Introduction to Data and Data Analytics

This set of vocabulary flashcards covers the fundamental definitions of data, the four types of data analytics, and the seven steps of the data analytics process as outlined in the lecture.

Data

Any facts or statistics which can be collected.

Quantitative

Data that is described in numbers.

Qualitative

Data that is described in words.

Data analytics

The process of examining raw data to draw conclusions, identify patterns, and support better decision-making.

Descriptive Analytics

A type of analytics used to find the answer to the question "What has happened?" using historical data, such as aiming for sales growth of $10\%$ every quarter.

Diagnostic Analytics

Analytics that goes beyond descriptive analytics to explore the reasons why something happened by examining diverse datasets for patterns and correlations.

Predictive Analytics

The use of past data, machine learning techniques, and statistical algorithms to make future projections and find trends.

Prescriptive Analytics

Analytics that uses information from predictive analytics to make recommendations for practice and counteractions.

Step 1: Define the Problem

The first step which involves defining the problem to be solved, setting a clear objective, and identifying the data needed.

Step 2: Data Collection

The process of organizing and collecting data from existing sources, surveys, interviews, market research, or observations.

Step 3: Data Cleaning

The process of correcting errors in data and removing duplicates and inconsistencies.

Step 4: Data Analysis

Using statistical or mathematical techniques and software like R, Python, and Excel to discover patterns, relationships, or trends.

Step 5: Interpreting and Visualizing the Data

The step focused on understanding what the data tells us and creating easy-to-understand visual representations.

Step 6: Data Stroytealling

Communicating findings and insights to stakeholders in a form they can understand, often avoiding technical jargon.

Step 7: Measuring Effectiveness and Improvement

The final step where a solution's effectiveness is validated against expectations, potentially involving a root cause analysis.