Learn in-demand skills like statistical analysis, Python, regression models, and machine learning in less than 6 months with [ Google Advanced Data Analytics Professional Certificate Course ]

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

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