Joseph Yeates – Python Fundamentals – CFI Education

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Joseph Yeates – Python Fundamentals – CFI Education

Python Fundamentals

• Start your hands-on data science journey by coding in Python to import, explore, transform, analyze, and visualize data.Connect data from various sources for a comprehensive analysis
• Gain a deeper understanding of your data with exploratory data analysis through common Python data structures
• Prepare your data for analysis, modeling, and machine learning with popular data transformation libraries and functions

Python Fundamentals
Led by Joseph Yeates

• Level 4
• Approx 5.5h to complete
• 100% online and self-paced

Overview

Python Fundamentals Course Overview
Python is the most popular programming language used for data science and is a must-know to start or advance your career in data. In this course, you will learn the most fundamental skills to write and execute Python code. We will have an overview of the basic Python concepts, which will enable you to get started with a data science project! You will see how to load, clean, analyze, and transform data with two popular Python packages: Numpy and Pandas. Then, we will demonstrate how to effectively communicate the key insights from your analysis by visualizing your data using the Matplotlib and Seaborn packages. Finally, you’ll combine these skills and put your new knowledge into practice by analyzing financial data through a case study.

Python Fundamentals Learning Objectives
Upon completing this course, you will be able to:Write and execute Python code to create variables, generate outputs, apply various operators, and manipulate different types of data

• Capture and transform data using Numpy and Pandas packages
• Explore data through different statistical methods to gain a deeper understanding
• Visualize data to share insights using the Matplotlib and Seaborn packages
• Combine and apply the skills above to analyze financial data

Who Should Take This Course?
This Python Fundamentals course is perfect for anyone who would like to build up their programming skills and use Python for data science to analyze data. This course is designed to equip anyone who desires to begin or further their career in data analysis, quantitative analysis, business intelligence, or other areas of business and finance.

What you’ll learn

• Lesson 
• Multimedia 
• Exams 
• Files

Course Introduction
Course Introduction
Learning Objectives
Download Learner Files
Install Anaconda
Python Development Environments
Jupyter Notebook Overview

Python Concepts
Chapter Introduction – Python Concepts
Variables & Data Types
Data Structures
Operators & Functions
Conditional Statements & For Loops
Chapter Summary – Python Concepts

Loading & Cleaning Data
Chapter Introduction – Loading & Cleaning Data
Introduction to NumPy & Pandas
Loading Data
Cleaning Data
Chapter Exercise – Loading & Cleaning Data

Analyzing Data
Chapter Introduction – Analyzing Data
Transforming Data
Statistical Analysis
Chapter Exercise – Analyzing Data

Visualizing Data
Chapter Introduction – Visualizing Data
Visualizing Data for Exploratory Analysis
Visualizing Data for Sharing Insights
Chapter Exercise – Visualizing Data

Case Study – Portfolio Optimization
Case Study Introduction – Portfolio Optimization
Import Packages & Connect to Data
Create the Equal-Weighted Portfolio
Visualize the Portfolio Performance
Calculate Performance Metrics for the Portfolio
Calculate the Sharpe Ratio for the Portfolio
Prepare Scenarios to Optimize Portfolio Weighting
Build the Portfolio Scenarios
Generate the Portfolio Scenarios
Identify the Optimal Portfolio
Visualize the Optimal Portfolio & Portfolio Scenarios
Case Study Summary – Portfolio Optimization

Course Summary
Course Summary

Qualified Assessment
Qualified Assessment

What our students say

Good course
Great course
Brian Mutondo

Good
Great
Oluwatosin Oyeniran

Intriguing
great course
Uzoamaka Chinyem

Insightful well structured content

This course starts off with explaining Python concepts to loading and cleaning data, analyzing data, and data visualization. The case studies used to demonstrate scenarios make it easier to follow and understand. Python really does the work at every stage of data analysis. This is a language aspiring data analysts should learn.
Linda Etuhole Nakasole

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