DBA-DAT7707 - Google Advanced Data Analytics Certificate
Course Description
The Google Advanced Data Analytics Certificate is a rigorous, hands-on program designed for learners who want to move beyond entry-level analytics into advanced data science techniques. This program emphasizes statistical analysis, regression modeling, machine learning foundations, and the translation of complex data into actionable insights using industry-standard tools such as Python, pandas, NumPy, scikit-learn, and data visualization libraries.
Learners progress through a structured curriculum that mirrors real-world data analytics workflows—from foundational data science concepts and statistical reasoning to predictive modeling, machine learning, and a comprehensive capstone project. The program prepares participants for data analytics-focused IT roles across industries.
Course Outline
Foundations of Data Science
- Data science lifecycle and workflows
- Data types, structures, and sources
- Data ethics, bias, and responsible analytics
- Introduction to Python for data analysis
- Working with datasets using pandas and NumPy
Go Beyond the Numbers – Translate Data into Insights
- Exploratory data analysis (EDA)
- Data cleaning and preparation techniques
- Data storytelling and stakeholder communication
- Visualization best practices using Python libraries
- Translating analytical findings into business decisions
The Power of Statistics
- Descriptive and inferential statistics
- Probability concepts and distributions
- Sampling methods and hypothesis testing
- Confidence intervals and statistical significance
- Applying statistics to real-world datasets
Regression Analysis – Simplify Complex Data Relationships
- Simple and multiple linear regression
- Model assumptions and diagnostics
- Feature selection and multicollinearity
- Interpreting regression outputs
- Evaluating model performance
The Nuts and Bolts of Machine Learning
- Supervised vs. unsupervised learning
- Classification and clustering techniques
- Model training, validation, and testing
- Overfitting, underfitting, and model tuning
- Introduction to scikit-learn workflows
AI for Data Analysis
- Role of AI in modern analytics
- Automating analysis and insight generation
- Responsible and ethical AI considerations
- Using AI tools to enhance productivity
- Practical applications in analytics projects
Google Advanced Data Analytics Capstone
- End-to-end data analytics project
- Problem definition and stakeholder requirements
- Data acquisition, analysis, and modeling
- Presentation of findings and recommendations
- Portfolio-ready deliverables
Accelerate Your Job Search with AI
- Positioning analytics skills for the job market
- Resume and portfolio development
- Interview preparation for data roles
- Using AI tools to support career advancement
- Navigating analytics career pathways
Learner Outcomes
Upon successful completion of this program, learners will be able to:
- Apply the data science lifecycle to solve complex analytical problems
- Perform exploratory data analysis and data cleaning using Python
- Use statistical methods to draw valid conclusions from data
- Build and interpret regression models to analyze relationships
- Apply foundational machine learning techniques to real datasets
- Translate technical findings into clear, actionable insights
- Develop and present a professional-quality data analytics capstone project
Notes
Target Audience:
- Aspiring data analysts and junior data scientists
- IT professionals seeking advanced analytics skills
- Business analysts transitioning into technical analytics roles
- Professionals with prior exposure to data analysis tools
Occupational Outlook (BLS):
According to the U.S. Bureau of Labor Statistics, careers related to data analytics and data science—such as data scientists and operations research analysts—are projected to grow significantly due to increased reliance on data-driven decision-making across industries. Demand is particularly strong in technology, healthcare, finance, and government sectors.
Information Technology Continuing Education Certificate:
Certificates of completion will be awarded to participants who successfully finish all required courses by the final day of the class.
Industry Certification:
This course provides the training to obtain the Google Advanced Data Analytics Certificate.
- Completion of this course automatically grants the certification.
- Learners must successfully complete Google’s required assessments to earn the credential.
Method of instruction:
Video lectures, reading, hands-on labs, writing assignments, and formative assessments.
Evaluation:
Must pass graded quizzes and labs with at least 80% or above, submit written assignments, and participate in learner forums.
Prerequisites
Knowledge:
- Prior completion of an introductory data analytics course (e.g., Google Data Analytics Certificate or equivalent)
- Basic proficiency in Python
- Understanding of statistics fundamentals
Resources
- Access to a computer with internet connectivity
- Google account for learning platform access
- Python development environment (e.g., Jupyter Notebook)