Python for Data Science: Unlock Data Insights with Python's Most Powerful Libraries

Course Overview

This course is perfect for individuals looking to leverage Python for data science, covering data manipulation, analysis, and visualization using the most popular Python libraries. You'll learn how to clean and organize data with Pandas, perform statistical analysis, and create insightful data visualizations using Matplotlib and Seaborn. By the end of the course, you will be able to work with large datasets and turn them into actionable insights.

Skills you'll aquired

Data manipulation and cleaning with Pandas
Statistical analysis and hypothesis testing
Data visualization using Matplotlib and Seaborn
Working with NumPy for numerical operations
Reading and writing data from various formats (CSV, Excel, SQL, etc.)
Exploratory Data Analysis (EDA)
Time series data analysis
Building and evaluating machine learning models using Scikit-learn

Course Features:

50 Hours

Live Class and Video Suggestions

18 Articles

12 Downloadable Resources

43 Practicals

54 Hands-on Projects

Certificate upon Completion

Live Interactions

Course Compact

Get introduced to the world of data science and Python's role in it.

What is Data Science?
Setting up the Python Environment (Jupyter Notebook)
Overview of Python Libraries for Data Science (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)

Learn to clean, process, and analyze large datasets.

Loading and Exploring Datasets
Data Wrangling and Cleaning with Pandas
Merging, Grouping, and Pivoting Data

Visualize your data to uncover patterns and insights.

Plotting with Matplotlib (Line Charts, Bar Charts, Histograms)
Advanced Data Visualization with Seaborn (Heatmaps, Pairplots)
Customizing and Exporting Visuals for Reports

Handle large data arrays and perform efficient numerical operations.

Working with NumPy Arrays and Matrices
Mathematical Operations on Datasets
Broadcasting and Vectorization

Analyze and interpret data patterns to make better decisions.

Conducting EDA with Pandas
Identifying Patterns, Trends, and Outliers
Handling Missing Values and Outliers

Build predictive models to make data-driven decisions.

Overview of Machine Learning Concepts
Implementing Simple Regression and Classification Models
Evaluating Model Performance (Accuracy, Precision, Recall)

Requirements

Basic knowledge of Python programming
Familiarity with basic statistics is beneficial but not required
Computer and access to internet

Description

The Python for Data Science course offers a comprehensive introduction to data manipulation, analysis, and visualization using Python's powerful libraries. Through a mix of lectures, hands-on projects, and real-world case studies, you will gain in-depth knowledge of Pandas, NumPy, Matplotlib, and Seaborn. This course will take you from a beginner to proficient data analyst capable of handling complex datasets, performing exploratory data analysis, and creating insightful visualizations that can drive key business decisions.

Meet the Instructor

Mayomi Odewaye

Software Engineer / Data Analyst / Project Manager

Mayomi Odewaye is a dynamic and accomplished professional who has made significant contributions to the fields of software engineering, data analysis, and project management. With a strong academic background and practical experience in Python programming, Machine Learning, Web Development, and PHP, Mayomi is passionate about teaching and mentoring aspiring developers. As the founder of KrossCheck, Mayomi is dedicated to providing innovative solutions that enhance the educational experience and streamline academic processes.