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 realworld datasets.
Audience
This course is designed for programmers already familiar with the basics of Python and are interested in working with realworld 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 realworld 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 – scikitlearn

 Exploring the scikitlearn library
 Building a Machine Learning Model
 Model Evaluation and Validation
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