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Introduction

Google Advanced Data Analytics Certificate

Overview

The Google Advanced Data Analytics have seven courses.

Courses

Course 1: Foundations of Data Science​

Courses

  • Describe data science and its functions within an organization
  • Identify tools used by data professionals
  • Articulate the value of data science in organizations
  • Investigate career opportunities for a data professional
  • Explore data professional workflow
  • Develop effective communication skills

Course 2: Get Started with Python​

Courses

  • Define what a programming language is and why Python is used by data scientists
  • Create python scripts to display data and perform operations
  • Manipulate and create strings, lists, dictionaries, and dataframes
  • Import and use Python modules to access powerful functions and methods
  • Demonstrate object-oriented programming using classes and objects

Course 3: Go Beyond the Numbers: Translate Data into Insights​

Courses

  • Explain the process of exploratory data analysis (EDA)
  • Apply Python tools to examine raw data structure and format
  • Use relevant Python libraries for cleaning raw data
  • Apply input validation skills to a dataset using Python
  • Create visualizations using Tableau that tell the story of a dataset

Course 4: The Power of Statistics​

Courses

  • Use descriptive statistics to summarize and explore data
  • Apply basic probability concepts and probability distributions to analyze data
  • Explain the theory, methods, and applications of sampling in inferential statistics
  • Construct and interpret confidence intervals
  • Perform and interpret hypothesis tests

Course 5: Regression Analysis: Simplify Complex Data Relationships​

Courses

  • Understand relationships in datasets based on PACE
  • Practice modeling simple and multiple linear regression
  • Practice modeling binomial logistic regression
  • Identify model assumptions
  • Perform model evaluation and interpretation

Course 6: The Nuts and Bolts of Machine Learning​

Courses

  • Identify characteristics of the different types of machine learning

  • Recognize common IDEs, resources, and libraries

  • Learn how to prepare data for modeling

  • Build and evaluate supervised and unsupervised learning models

    • K-means and other clustering models

    • Different classification techniques such as decision trees, random forests, and gradient boosting

Course 7: Google Advanced Data Analytics Capstone​

Courses

  • Develop a capstone project that applies skills learned from previous courses
  • Examine data to identify patterns, trends, issues, and more
  • Create models using machine learning techniques
  • Compose data visualizations
  • Review career resources