Data science skills are increasingly in-demand. We just published a hands-on data science with Python course on the 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 YouTube channel (5.5-hour watch).