Google-Advanced-Data-Analytics

Learn in-demand skills like statistical analysis, Python, regression models, and machine learning in less than 6 months.

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

Skills you'll gain

• Data Science
• Regression Models
• Predictive Modelling
• Exploratory Data Analysis (EDA)
• Statistical Analysis
• Machine Learning
• Python Programming
• Data Analysis
• Jupyter Notebook
• Tableau Software
• Data Visualization (DataViz)
• Kaggle

Foundations of Data Science ✅

Course 1 • 22 hours

• Understand common careers and industries that use advanced data analytics
• Investigate the impact data analysis can have on decision-making
• Explain how data professionals preserve data privacy and ethics
• Develop a project plan considering roles and responsibilities of team members

Get Started with Python ✅

Course 2 • 28 hours

• Understand common careers and industries that use advanced data analytics
• Investigate the impact data analysis can have on decision-making
• Explain how data professionals preserve data privacy and ethics
• Develop a project plan considering roles and responsibilities of team members

Go Beyond the Numbers: Translate Data into Insights ✅

Course 3 • 26 hours

• Apply the exploratory data analysis (EDA) process
• Explore the benefits of structuring and cleaning data
• Investigate raw data using Python
• Create data visualizations using Tableau

The Power of Statistics 🕜

Course 4 • 30 hours •

• Explore and summarize a dataset
• Use probability distributions to model data
• Conduct a hypothesis test to identify insights about data
• Perform statistical analyses using Python

Regression Analysis: Simplify Complex Data Relationships

Course 5 • 24 hours •

• Investigate relationships in datasets
• Identify regression model assumptions
• Perform linear and logistic regression using Python
• Practice model evaluation and interpretation

The Nuts and Bolts of Machine Learning

Course 6 • 29 hours •

• Identify characteristics of the different types of machine learning
• Prepare data for machine learning models
• Build and evaluate supervised and unsupervised learning models using Python
• Demonstrate proper model and metric selection for a machine learning algorithm

Google Advanced Data Analytics Capstone

Course 7 • 8 hours •

• Examine data to identify patterns and trends
• Build models using machine learning techniques
• Create data visualizations
• Explore career resources