Data science skills are increasingly in-demand. We just published a hands-on data science with Python course on the freeCodeCamp.org YouTube channel. This comprehensive, 5.5+ hour course is designed to provide aspiring data scientists with essential skills, blending theory, practical demonstrations, and real-world applications through two detailed projects.
Tatev and Vahe from LunarTech teach this course. They are both experienced engineers with a passion for machine learning. This course offers valuable insights and hands-on experience crucial for your growth in data science.
The course is structured into three main parts:
Part 1: Data Analytics in Python covers the basics of data analytics, including data wrangling, visualization techniques, descriptive statistics, and data filtering and aggregation. You'll learn how to handle and organize data efficiently, create compelling visual stories with data, understand data characteristics through statistical measures, and group, sort, and filter data effectively.
Part 2: AB Testing Fundamentals provides a crash course on experimentation and AB testing theory. You'll learn how to set up hypotheses and interpret results correctly, giving you a solid foundation in AB testing principles.
Part 3: End-to-End Case Studies features two in-depth projects that offer hands-on experience and practical insights. The first project focuses on data-driven UX design and customer engagement, guiding you through an experimentation and real-life case study. The second project involves a comprehensive analysis of customer behavior, sales, segmentation, and optimization in a superstore setting. These projects are designed not only to enhance your understanding but also to provide practical experience that you can showcase on your resume.
Here is a list of all the sections in this course:
Introduction
Python for Data Science and Analytics
Data Exploration and Preprocessing
Filtering, Sorting, Grouping
Descriptive Statistics
Merging & Joins
Data Visualization in Python
AB Test Crash Course - Theory
Project 1 - Data Analytics and Data Science Project
Experimental vs. Control Set up
Data Analytics in A/B Testing
Parameters for A/B Testing
Analyzing A/B Test Data
Statistical Outputs Explained
Concluding A/B Test Results and Case Study
Project 2 - Superstore Data Analytics Project
Superstore Customer Segmentation
Revenue by Customer Segment
Customers Sales Insights
Exploring Customer Loyalty at Superstores
Superstore Shipping Strategies
Geographic Market Analysis
Product Performance Insights
Comprehensive Sales Analysis
Tracking Sales Trends
Visualizing Sales by State
Check out the full course on the freeCodeCamp.org YouTube channel (5.5-hour watch).