Unit 5: Data Analytics — Short Questions

Data analytics is the process of examining data to find patterns, draw conclusions and support decision-making. It helps turn raw data into useful insights.

It supports informed decisions and improves performance using data insights. It helps businesses identify opportunities and solve problems effectively.

Mean, median and mode represent the center of a dataset. They help summarize large datasets with a single value.

Mean is the average of all numbers in a dataset. It is calculated by dividing the sum of values by the total number of values.

Arrange data in order and pick the middle value. If there's an even count, take the average of the two middle values.

Mode is the most frequently occurring value in a dataset. It can have more than one value if multiple numbers repeat equally.

Yes, if multiple values appear with equal highest frequency. This is known as bimodal or multimodal distribution.

Variance shows how spread out the data points are from the mean. A higher variance indicates greater variability in the data.

High variance means the data points are far from the mean. It suggests less consistency in the dataset.

Standard deviation is the square root of variance. It tells us how much the data varies from the average.

Because it's expressed in the same unit as the original data. Variance is in squared units, making it harder to interpret.

√(Variance) or sqrt(sum of squared deviations divided by N). It gives a practical measure of data spread.

Probability measures how likely an event is to occur. It ranges from 0 (impossible) to 1 (certain).

There's a 70% chance of rain tomorrow. This helps people decide whether to carry an umbrella.

Probability = Favorable outcomes / Total possible outcomes. It helps quantify uncertainty in real-life situations.

The probability is 1/2 or 50%. There are only two equally likely outcomes: heads or tails.

Meteorologists use probability to predict the likelihood of weather events. For example, 80% chance of thunderstorms.

It is gathering information from various sources for analysis. Accurate data collection ensures reliable results.

Surveys, observations and experiments. Each method has its own strengths and suitable applications.

To study behavior without asking people directly. Example: Tracking customer movement in a store.

To gather opinions, preferences, or feedback from people. Surveys are easy to distribute and analyze.

An experiment tests cause-and-effect relationships. One variable is changed to see its effect on another.

It involves organizing and converting raw data into usable formats. Proper preparation ensures accurate analysis.

It fixes or removes errors and inconsistencies in data. Clean data leads to better insights and decisions.

Clean data ensures accurate and reliable analysis results. Unclean data can lead to misleading conclusions.

Missing values, duplicates, incorrect formatting and typos. These issues must be addressed before analysis.

Imputation replaces missing values with estimated ones. Common methods include using the mean, median, or similar data.

It uses math models to analyze data and make predictions. Models help uncover patterns and forecast future outcomes.

Define problem, collect data, choose model, train, evaluate. These steps ensure a structured approach to modeling.

It predicts numerical outcomes based on input variables. Example: Predicting house prices based on size and location.

It predicts binary outcomes like yes/no or pass/fail. Example: Whether a student will pass or fail based on study hours.

Clustering groups similar items together based on their features. It helps identify natural groupings in the data.

K-means divides data into K groups based on similarity. It's a simple and widely used clustering algorithm.

You must choose the number of clusters (K) first. Choosing the right K affects the quality of results.

Grouping students based on academic performance for targeted teaching. Teachers can tailor strategies to each group.

It segments customers with similar behaviors for marketing. Businesses can personalize offers to each segment.

To represent data visually and help identify patterns, trends and insights.

To compare different categories by showing their values as bars.

The distribution of data by grouping values into intervals or bins.

They allow users to enter data and create charts like bar charts or line graphs for easier interpretation.