Data Science

Unit 1: Introduction to Data Science

• Defining Data Science
• Roles and Responsibilities in Data Science
• Data Science in Business
• Use Cases for Data Science
• Relationship Between:
  • Data Science and Big Data
  • Data Science and Machine Learning
• Data Science Process Overview:
  • Defining Goals
  • Retrieving Data
  • Data Preparation
  • Data Exploration
  • Data Modeling
  • Presentation

Unit 2: Introduction to Statistics

• What is Statistics?
• Descriptive Statistics:
  • Introduction
  • Population and Sample
  • Types of Variables
  • Measures of Central Tendency
  • Measures of Variability
  • Coefficient of Variance
  • Skewness and Kurtosis
• Inferential Statistics:
  • Normal Distribution
  • Test Hypotheses
  • Central Limit Theorem
  • Confidence Interval
  • T-Test
  • Type I and II Errors

Unit 3: Machine Learning Introduction and Concepts

• Machine Learning Basics:
  • Modeling Process
  • Training Model
  • Validating Model
  • Predicting New Observations
• Key Terminologies in Machine Learning
• Types of Machine Learning Algorithms:
  • Supervised Learning Algorithms:
    • Regression: Linear Regression
    • Classification Algorithms
  • Unsupervised Learning Algorithms:
    • Clustering Algorithms

Unit 4: Data Visualization and Ethics

• Introduction to Data Visualization:
  • Visualization Options
  • Filters
  • Python Libraries for Visualization:
    • Matplotlib
    • Seaborn
• Data Science Ethics:
  • Doing Good Data Science
  • Ownership of Data
  • Valuing Different Aspects of Privacy
  • Getting Informed Consent
  • The Five Cs (Care, Consent, Confidentiality, Context, and Clarity)
  • Diversity and Inclusion
  • Future Trends