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Data Science & Analysis - Course Outline

1. Introduction to Data Science

  • Definition and Importance of Data Science
  • Overview of the Data Science Process
  • Key Concepts and Terminology in Data Science

2. Understanding Data Types and Sources

  • Types of Data (Structured, Unstructured, Semi-structured)
  • Common Data Sources (Databases, APIs, Web Scraping)
  • Data Collection Methods

3. Data Preparation and Cleaning

  • Importance of Data Quality
  • Techniques for Data Cleaning and Preprocessing
  • Handling Missing Values and Outliers
  • Data Transformation and Normalization

4. Exploratory Data Analysis (EDA)

  • Overview of EDA Techniques
  • Using Visualization Tools (e.g., Matplotlib, Seaborn)
  • Descriptive Statistics and Summarizing Data
  • Identifying Patterns and Trends in Data

5. Statistical Analysis for Data Science

  • Introduction to Statistics in Data Science
  • Inferential Statistics and Hypothesis Testing
  • Correlation and Regression Analysis
  • ANOVA and Chi-Square Tests

6. Introduction to Programming for Data Science

  • Overview of Programming Languages (Python and R)
  • Basic Programming Concepts (Variables, Data Structures, Control Flow)
  • Libraries and Frameworks for Data Science (Pandas, NumPy, Scikit-learn)

7. Data Visualization Techniques

  • Importance of Data Visualization
  • Creating Effective Visualizations (Bar Charts, Line Graphs, Heatmaps)
  • Tools for Data Visualization (Tableau, Power BI)

8. Machine Learning Fundamentals

  • Introduction to Machine Learning Concepts
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Common Algorithms (Linear Regression, Decision Trees, Clustering)

9. Building Machine Learning Models

  • Data Splitting (Training, Validation, Test Sets)
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
  • Model Optimization and Tuning (Cross-Validation, Grid Search)

10. Big Data Technologies

  • Introduction to Big Data Concepts
  • Overview of Big Data Tools (Hadoop, Spark)
  • Working with Distributed Data Systems

11. Ethics in Data Science

  • Understanding Data Privacy and Security
  • Ethical Considerations in Data Analysis
  • Fairness and Bias in Data Science

12. Capstone Project: Data Science Application

  • Defining the Project Scope and Objectives
  • Applying Data Science Techniques to Real-world Problems
  • Presenting Findings and Insights
  • Reflection on Learning and Challenges

Duration

3 - 4 Months

Benefits

  • Assigned Live Tutor to take you from start to finish via Zoom.
  • Hands-on Practical Training and Assignments.
  • Accredited Examination and Certification Online.
  • Industrial Training / Internships.
  • Career Guide, Mentoring and Continuous Support.
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