Google Data Analytics Professional Certificate

Foundational Data Analytics Concepts

Understanding the Data Landscape

• Grasping the core definition of data analytics and its role in business operations.
• Distinguishing between quantitative and qualitative data and their respective applications.
• Identifying structured and unstructured data formats and methods for handling each.
• Understanding the complete data analysis process from problem framing to action.
• Recognizing the importance of data ethics, privacy, and integrity in all analytical work.

Strategic Thinking for Data Analysis

• Formulating clear, solvable business problems that data can address.
• Defining specific analytical questions that guide data collection and investigation.
• Identifying key stakeholders and understanding their needs for data insights.
• Translating business objectives into measurable data metrics and KPIs.

Data Querying and Manipulation with SQL

Core SQL Commands and Syntax

• Writing SQL queries to retrieve specific data from single or multiple tables using SELECT, FROM, and WHERE clauses.
• Filtering data effectively using comparison operators, logical operators (AND, OR, NOT), and pattern matching (LIKE).
• Ordering query results based on single or multiple columns in ascending or descending order using ORDER BY.
• Performing data aggregation using functions such as COUNT, SUM, AVG, MIN, and MAX to summarize datasets.

Advanced SQL Techniques

• Grouping data based on common values using GROUP BY and filtering those groups with HAVING clauses.
• Combining data from different tables using various JOIN types: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
• Writing nested queries (subqueries) to solve complex data retrieval problems where one query depends on the result of another.
• Utilizing common table expressions (CTEs) to simplify complex queries and improve readability.
• Applying window functions to perform calculations across a set of table rows that are related to the current row.

Spreadsheet-Based Data Analysis

Essential Spreadsheet Functions

• Employing mathematical functions (SUM, AVERAGE, PRODUCT) for basic calculations.
• Applying logical functions (IF, AND, OR, NOT) to create conditional logic within spreadsheets.
• Using text functions (LEN, LEFT, RIGHT, MID, CONCATENATE, FIND, REPLACE) for cleaning and manipulating text data.
• Implementing lookup functions (VLOOKUP, HLOOKUP, INDEX-MATCH) to retrieve specific data points from large datasets based on a common identifier.

Data Organization and Manipulation in Spreadsheets

• Sorting data efficiently based on single or multiple criteria.
• Applying filters to display specific subsets of data.
• Creating and analyzing pivot tables to summarize, analyze, explore, and present large datasets.
• Developing pivot charts to visually represent insights derived from pivot tables.
• Utilizing data validation rules to ensure data entry consistency and accuracy.
• Applying conditional formatting to highlight specific data patterns or anomalies visually.

Data Cleaning and Organization

Identifying and Addressing Data Issues

• Recognizing common data quality issues such as duplicates, missing values, inconsistencies, and incorrect data types.
• Developing strategies for handling missing data, including imputation, deletion, or flagging.
• Implementing techniques to identify and remove duplicate entries within datasets.
• Standardizing inconsistent data formats, such as dates, currencies, and text entries, for uniformity.

Techniques for Data Transformation

• Transforming data types to ensure compatibility for analysis (e.g., converting text to numbers).
• Splitting and combining data columns to prepare data for specific analytical needs.
• Renaming columns and reorganizing data structures for clarity and ease of use.
• Applying data validation and cleansing routines to maintain high data quality throughout the analysis pipeline.

Data Visualization and Storytelling

Principles of Effective Data Visualization

• Understanding the principles of visual perception and how they apply to data representation.
• Choosing the most appropriate chart types for different data types and analytical goals (e.g., bar charts for comparisons, line charts for trends, scatter plots for relationships).
• Designing visualizations that are clear, concise, and convey insights without distortion.
• Applying best practices for color theory, typography, and layout in data visualization.

Creating Impactful Visualizations and Dashboards

• Constructing interactive dashboards that allow users to explore data dynamically.
• Utilizing visualization tools to create various chart types, including pie charts, area charts, and geographic maps.
• Annotating visualizations to highlight key findings and provide context.
• Developing compelling narratives around data insights to effectively communicate findings to diverse audiences.
• Ensuring visualizations are accessible and interpretable by all viewers.

Programming for Data Analysis with R

Foundational R Programming

• Navigating the RStudio integrated development environment and managing R projects.
• Understanding basic R syntax, data types (numeric, character, logical, factor), and variables.
• Working with fundamental R data structures: vectors, lists, matrices, and data frames.
• Importing and exporting data from various sources into R, such as CSV files and spreadsheets.

Data Manipulation and Visualization in R

• Performing data cleaning and transformation tasks using the `dplyr` package, including selecting, filtering, arranging, mutating, and summarizing data.
• Combining and reshaping data frames using functions like `bind_rows`, `bind_cols`, and `pivot_longer`/`pivot_wider`.
• Creating static and interactive data visualizations using the `ggplot2` package, including scatter plots, histograms, box plots, and bar charts.
• Customizing plot aesthetics, adding layers, and creating multi-panel plots for complex visualizations.
• Writing simple R functions to automate repetitive data analysis tasks.

Statistical Foundations for Data Analysis

Descriptive Statistics

• Calculating and interpreting measures of central tendency: mean, median, and mode.
• Determining and understanding measures of spread: range, variance, and standard deviation.
• Analyzing data distributions, including understanding skewness and kurtosis.
• Identifying outliers and understanding their impact on data analysis.

Inferential Statistics and Sampling

• Differentiating between correlation and causation and understanding their implications in analysis.
• Understanding basic sampling techniques to gather representative data from a larger population.
• Grasping the fundamental concepts of hypothesis testing, including null and alternative hypotheses.
• Interpreting p-values and confidence intervals in the context of statistical significance.
• Applying statistical thinking to draw valid conclusions from data and support decision-making.