Google Advanced Data Analytics is a comprehensive program designed to elevate your data analytics expertise. Through seven modules, you'll delve into foundational data science, Python, statistical analysis, regression modeling, and machine learning. Gain practical experience with Jupyter Notebook, Python, and Tableau, and expand your career opportunities with Google and other leading employers.
The course is tailored for individuals with prior data analytics experience or graduates of the Google Data Analytics Certificate. You'll explore the roles of data professionals, learn to create impactful data visualizations, build regression and machine learning models, and effectively communicate insights to stakeholders.
Upon completion, you can apply for roles like senior data analyst, junior data scientist, and data science analyst. Join the ranks of successful graduates who've reported positive career outcomes within six months, including new jobs, promotions, or raises.
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The Google Advanced Data Analytics course comprises seven modules that cover foundational data science, Python, statistical analysis, regression modeling, machine learning, and a capstone project. Gain practical skills through hands-on projects with Python, Jupyter Notebook, and Tableau, and prepare for in-demand roles in data analytics.
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.
Explain how Python is used by data professionals. Explore basic Python building blocks, including syntax and semantics. Understand loops, control statements, and string manipulation. Use data structures to store and organize data.
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.
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.
Investigate relationships in datasets. Identify regression model assumptions. Perform linear and logistic regression using Python. Practice model evaluation and interpretation.
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.
Examine data to identify patterns and trends. Build models using machine learning techniques. Create data visualizations. Explore career resources.
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