Introduction to Data Science and Excel Data Management

Definition of Data Analysis: The process of finding patterns, evaluating cause and effect, and making estimations from data by applying a logic-based approach such as statistical analysis.
Primary Applications of Data Analysis:
- Analyzing large amounts of data, a process also referred to as data mining.
- Combining and correlating large datasets.
- Evaluating the performance of specific processes, including operations, sales, and general business activities.
- Performing benchmarking to compare business operations against baselines or competitors.
- Creating data models derived from separate and distinct data sources for analysis.
- Surfacing trends or specific insights regarding processes and business activities.
- Creating reports and dashboards to showcase findings for further data exploration or analysis.

Definition of Data Science: A field where data scientists work with large structured and unstructured datasets using scientific methods and processes to discover insights to inform critical decisions.
Key Characteristics of Data Science Work:
- Handling datasets from both structured and unstructured databases.
- Utilizing scientific methods and processes to illuminate insights required for critical decision-making.
- Frequent use of powerful computing hardware, deep search algorithms, and machine learning.
- The primary goal is to explain events, experiences, and various phenomena using data.
Commonly Used Data Science Tools:
- Spreadsheets: Applications such as Microsoft Excel or Google Sheets.
- Visualization Software: Platforms like Tableau or Power BI, which provide more advanced data analysis and visualization capabilities than standard spreadsheets.
- R Project for Statistical Computing (R): Used specifically for advanced statistical analysis.
- Python: Employed for taking a programmatic approach to advanced data analysis with large datasets.

Definition of Data Visualization: The act of using visual elements to analyze, find patterns in, and report insights gleaned from data.
Core Functions of Visualization:
- Describes the use of charts, graphs, maps, and other visual assets to analyze data.
- Makes it easier to identify and demonstrate patterns, trends, and correlations.
- Detects insights that might otherwise go undetected in raw data form.
- Essential for understanding complex relationships represented in data, particularly for complex datasets or analysis correlating multiple sources.
Business Intelligence (BI) Context:
- Data visualization is a standard practice for modern BI.
- Its importance has grown as data availability increased through cloud and cloud-based storage solutions.
Processing Efficiency:
- Humans process visual data $60,000$ times more quickly than text or numerical data.
- Humans can comprehend radical differences in data through visualizations much faster.
Applications of Visualization:
- Planning and analysis.
- Real-time alerting based on pre-defined conditions.

Microsoft Excel is considered an excellent tool for visualizing data from spreadsheets due to its robust options:
- Charts: Excel supports various types, such as bar charts and pie charts, to graphically represent data and relationships.
- PivotTables: These are objects that allow users to manipulate data by rotating or turning large amounts of data into summaries that show insights.
- Geospatial Visualizations: These allow users to show geographic data on maps to understand how geographic locations relate to the data.
- Dashboards: Users can combine charts, PivotTables, and geospatial visualizations to correlate and show complex relationships in the data.

Connecting to data sources that contain relevant data for analysis.
Preparing data for analysis, which includes cleaning up headers, columns, null values, and other data artifacts.
Exploring data to identify patterns, trends, and insights.
Performing deep data analysis to answer specific business or research questions.
Creating visualizations (charts and dashboards) for various stakeholders, including peers, managers, executives, clients, and the public.
Creating workbooks and worksheets that team members can use to explore datasets independently.
Using data to tell stories.

Definition of a Table: A dataset composed of contiguous rows and columns that Excel treats as a single, independent object.
Table Management Tools:
- The Create Table Dialog Box: Used to define the range and properties when first establishing a table object.
- The Table Design Contextual Tab: A specific menu in the Excel Ribbon that appears when a table is selected, providing tools for modifying table styles and properties.

Definition of Sorting: The process of reordering worksheet data based on some defined criteria, such as alphabetically or from the highest value to the lowest value.
The Sort Dialog Box Components:
- Defined Sort Levels: Allows for multi-level sorting (e.g., sort by Department, then by Last Name).
- Column/Row Drop-down Menus: Selection of the specific field to be sorted.
- Move Up and Move Down Buttons: Used to reorder the priority of sort levels.
- Sort On Drop-down Menus: Determines if the sort is based on values, cell color, font color, or conditional formatting icons.
- Order Drop-down Menus: Selection of the sort direction (e.g., A to Z, Smallest to Largest).
Definition of Filtering: The process of removing from view any data entries that do not match some specified criteria. This involves "suppressing" rows that do not meet the standards without deleting the underlying data.

Common Filter Operators:
- $=$ : Filters data based on an exact content match.
- $<$ : Filters for numerical or date and time values that are less than the defined criteria.
- $>$ : Filters for numerical or date and time values that are greater than the defined criteria.
- $\le$ : Filters for numerical or date and time values that are less than or equal to the defined criteria.
- $\ge$ : Filters for numerical or date and time values that are greater than or equal to the defined criteria.
- $<>$ : Filters for numerical, textual, or date and time values that are not equal to the defined criteria.
Wildcard Characters:
- $?$ : Serves as a wildcard for a single character in the same position.
- $*$ : Serves as a wildcard for multiple characters in the same position.

Reflective Question 1: Who might do data analysis and visualization in your organization?
Reflective Question 2: How might you use sorting and filtering for data analysis?