Fundamentals of Data Science

Program:
DATA SCIENCE
Ngole E.
Ngole E.
Entrepreneur

Overview

  • Lectures 27
  • Projects 20
  • Duration 15 hours
  • Skill level Beginner
  • Language ENGLISH
  • Assessments YES
Course Description
Fundamentals of Data Science

The Fundamentals of Data Science course at the Vocational Training Institute of Creative Computer Specialists provides a robust introduction to the essential skills and concepts of data science, tailored for aspiring data analysts, scientists, and anyone interested in transforming data into meaningful insights. This course is structured to give students a solid foundation in data science, enabling them to extract, analyze, and visualize data effectively to support decision-making across various industries.

Key Learning Outcomes:

Upon successful completion of this course, students will be able to:

  1. Understand Core Data Science Concepts: Grasp the basics of data science, including its role in business and industry, essential terminologies, and an overview of the data science lifecycle.
  2. Work with Data Using Python and R: Develop programming skills in Python and R, the primary languages for data science, and learn to manipulate, clean, and process large datasets.
  3. Master Data Wrangling and Preprocessing: Gain proficiency in preparing raw data for analysis by handling missing values, data normalization, and feature engineering.
  4. Explore Statistics and Probability Fundamentals: Apply statistical and probabilistic methods to analyze data and make data-driven decisions.
  5. Learn Data Visualization Techniques: Discover how to create impactful data visualizations using tools like Matplotlib, Seaborn, and Tableau to communicate insights effectively.
  6. Conduct Exploratory Data Analysis (EDA): Understand and implement techniques for examining datasets to uncover patterns, anomalies, and relationships in the data.
  7. Introduction to Machine Learning Basics: Gain exposure to basic machine learning algorithms, including linear regression, classification, and clustering, with a focus on supervised and unsupervised learning models.
  8. Work on Real-World Projects: Apply knowledge gained throughout the course in a comprehensive capstone project, where students solve real-world problems using data science techniques.

Methodology:

This course is 100% practical, utilizing hands-on projects, collaborative exercises, and case studies to ensure students acquire both the technical skills and analytical mindset essential for data science. Each module includes practical assignments, real-life datasets, and interactive sessions, ensuring students gain industry-relevant experience.

Target Audience:

This course is ideal for high school graduates, entrepreneurs, and working professionals across Buea and the greater regions of the Southwest, Littoral, Northwest, and Cameroon who are interested in building a career in data science or leveraging data-driven insights in their respective fields.

Course Duration: 9 - 12 months, available in in-person, online, and hybrid formats.

Curriculum

    The curriculum for this course is currently under review and will be finalized soon.
    • Lesson 1. Introduction to Data Science
      15 minutes
    • Lesson 2. What is Data Science?
      20 minutes
    • Lesson 3. The Data Science Lifecycle
      15 minutes
    • Lesson 4. Applications of Data Science
      15 minutes
    • Lesson 5. Ethical Considerations in Data Science
      20 minutes
    • Lesson 1. Data Wrangling and Preprocessing
      5 minutes
    • Lesson 2. Types of Data
      15 minutes
    • Lesson 3. Data Cleaning
      25 minutes
    • Lesson 4. Data Transformation
      25 minutes
    • Lesson 5. Data Integration
      20 minutes
    • Lesson 6. Introduction to Relational Databases
      30 minutes
    • Lesson 1. Exploratory Data Analysis (EDA)
      40 minutes
    • Lesson 2. Descriptive Statistics
      25 minutes
    • Lesson 3. Data Visualization
      20 minutes
    • Lesson 4. Identifying patterns and trends in data
      15 minutes
    • Lesson 5. Communicating insights through visualizations and summaries
      40 minutes
    • Lesson 1. Introduction to Statistical Inference and Modeling
      10 minutes
    • Lesson 2. Basic Statistics
      20 minutes
    • Lesson 3. Hypothesis Testing
      15 minutes
    • Lesson 4. Regression Analysis
      20 minutes
    • Lesson 5. Model Evaluation
      25 minutes
    • Lesson 1. Communicating Results and Next Steps
      10 minutes
    • Lesson 2. Communicating data insights
      20 minutes
    • Lesson 3. Creating compelling presentations and reports
      20 minutes
    • Lesson 4. Discussing limitations of analyses and future research directions
      25 minutes

Instructor

Ngole E.
Ngole E.
Entrepreneur

Ngole E.

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