Watch Advanced Data Analytics,Google | Data Analytics Video Tutorial

What you'll learn ✅Explore the roles of data professionals within an organization ✅Create data visualizations and apply statistical methods to investigate data ✅Build regression and machine learning models to analyze and interpret data ✅Communicate insights from data analysis to stakeholders ⭐⭐⭐⭐🕑TIME STAMP📋⭐⭐⭐⭐⭐ 👉FOUNDATION OF DATA SCIENCE 0:00:00 Welcome to the Course 0:11:28 Careers in Data Science 0:23:23 Program Plan and Expectations 0:26:44 Review Introduction to Data Science Concepts 0:28:36 Data Driven Careers 0:39:00 Use Data Analytics for Good 0:47:11 Trajectory of the Field 0:50:31 Review the impact of data Today 0:51:25 Data Career Skills 0:59:12 Work in the field 1:07:41 Data Professional Career Resources 1:19:20 Review your Career as a data professional 1:20:51 The Data project workflow 1:28:27 Elements of Communication 1:43:50 communicate like a data professional 1:47:06 Review data application and workflow 1:47:50 Begin Building a portfolio to impress 1:54:01 End of Course Portfolio project wrap up 👉GET STARTED WITH PYTHON 1:56:48 Get Started with the Course 2:10:54 The Power of Python 2:27:22 Use Python Syntax 2:42:22 Review hello python 2:43:55 Functions 3:06:45 Conditional Statements 3:23:28 While Loops 3:35:06 For Loops 3:43:15 Strings 3:58:49 Review Loops and strings 4:00:53 Lists and Tuples 4:21:57 Dictionaries and Sets 4:36:46 Arrays and Vectors with numpy 4:51:27 DataFrames with Pandas 5:26:57 Review data Structures in python 5:28:36 Apply your skills to a workplace scenario 5:36:23 Course review get started with python 👉GO BEYOND THE NUMBERS TRANSLATE DATA INTO INSIGHT 5:38:05 Get Started with the Course 6:00:06 Use Pace to Inform Eda and Data visualizations 6:12:06 Review find and share stories using data 6:15:00 Discovering is the beginning of an investigation 6:40:52 Understand data format 7:00:45 Create structure from raw data 7:22:10 Review explore raw data 7:25:32 The Challenge of missing or Duplicate data 7:52:43 The Ins and outs of data outliers 8:12:15 Change categorical data to numerical data 8:25:59 Input validation 8:41:41 Review clean your data 8:43:56 Present a story 8:58:41 Advanced tableau 9:25:18 Apply your skill to a workplace scenario 9:29:31 End of Course portfolio project wrap up 👉THE POWER OF STATISTICS 9:34:28 Get Started with the Course 9:55:45 Descriptive Statistics 10:16:23 Calculate Statistics with Python 10:28:44 Review introduction to statistics 10:29:52 Basic concepts of probability 10:48:08 Conditional probability 11:05:35 Discrete probability distributions 11:24:22 Continuous probability distributions 11:38:23 Probability distributions with python 11:48:39 Review Probability 11:51:07 Introduction to Sampling 12:16:04 Sampling distributions 12:36:10 Work with sampling distributions in python 12:46:48 Review Sampling 12:49:15 Introduction to confidence intervals 13:06:47 Construct confidence intervals 13:28:24 Review confidence intervals 13:30:55 Hypothesis Testing 13:47:26 One sample tests 13:56:51 Two sample tests 14:13:54 Hypothesis testing with python 14:24:01 Review introduction to hypothesis testing 14:26:02 Apply your skills to a workplace scenario 14:32:15 End of Course portfolio project wrap up 👉REGRESSION ANALYSIS SIMPLIFY COMPLEX DATA RELATIONSHIPS 14:37:17 Get Started with the Course 14:50:07 Linear Regression 15:04:47 Logistic Regression 15:12:04 Review introduction to complex data relationships 15:15:05 Foundations of linear regression 15:41:24 Evaluate a linear regression model 15:51:26 Interpret linear regression results 15:57:56 Review simple linear regression 16:08:24 Model assumptions revisited 16:19:38 Model interpretation 16:43:45 Review multiple linear regression 16:56:26 Analysis of variance 17:25:57 Review Advanced hypothesis testing 17:27:59 Foundations of logistic regression 17:41:15 Interpret logistic regression results 18:00:59 Review logistic regression 18:03:06 Apply your skills to a workplace scenario 👉THE NUTS AND BOLTS OF MACHINE LEARNING 18:14:53 Get Started with the Course 18:29:15 Categorical versus continuous data types and models 18:36:25 Machine Learning in Everyday life 18:43:12 Ethics in Machine Learning 18:50:57 Utilize the python toolbelt for machine learning 19:01:27 Machine learning resources for data professionals 19:09:26 Review the different types of machine learning 19:38:37 Pace in Machine learning the construct and execute stages 19:56:13 Review Workflow for building complex models 19:57:33 Explore unsupervised learning and K-means 20:11:50 Evaluate a K-means model 20:28:50 Review unsupervised learning Techniques 20:29:57 Additional supervised learning techniques 🧾 For Earning the Certificate, Enroll in this Course here®️: https://www.coursera.org/ ✨✨PLEASE IGNORE THESE TAGS✨✨ #dataanalytics data analytics tutorial #advanceddataanalytics, advanced data analysis, advanced data science, advanced analytics