Full Course - Data Science - Data Science for Absolute Beginners! Full Course: 6 Hours

Data Science full tutorial course for absolute beginners. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics. Course created by Barton Poulson from datalab.cc. Creative Commons Attribution license (reuse allowed) Contents Part 1: Data Science: An Introduction: Foundations of Data Science - Welcome (1.1) - Demand for Data Science (2.1) - The Data Science Venn Diagram (2.2) - The Data Science Pathway (2.3) - Roles in Data Science (2.4) - Teams in Data Science (2.5) - Big Data (3.1) - Coding (3.2) - Statistics (3.3) - Business Intelligence (3.4) - Do No Harm (4.1) - Methods Overview (5.1) - Sourcing Overview (5.2) - Coding Overview (5.3) - Math Overview (5.4) - Statistics Overview (5.5) - Machine Learning Overview (5.6) - Interpretability (6.1) - Actionable Insights (6.2) - Presentation Graphics (6.3) - Reproducible Research (6.4) - Next Steps (7.1) Part 2: Data Sourcing: Foundations of Data Science (1:39:46) - Welcome (1.1) - Metrics (2.1) - Accuracy (2.2) - Social Context of Measurement (2.3) - Existing Data (3.1) - APIs (3.2) - Scraping (3.3) - New Data (4.1) - Interviews (4.2) - Surveys (4.3) - Card Sorting (4.4) - Lab Experiments (4.5) - A/B Testing (4.6) - Next Steps (5.1) Part 3: Coding (2:32:42) - Welcome (1.1) - Spreadsheets (2.1) - Tableau Public (2.2) - SPSS (2.3) - JASP (2.4) - Other Software (2.5) - HTML (3.1) - XML (3.2) - JSON (3.3) - R (4.1) - Python (4.2) - SQL (4.3) - C, C++, & Java (4.4) - Bash (4.5) - Regex (5.1) - Next Steps (6.1) Part 4: Mathematics (4:01:09) - Welcome (1.1) - Elementary Algebra (2.1) - Linear Algebra (2.2) - Systems of Linear Equations (2.3) - Calculus (2.4) - Calculus & Optimization (2.5) - Big O (3.1) - Probability (3.2) Part 5: Statistics (4:44:03) - Welcome (1.1) - Exploration Overview (2.1) - Exploratory Graphics (2.2) - Exploratory Statistics (2.3) - Descriptive Statistics (2.4) - Inferential Statistics (3.1) - Hypothesis Testing (3.2) - Estimation (3.3) - Estimators (4.1) - Measures of Fit (4.2) - Feature Selection (4.3) - Problems in Modeling (4.4) - Model Validation (4.5) - DIY (4.6) - Next Step (5.1) Credit ❤️ This course was taught by Barton Poulson from datalab.cc YT channel: https://www.youtube.com/user/datalabcc Source website: http://datalab.cc/ License: Creative Commons Attribution license (reuse allowed) Join us! 🚀 Like our FB Page: https://www.facebook.com/learnscientific/ Website: https://scientificprogramming.io/