Google Advanced Data Analytics Professional Certificate

The Google Advanced Data Analytics Professional Certificate is designed to build advanced data analytics capabilities, focusing on practical skills for analyzing large datasets, deriving insights, and presenting results. The program includes seven comprehensive courses.

Course Overview

1. Foundations of Data Science
   Explore the roles and industries that utilize advanced data analytics. Learn about data privacy, ethics, and project planning essentials.

2. Get Started with Python
   Learn Python programming basics, including syntax, data structures, and control statements. Gain hands-on experience with Jupyter Notebook for data analysis.

3. Go Beyond the Numbers: Translate Data into Insights
   Apply exploratory data analysis (EDA) techniques and data cleaning methods. Create data visualizations using Tableau to present actionable insights.

4. The Power of Statistics
   Conduct statistical analyses using probability distributions and hypothesis testing. Utilize Python for advanced data modeling and insights.

5. Regression Analysis: Simplify Complex Data Relationships
   Investigate and apply regression models to understand complex data relationships. Perform linear and logistic regression with Python.

6. The Nuts and Bolts of Machine Learning
   Learn about different types of machine learning and model building. Prepare data and evaluate both supervised and unsupervised learning models.

7. Google Advanced Data Analytics Capstone
   Build and assess machine learning models to identify patterns and trends. Create comprehensive data visualizations and explore career resources.

Skills Acquired

• Python programming
• Machine learning
• Predictive modeling
• Data cleaning and transformation
• Data visualization (Matplotlib, Seaborn, Tableau)
• Statistical analysis

Projects

• Data Cleaning & Transformation: Prepares raw data for analysis.
• Exploratory Data Analysis (EDA): Uncovers insights using Python.
• Predictive Modeling: Develops machine learning models.
• Data Visualization: Creates interactive dashboards and visualizations.

Tools & Libraries

• Python (Pandas, NumPy, Scikit-learn)
• Matplotlib, Seaborn, Tableau
• Jupyter Notebook