Data Science Fundamentals

Unlock the power of data with our comprehensive online course designed for beginners. Learn the essential concepts and tools needed to succeed in the field of data science.

Course Description

This course provides a solid foundation in data science, covering essential topics such as data analysis, data visualization, and machine learning. You will learn how to use Python and popular data science libraries like NumPy, Pandas, and Scikit-learn to explore, analyze, and model data. Whether you're looking to start a new career or enhance your existing skills, this course will equip you with the knowledge and practical experience to tackle real-world data challenges.

Course Objectives

Upon completion of this course, you will be able to:

  • Understand the core concepts of data science and its applications.
  • Use Python and its libraries to manipulate, analyze, and visualize data.
  • Apply statistical methods to gain insights from data.
  • Build and evaluate machine learning models for prediction and classification.
  • Communicate data-driven findings effectively.
  • Work with various data formats and sources.
  • Understand ethical considerations in data science.
  • Deploy basic machine learning models.

Course Curriculum

  1. Introduction to Data Science
    • What is Data Science?
    • Data Science Process
    • Applications of Data Science
  2. Python Fundamentals
    • Introduction to Python
    • Data Types and Structures
    • Control Flow and Functions
  3. Data Manipulation with Pandas
    • Introduction to Pandas
    • DataFrames and Series
    • Data Cleaning and Transformation
  4. Data Visualization with Matplotlib and Seaborn
    • Introduction to Matplotlib
    • Introduction to Seaborn
    • Creating Charts and Plots
  5. Statistical Analysis
    • Descriptive Statistics
    • Inferential Statistics
    • Hypothesis Testing
  6. Machine Learning Fundamentals
    • Introduction to Machine Learning
    • Supervised vs. Unsupervised Learning
    • Model Evaluation Metrics
  7. Regression Models
    • Linear Regression
    • Polynomial Regression
    • Model Evaluation
  8. Classification Models
    • Logistic Regression
    • Decision Trees
    • Support Vector Machines
  9. Data Science Ethics
    • Bias in Data
    • Privacy Concerns
    • Responsible AI

Prerequisites

No prior experience in data science is required. Basic computer skills and a willingness to learn are essential. Familiarity with programming concepts is helpful but not mandatory.

Profile picture of Dr. Eleanor Vance
Dr. Eleanor Vance

Data Science Instructor

Dr. Vance holds a Ph.D. in Statistics from the University of Chicago and has over 10 years of experience in data science and analytics. She has worked with leading companies in the finance and healthcare industries, helping them leverage data to drive business decisions. Dr. Vance is passionate about teaching and mentoring aspiring data scientists.

Requirements

  • A computer with internet access
  • Python 3.6 or higher installed
  • Basic text editor or IDE (e.g., VS Code, Jupyter Notebook)

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