MIS 2749 Data Analytics and Data Ecosystems

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SQL (Structured Query Language) is

A popular programming language used to communicate with databases. Allows the user to filter specific data and to track correlated pieces of data.

Query

A question or request for specific information contained in a database. Example: A SQL statement.

Syntax

A set of rules and guidelines that define a specific computer language. For instance, SQL statements must use proper case, spacing, and punctuation.

Using SQL

Characters, Fields, Records Characters: letters, numbers, symbols Fields: a set of data values made up of characters. Also called data attributes. Commonly found in columns. Examples: first name, last name, ID number, address, email, phone. Records: Collections of related fields. Often structured in rows. Example: The specific record of a student that contains their name, ID number, etc. Tables: Groups of assigned rows and columns that contain related data. Examples: student contact information in one table, student class enrollment in another table, etc.

Third-Party Data

Data collected by an organization that often does not have a relationship with the organization collecting the data or the data being collected. Example: A third party might be tasked with collecting information on visitors to a social media site of an organization.

3 Different Types of Data Analytics

Descriptive, Predictive, and Prescriptive.

Skills and Characteristics of Data Analysts

Observation, Research, Interpretation of a problem. Technical writing skills. Experience with computer code including SQL, Python, and Oracle. Strong analytical and problem-solving skills. Experience with data visualization software including Tableau and Power BI. Microsoft Excel and spreadsheet experience. Effective time management and the ability to multitask and to meet deadlines. Oral communication and presentation software skills.

Classification of Data Formats

Primary vs. Secondary Internal vs. External Continuous vs. Discrete Qualitative vs. Quantitative Nominal vs. Ordinal Structured vs. Unstructured

Components of a SQL Statement

SELECT - Allows the user to choose the precise fields to be returned. FROM - Allows the user to choose the tables where the fields needed for the query are located. WHERE - Allows the user to filter for desired and specific information.

Example of a SQL Statement

SELECT first_name FROM customer_information.customer_name WHERE first_name = 'david' SELECT identifies the field to extract data from; in this case it is the first_name field. FROM indicates the table in which the field is located, and WHERE helps to narrow the query so that the only customers with the first name of 'David' are returned.

Prescriptive Analytics

Seeks to predict what, when, and why a given scenario might occur. Used to determine the best course of action. Considered to be the most advanced form of data analytics. Example: monitoring flu strains and activity to determine possible outbreak areas.

Trends in Data Analytics

Smarter, scalable artificial intelligence (AI). Composable (?) data and analytics. Data fabric as a foundation. Data analytics as a core business function.

Data Openness

The free access, distribution, and usage of data. To be considered open, the data must meet these criteria: The public must have access and datasets must be available for use. Datasets must have access rights that allow them to be reused and redistributed. Datasets must be universally available so that anyone can use the data. Example of Open Data: data collected during the Covid-19 pandemic was openly shared and used by governments, health institutions, and municipalities across the globe.

6 Steps to Create Data-Driven Decisions

(1) Ask - Determine what questions you need to ask and establish a clear definition of the problem (2) Prepare - Data should be collected, stored, and secured in preparation for data processing (3) Process - Data cleansing and checking should occur to ensure high-quality data is ready for analysis (4) Analysis - Data is analyzed to find patterns, trends, and relationships within the data set (5) Share - Data is shared with the appropriate audience. This can include visualization and presentations to key stakeholders (6) Act - Once data and results have been shared, decisions must be made.

5 Aspects of Analytical Skills

(1) Curiosity - involves investigation into learning more about a problem or change of a desired state as well as knowing the right questions to ask to uncover issues. (2) Understanding context - involves understanding where data and information fit into the plan and approach. (3) Having a technical mindset - involves the ability to break down processes and information into smaller digestible/analytical steps. (4) Data design - involves the ability to conceptualize of how data and information should be organized. (5) Data strategy - involves the ability to analyze the people, processes, hardware, and software used in data analysis.

5 Steps of Data Project Life Cycle

(1) Sensing - Identifying the meaningful data that should be collected by the organization. (2) Collecting - Gathering the data from determined and validated data sources. (3) Wrangling - Converting the raw data into a user-friendly format. (4) Analysis - Examining and analyzing the data. (5) Storage - Storing the data (involves all the hardware, software, and procedures for securing, maintaining, and accessing the data).

US Bureau of Labor Stats for Data Analysts

Entry-level education bachelor's degree in: Management information systems. Computer science. Mathematics or statistics. Economics or finance. Median salary: Across different disciplines is $88,770. Job outlook: 15% growth (much faster than average).

Where Data Analysts are Employed

Finance, e-commerce, healthcare, government, science.

Predictive Analytics

Focuses on understanding, predicting, and planning for future events and business outcomes. Uses probability analysis, data mining, statistical modeling, machine learning, and deep learning to generate possible future outcomes.

Key Considerations for Selecting the Right Data

How will the data be collected? Where will you get your data? What types of data do you need to solve your problem? How much data should be collected? How much time do you have to collect and analyze the data?

Uses of Data Analytics in Business.

Improved and effective decision making. Improved customer service. Increased efficiency of production and operation processes. Improved patient experiences and internal procedures in healthcare.

Personally Identifiable Information (PII)

Name, social security number, medical records, email addresses, account numbers, phone numbers, IP addresses.

3 Key Tools for Data Analysts

Spreadsheets (Excel); Databases and query languages MYSQL, Oracle SQL); Data visualization tools (Tableau, Microsoft Power BI, Looker).

Data Visualization

The graphical and structured representation of data. Makes it easier to see results and conclusions from data analysis.

Data Ecosystem

The information technology (IT) architecture and infrastructure, software applications, programming languages, and storage technologies used for the collection, storage, analysis, and interpretation of meaningful data.

Data Transformation

The process of converting the format of data from one form to another. Done to make data easier to analyze; to increase its usability; to change its format, structure, or value; to make it compatible with another system; to merge data. Examples: copying data, deletion of database fields, standardization of names, joining of tables or databases, converting a file to a different format.

Data Analytics

The process of investigating raw data uncover trends and correlations and to answer specifically crafted questions.

Data Anonymization

The process of protecting people's private or sensitive data by eliminating identifying information. "personally identifiable information (PII)"

Data-Driven Decision Making (DDDM)

The use of facts, metrics, and data to guide strategic business decisions that align with organizational goals, objectives, and initiatives. (from Tableau)

Descriptive Analytics

Uncovers historical trends in data sets. Deals with what happened or what is occurring. Examples: return on investment (ROI) and summaries of past events such as sales, operational efficiency, impact of marketing efforts

Questions Data Analysts Can Ask

What is the overall outcome and results that are needed? Who is the receiver of the information and analysis? What is the question that is being asked? Am I answering the question? What are the time constraints for the project? How quickly must a decision be made or information produced?

Common Duties of a Data Analyst

Working on data analytics teams to extract data from large data sets. Creating reports that outline key findings. Monitoring KPIs to identify success or failure. Analyzing data to identify trends. ALSO: Collaboration with executives and other stakeholders to uncover areas for improvement. Data visualization to aid in the interpretation of data. Structuring large data sets to ensure data is accessible and usable. Creating reports and presentations for management and executives that outline key findings and recommendations.


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