A Beginner's Guide to Data & Analytics - Highlights

The 22-page free ebook "A Beginner's Guide to Data & Analytics" from Harvard Business School Online offers essential definitions and practical advice for those looking to learn more about the field.

* Data science is the process of building, cleaning, and structuring datasets to analyze and extract meaning. 

* Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends

* Types of Analytics:

1. Descriptive analytics looks at data to examine, understand, and describe something that’s already happened.
2. Diagnostic analytics goes deeper than descriptive analytics by seeking to understand the “why” behind what happened.
3. Predictive analytics relies on historical data, past trends, and assumptions to answer questions about what will happen in the future.
4. Prescriptive analytics identifies specific actions an individual or organization should take to reach future targets or goals

* Data literacy is the ability to read, understand, and utilize data in different ways. Being data-literate can help non-data professionals read and understand data, and use it to inform their decision-making.

* Data ecosystem refers to the programming languages, packages, algorithms, cloud-computing services, and general infrastructure an organization uses to collect, store, analyze, and leverage data.

* Data life cycle describes the path data takes from when it’s first generated to when it’s interpreted into actionable insights. This life cycle can be split into eight steps: 

1. Generation
2. Collection
3. Processing 
4. Storage
5. Management
6. Analysis
7. Visualization
8. Interpretation

* Data privacy, also known as information privacy, is a subcategory of data protection that encompasses the ethical and legal obligation to protect access to personally identifiable information (PII), which is any information that can be linked to a specific individual.

* Data integrity is the accuracy, completeness, and quality of data as it’s maintained over time and across formats.

* Data wrangling is the process of cleaning raw data in preparation for analysis. It involves identifying and resolving mistakes, filling in missing data, and organizing and transferring it into an easily understandable format.

* Data Visualization is the process of transforming raw data into compelling visuals that tell a story.

* Data visualization tools:

1. Microsoft Excel
2. Power BI
3. Google Charts
4. Tableau
5. Zoho Analytics
6. Data Wrapper
7. Infogram

* Machine learning refers to the use of computer algorithms that automatically learn from and adapt in response to data. 

* Every analysis should be a feedback loop that deepens your learning.

* Questions to ask yourself when handling data:

• What am I hoping to understand?

• What do I need to know to make a certain business decision?

• What story is this data telling?

• What do the relationships between variables mean for ____ at my company?

• What if ____ changed? Which variables, trends, or forecasts would be impacted?

• What needs to change in the data to get the desired outcome?

• Why does the data trend in this direction, and what does it mean for the future?

• How can I further analyze the data to get the answers needed to make important decisions?

* Understanding data frameworks like the Data-Driven Decision-Making Framework gives you the capability to take a raw dataset, interpret its story, and use it to answer relevant business questions. 

Also see - Data Literacy Foundations course by Maven Analytics