Certificate of Completion: Link
The Google Advanced Data Analytics Professional Certificate is a comprehensive program designed to equip learners with advanced data analytics skills essential for today's data-driven industries. Consisting of seven courses, the program covers a wide range of topics including Python programming, exploratory data analysis, statistics, regression analysis, and hypothesis testing. Through practical, hands-on projects and assessments, learners gain proficiency in using tools such as Jupyter Notebook, Python, and Tableau.
Within this repository are the annotated notebooks and the capstone project I have undertaken for the Google Advanced Data Analytics Professional Certificate program. Organised into separate folders, each directory encompasses the coursework for individual courses alongside the certificate granted upon fulfilment of the program requirements. A summary of the program courses is as follows:
- Foundations of Data Science - The "Foundations of Data Science" course provides a comprehensive overview of data professionals' roles and their contributions to organisational goals. It covers data science roles, communication skills, and ethical considerations. Additionally, the course explores the impact of data analysis on decision-making, data privacy, and project planning for real-world scenarios.
- Get Started with Python - The "Get Started with Python" course offers a thorough introduction to the Python programming language, emphasising its relevance to data analysis. Learners explore fundamental Python concepts such as variables, data types, functions, conditional statements, loops, and data structures. The course encourages hands-on practice with Jupyter Notebooks and covers essential coding skills necessary for data analysis tasks.
- Go Beyond the Numbers: Translate Data into Insights - The "Go Beyond the Numbers: Translate Data into Insights" course dives into advanced data analysis techniques, emphasising exploratory data analysis (EDA). It covers best practices for data cleaning and visualisation, utilising Python for structuring and cleaning data, and Tableau for visualisation. Learners also master sharing insights effectively with stakeholders through both written and visual communication.
- The Power of Statistics - The "The Power of Statistics" course explores descriptive and inferential statistics, probability distributions, sampling, confidence intervals, and hypothesis testing. It covers essential statistical concepts and their applications in data analysis. The course emphasises practical skills in conducting statistical analyses using Python and interpreting results effectively.
- Regression Analysis: Simplify Complex Data Relationships - The "Regression Analysis: Simplify Complex Data Relationships" course focuses on modelling variable relationships using linear and logistic regression. It covers interpreting coefficients and handling more advanced statistical concepts like ANOVA analysis and chi-square tests. The course includes practical applications and discussions on how to communicate results effectively.
- The Nuts and Bolts of Machine Learning - The "The Nuts and Bolts of Machine Learning" course delves into the fundamentals of machine learning, covering key concepts such as supervised and unsupervised learning, model evaluation, feature engineering, and model selection. Through practical exercises and projects, students gain hands-on experience in implementing machine learning algorithms using Python and popular libraries like scikit-learn. The course focuses on building a solid foundation in machine learning techniques and understanding their applications in real-world scenarios.
- Google Advanced Data Analytics Capstone - The Google Advanced Data Analytics Capstone course is the final segment of the certification program, consolidating knowledge from preceding courses. Learners undertake a capstone project applying skills acquired throughout the program to tackle real-world data challenges. With guidance from experienced Google professionals, participants develop data-driven insights, refine their analytical techniques, and strengthen their ability to communicate findings effectively. By completing this course, students showcase their proficiency in advanced data analytics, demonstrating their readiness to tackle complex data problems in professional settings.