum of problems data science can solve.

Module 7: Implementing Data Science in Business
This module focuses on the practical aspects of integrating data science into business operations, from identifying opportunities to managing projects and measuring impact.

Module 8: Future Trends and Strategic Imperatives
This final module looks ahead, exploring emerging trends in data science and AI, and discusses how business leaders can develop a forward-looking data strategy to stay competitive.

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About Course

Course Content

1.1 What is Data? Types of data (structured, unstructured), data sources, and the data lifecycle.

1.2 The Rise of Data in Business: Why data is critical for modern business, competitive advantage, and digital transformation.

1.3 Understanding Business Intelligence (BI)- Difference between BI and Data Science, common BI tools and dashboards, and their role in decision-making.

1.4 Data Literacy for Leaders- Key terms, concepts, and how to speak the language of data.

Test 1: Introduction to Data and Business Intelligence – Multiple Choice Questions

Module 2 : Core Concepts of Data Science This module introduces the fundamental principles of data science, its typical workflow, the various roles involved, and crucial ethical considerations.

2.1 What is Data Science? Defining data science, its interdisciplinary nature (statistics, computer science, business acumen).

2.2 The Data Science Workflow- Overview of typical steps from problem definition to deployment and monitoring.

2.3 Key Data Science Roles : Understanding the functions of data scientists, data engineers, machine learning engineers, and data analysts.

2.4 Ethical Considerations in Data Science: Bias, privacy, fairness, and responsible AI.

Test 2: Core Concepts of Data Science

Module 3 : Data Collection, Storage, and Preparation This module focuses on the practical aspects of handling data, from where it comes from to how it's stored and made ready for analysis.

3.1 Data Sources and Acquisition: Internal vs. external data, APIs, web scraping (conceptual understanding).

3.2 Introduction to Data Storage: Databases (SQL vs. NoSQL), data warehouses, and data lakes (conceptual understanding).

3.3 Data Cleaning and Preprocessing: Why “dirty data” is a problem, common issues (missing values, outliers), and the importance of data quality.

3.4 Data Governance and Security: Ensuring data integrity, compliance, and protection.

Test 3: Data Collection, Storage, and Preparation

Module 4: Exploratory Data Analysis (EDA) and Visualization This module delves into the crucial process of understanding your data through exploration and presenting insights effectively through visualization.

4.1 Understanding Your Data- The purpose of EDA, identifying patterns, anomalies, and relationships.

4.2 Key Statistical Concepts (Simplified): Averages, median, mode, variance, correlation – what they mean for business.

4.3 Principles of Data Visualization- Why good visualizations matter, common chart types (bar, line, pie, scatter), and best practices.

4.4 Interpreting Dashboards and Reports: How to derive actionable insights from visual data representations.

Test 4: Exploratory Data Analysis (EDA) and Visualization

Module 5: Predictive Analytics and Machine Learning Fundamentals This module introduces the exciting world of predictive analytics and machine learning, explaining how businesses can use data to forecast future events and automate decision-making.

5.1 Introduction to Predictive Analytics: Forecasting, classification, and regression – what problems they solve.

5.2 What is Machine Learning? Supervised vs. unsupervised learning, training data, and model building (conceptual).

5.3 Common ML Algorithms (High-Level): Decision Trees, Regression, Clustering – understanding their business applications.

5.4 Evaluating Model Performance: Accuracy, precision, recall – what these metrics mean for business outcomes.

Test 5: Predictive Analytics and Machine Learning Fundamentals

Module 6: Advanced Analytics Concepts and Applications This module explores more advanced analytical techniques and their diverse applications, helping business leaders understand the broader spectrum of problems data science can solve.

6.1 Natural Language Processing (NLP): Understanding text data, sentiment analysis, and chatbots.

6.2 Computer Vision: Image recognition, object detection, and its business uses (e.g., quality control).

6.3 Recommender Systems: How they work and their impact on customer experience and sales.

6.4 Time Series Analysis: Forecasting trends and patterns over time (e.g., sales forecasting, stock prices).

Test 6: Advanced Analytics Concepts and Applications

Module 7: Implementing Data Science in Business This module focuses on the practical aspects of integrating data science into business operations, from identifying opportunities to managing projects and measuring impact.

7.1 Identifying Business Problems for Data Science: Translating business challenges into data questions.

7.2 Building a Data-Driven Culture: Overcoming organizational hurdles, fostering collaboration, and change management.

7.3 Working with Data Science Teams: Effective communication, setting expectations, and managing projects.

7.4 Measuring ROI of Data Science Initiatives: Quantifying the value and impact of data projects.

Test 7: Implementing Data Science in Business

Module 8: Future Trends and Strategic Imperatives This final module looks ahead, exploring emerging trends in data science and AI, and discusses how business leaders can develop a forward-looking data strategy to stay competitive.

8.1 AI and Generative AI in Business: Understanding the latest advancements and their potential impact.

8.2 Cloud Computing for Data Science: The role of cloud platforms (AWS, Azure, GCP) in scaling data initiatives.

8.3 Data Mesh and Data Products: Modern approaches to data architecture and data accessibility.

8.4 Developing a Data Strategy: Crafting a roadmap for data maturity and innovation within your organization.

Test 8

Certificate on Completion