Data Science

Module 1: The Essence of Data Science

1.1 Defining Data Science: Interdisciplinary Nature
1.2 The Data Science Lifecycle: From Problem to Insight
1.3 Types of Data: Structured, Unstructured, and Semi-structured
1.4 The Role of a Data Scientist (Core Competencies)

Module 2: Foundational Statistics & Probability

2.1 Descriptive Statistics: Measures of Central Tendency (Mean, Median, Mode)
2.2 Descriptive Statistics: Measures of Dispersion (Variance, Standard Deviation, Quartiles)
2.3 Introduction to Probability: Basic Concepts and Rules
2.4 Key Probability Distributions: Normal Distribution, Binomial Distribution (Conceptual)
2.5 Inferential Statistics: Introduction to Hypothesis Testing and Confidence Intervals

Module 3: Understanding & Preparing Data

3.1 Data Sources and Collection Methods (Conceptual)
3.2 The Importance of Data Quality
3.3 Conceptual Data Cleaning: Identifying and Handling Errors, Duplicates, and Inconsistencies
3.4 Missing Data Strategies (Conceptual Imputation vs. Deletion)
3.5 Data Transformation Concepts: Scaling and Normalization (Why and When)

Module 4: Exploring & Communicating with Data

4.1 Principles of Exploratory Data Analysis (EDA)
4.2 Identifying Patterns, Trends, and Outliers through EDA
4.3 The Role of Data Visualization: Why Visuals Matter
4.4 Common Chart Types and Their Purpose (e.g., Bar Chart, Line Graph, Scatter Plot, Histogram - Conceptual Use Cases)
4.5 Communicating Insights: Storytelling with Data

Module 5: Introduction to Machine Learning

5.1 What is Machine Learning? Learning from Data
5.2 Supervised Learning: Predicting Outcomes (Conceptual)Regression (Predicting Continuous Values)
Classification (Predicting Categories)
5.3 Unsupervised Learning: Finding Patterns (Conceptual)Clustering (Grouping Similar Data Points)
Dimensionality Reduction (Simplifying Data)
5.4 Model Training, Evaluation, and Prediction (Conceptual Flow)
5.5 Understanding Overfitting and Underfitting

Module 6: Core Machine Learning Algorithms (Conceptual Overview)

6.1 Linear Regression: Understanding Relationships
6.2 Logistic Regression: Binary Prediction
6.3 Decision Trees: Rule-Based Decisions
6.4 K-Nearest Neighbors (KNN): Proximity-Based Classification
6.5 K-Means Clustering: Grouping Data
6.6 Conceptual Model Evaluation Metrics: Accuracy, Precision, Recall (What they mean, not how to calculate)

Module 7: The Data Science Workflow

7.1 Step-by-step: Define problem → Gather data → Clean → Analyze → Visualize → Model → Communicate

7.2 CRISP-DM and other common project frameworks

Module 8: Tools of the Trade

8.1 Brief intro to Python, Pandas, SQL, Power BI

8.2 No-code and low-code alternatives

8.3 How tools work together in a real project

Module 9: Thinking Like a Data Scientist

9.1 Asking the right questions

9.2 Telling stories with data

9.3 Communicating findings to non-tech audiences

9.4 Ethics in data usage

Module 10: Data Ethics and Problem Solving

10.1 Ethical Considerations in Data Science: Bias, Fairness, Privacy
10.2 The Importance of Domain Knowledge
10.3 Formulating Data Science Problems Effectively
10.4 The Iterative Nature of Data Science Projects