Advanced Data Analytics,Google | Data Analytics
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/
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