Data Analytics Fundamentals

Summarizes and describes key features of data. It provides insights about the past, such as averages, trends, and distributions.

Identifies the root cause of an event by investigating relationships between various data points. It aims to answer "why" a particular outcome occurred.

Forecasts future events by analyzing patterns in historical data and applying machine learning techniques. It uses past data to predict what might happen next.

Offers data-driven recommendations for actions to take based on past patterns and predictions. It goes beyond predicting and suggests the best course of action.

Descriptive analysis summarizes data features, while diagnostic analysis focuses on finding the cause of events by exploring relationships between data points.

Machine learning techniques are used to find patterns in data for predictive analysis. In prescriptive analysis, they help identify potential actions and their outcomes.

Data analysis helps industries across sectors to make informed decisions, optimize processes, and gain valuable insights from data.

Data analysis combines information technology to process data, statistics to interpret results, and domain knowledge to understand the context of the data.

Software used to process and analyze large datasets, enabling tasks like log aggregation, visualization, and security analysis.

Using data collected from players' interactions in games to improve the gaming experience, detect issues, and enhance gameplay.

Analyzing data about user interactions on websites, such as clicks, pages visited, and time spent on each page.

The process of collecting and centralizing logs from multiple sources, like web servers, into a single location for analysis.

Representing data graphically using charts, graphs, and dashboards to identify trends, patterns, and outliers.

Using data about user preferences and past behavior to generate personalized recommendations for products or services.

Analyzing data generated by interconnected devices, such as smart home appliances or wearable fitness trackers.

Examining security logs to detect suspicious activities, identify threats, and improve security posture.

Analyzing data from system logs, which record events and actions within computer systems, to monitor performance and identify security threats.

An AWS service that logs API activity made to an AWS account, providing visibility into user actions and system events.

Practical applications of data analysis across various industries, such as gaming, social media, e-commerce, and IoT, to improve services, solve problems, and gain insights.

An AWS storage service used to store various data types, including CloudTrail logs.

The usefulness of data analysis in transforming raw data into valuable information that can be used to solve problems, improve processes, and enhance customer experiences.

Visual representations of security data, displaying key metrics and alerts to help identify and respond to security incidents.

The importance of data analysis in today's world, where data is abundant and crucial for making informed decisions and staying competitive.

Cloud services offered by providers like AWS that are pre-configured and ready to use, streamlining big data operations.

The ability to understand, interpret, and communicate data effectively.

A variable that can be measured numerically, representing a measurable characteristic like speed, sales, or rainfall.

The statistical relationship between two variables, indicating how closely they are related.

A statistical technique used to predict the value of one variable based on the value of another.

Cloud computing offers cost-efficiency, scalability, and flexibility for data analytics by providing access to on-demand infrastructure and managed services.

A statistical measure that describes the strength and direction (positive or negative) of a linear relationship between two quantitative variables.

When two variables move in the same direction. As one variable increases, the other also increases.

When two variables move in opposite directions. As one variable increases, the other decreases.

How closely related two variables are. A stronger correlation implies a stronger relationship between the variables.

A set of four datasets with identical summary statistics but drastically different visualizations, demonstrating the importance of visual representation.

A straight line relationship between two variables, where one variable increases or decreases at a constant rate relative to the other.

A data point that deviates significantly from other data points in the data set.

Visualizations provide a richer understanding of data trends, revealing relationships that might be obscured by statistical tests alone.

Linear regression predicts Y values from X values using the best-fitting straight line on a scatter plot. It shows the direction and strength of the relationship between two numeric variables.

Correlation only shows the direction and strength of the relationship between two variables, while linear regression predicts Y values from X values using a best-fit line.

The regression line is the best-fitting straight line through the points on a scatter plot. It minimizes the distance between each point to the line.

The regression line helps us predict Y values for known X values. This means we can estimate the outcome of one variable based on the value of another.

The dependent variable is the variable we want to predict (Y), and it is usually plotted on the y-axis.

The independent variable is the variable we use to make predictions (X), and it is usually plotted on the x-axis.

For meaningful linear regression, we need quantitative variables, a linear relationship between the variables, and to be aware of any outliers that could distort the results.

Linear regression can be used to predict a house's price based on its square footage, to predict a student's grade based on the time spent studying, or to predict sales based on advertising spending.