Data Analysis with Python

Our Data Analysis with Python course at Philip Code Academy equips you with the skills to collect, clean, analyze, and visualize data, enabling you to make data-driven decisions with confidence. Designed for both beginners and those with some programming background, this course blends practical coding skills with analytical thinking to prepare you for real-world data challenges.

InstructorShodolamu Opeyemi

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Description

Shodolamu Opeyemi Philip

The lead instructor at Philip Code Academy, specializing in empowering learners with practical and industry-relevant skills. With a strong foundation in coding, data analysis, and problem-solving, he is passionate about transforming beginners into confident programmers capable of building real-world projects. Through an engaging, hands-on teaching approach, Philip equips students with the knowledge to write clean, efficient Python code and leverage it for diverse applications, including automation, data analysis, and software development.

Data Analysis with Python – Course Overview

Our Data Analysis with Python course at Philip Code Academy equips you with the skills to collect, clean, analyze, and visualize data, enabling you to make data-driven decisions with confidence. Designed for both beginners and those with some programming background, this course blends practical coding skills with analytical thinking to prepare you for real-world data challenges.

What You’ll Learn

Introduction to Data Analysis
- Understanding the data analysis process and its real-world applications.
- Setting up your Python environment for data work.
Working with Python for Data Analysis
- Review of Python fundamentals for data tasks.
- Key libraries: NumPy, Pandas, Matplotlib, and Seaborn.
Data Collection & Importation
- Reading datasets from CSV, Excel, SQL databases, and web sources.
Data Cleaning & Preparation
- Handling missing data, duplicates, and inconsistent formats.
- Data transformation and formatting techniques.
Exploratory Data Analysis (EDA)
- Descriptive statistics and data summarization.
- Data grouping, filtering, and sorting.
Data Visualization
- Creating clear and compelling charts with Matplotlib and Seaborn.
- Visualizing distributions, trends, and relationships in data.
Handling Time Series Data
- Working with dates, times, and time-based analysis.
Basic Data Analysis Projects
- Applying learned skills to real-world datasets.

Key Takeaways

Ability to manipulate, analyze, and visualize data efficiently using Python.
Practical experience with industry-standard libraries and tools.
Foundation to progress into advanced analytics, data science, or machine learning.