Advanced Data AnalyticsGoogle Data Analytics

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=jempH0qPQn8

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

#############################

New on site