Introduction to Data ScienceDownload

Description

This three day workshop will introduce participants to the field of data science using Python. We’ll cover key concepts such as data manipulation, data analysis, and data visualization using popular Python libraries. Participants will also learn basic machine learning techniques and how to apply them to real-world datasets.

Audience

This course is designed for programmers already familiar with the basics of Python and are interested in working with real-world data sets, as well as the basics of machine learning.

Prerequisites

  • Basic knowledge of Python programming
  • Familiarity with concepts of data types, variables, loops, and functions in Python
  • No prior knowledge of data science is required

Objectives

Five Core Concepts, including fundamentals, machine learning, & data cleaning.
  • Understand the fundamentals of data science and its importance in industry.
  • Use key Python libraries like Pandas, NumPy, and Matplotlib for data manipulation and analysis.
  • Apply basic machine learning techniques to real-world datasets.
  • Gain proficiency in data cleaning and visualization using Python.
  • Develop a foundational knowledge of machine learning algorithms and model evaluation.

Outline

Chapter One – Getting Started
    • Importance of Data Science in Industry
    • The Wave
    • Overview of key Python Libraries
      • Pandas
      • NumPy
      • Matplotlib
Chapter Two – NumPy
    • NumPy Basics
    • Array Operations
    • Handling Multidimensional Data
    • Array Indexing and Slicing
    • Broadcasting and Vectorization
    • Data Aggregation
Chapter Three – Pandas Mechanics
    • Introduction to Pandas
    • Data Structures
    • Data Importing and Exporting
    • Data Cleaning
Chapter Four – Data Analysis with Pandas
    • Grouping and Aggregating Data
    • Time Series Analysis
    • Date Transformation and Reshaping

Chapter Five – Data Visualization with Matplotlib
  • Understanding Matplotlib
  • Creating Basic Plots (line plots, bar plots, scatter plots)
  • Customizing Plots and Adding Annotations

Chapter Six – Overview of Machine Learning
    • What is Machine Learning
    • Types of Machine Learning Algorithms
      • Supervised Learning
      • Unsupervised Learning
      • Reinforcement Learning
Chapter Seven – scikit-learn 
    • Exploring the scikit-learn library
    • Building a Machine Learning Model
    • Model Evaluation and Validation

Have Questions? Want to learn more? We’d love to talk to you!

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