Google Advanced Data Analytics Professional Certificate
Level up from basic analytics to advanced data science skills using Python, machine learning, and real-world datasets—guided by mentors and powered by Google.
Go Beyond Dashboards — Master Advanced Data Analytics & Machine Learning
Level: Intermediate → Advanced
Prerequisite: Basic data analytics knowledge (Excel, SQL, or Python)
Duration: 12–16 weeks (Cohort-Based)
Format: Live + Guided Projects + Self-Paced Learning
Powered by: Google via Coursera
Certification: Google Advanced Data Analytics Professional Certificate
Start Date: [Insert Next Cohort Date]
Seats: Limited to 20 learners
By the end of this program, you will:
Apply advanced statistical analysis
Build predictive models (machine learning)
Use Python for real-world data science workflows
Perform regression, classification, and model evaluation
Translate complex data into business insights
Python (Pandas, NumPy, Scikit-learn)
Jupyter Notebooks
Data visualization libraries
Machine learning frameworks
Predictive models
Machine learning projects
Advanced analytics reports
Portfolio-ready capstone project
Weeks 1–3: Advanced Data Foundations
Python for advanced analytics
Data wrangling & feature engineering
Exploratory data analysis (EDA)
Weeks 4–7: Statistics & Modeling
Hypothesis testing
Regression models
Data-driven decision-making
Weeks 8–11: Machine Learning
Supervised learning (classification & regression)
Model evaluation & tuning
Intro to real-world ML workflows
Weeks 12–16: Capstone Project
Solve a real-world problem (finance, SME, or public data)
Build and present a predictive model
Create a portfolio-ready data science project
This program prepares you for:
Data Scientist (Entry-Level)
Advanced Data Analyst
Machine Learning Analyst
Analytics Consultant
KES
Apply Now — Limited Seats for Advanced Cohort
Guided Programs. Cohort Training. Enterprise Training
Learn concepts. Build skills. Apply to your Job/ business. Grow sustainably
Step 1: Learn
Access practical, market-relevant training designed for real-world application.
Step 2: Apply
Work on real challenges, case studies, and tools that translate learning into action.
Step 3: Grow
Leverage skills to unlock opportunities—employment, enterprise growth, and partnerships.
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