Analytics Experience Quiz

17. What are some examples of data visualization? -

1. Bar Chart: A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. 2. Pie Chart: A pie chart is a circular statistical graphic, which is divided into slices to illustrate numerical proportions. 3. Line Chart: A line chart or line graph is a type of chart that displays information as a series of data points called 'markers' connected by straight line segments. 4. Scatter Plot: A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. 5. Histogram: A histogram is an approximate representation of the distribution of numerical data. 6. Box Plot: A box plot is a method for graphically depicting groups of numerical data through their quartiles.

4. What are the levels of programming languages?

1. Low Level: Closer to machine language. Mainly used for system programming. 2. High Level: Closer to natural languages. Mainly used for application programming and data analysis.

18. What information can we get from a box plot?

A box plot can show us the following information: 1. Minimum: The minimum value of the data. 2. First Quartile: The value below which 25% of data points are found in the data. 3. Median: The median of the data. 4. Third Quartile: The value below which 75% of data points are found in the data. 5. Maximum: The maximum value of the data. 6. Outliers: The outliers in the data.

11. What is exploratory analytics?

An exploratory data analysis (EDA) looks at data sets to summarize their main characteristics, finding patterns, trends, correlations, outliers, and anomalies, often using visual methods.

15. What are some of the solutions to big data challenges?

Based on the task, there are various solutions to big data challenges. Some of the solutions are: ◦ Data Warehouse: A data warehouse is a database that is designed for query and analysis rather than for transaction processing. ◦ Data Lake: A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. ◦ Data Mart: A data mart is a subset of data from a data warehouse that is organized around a specific subject or business process. ◦ Cloud Computing: Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user.

14. What is big data and what are the big data challenges?

Big data is a term used to describe a large volume of data that is difficult to process using traditional database and software techniques. Big data challenges: ◦ Volume: The amount of data is too large to be processed using traditional database and software techniques. ◦ Velocity: The data is generated at a high rate. ◦ Variety: The data is in different formats and sources. ◦ Veracity: The data is not accurate and trustworthy. ◦ Value: The data is not useful.

7. What is central tendency?

Central tendency is a measure of the center or typical value of a probability distribution. The most common measures of central tendency are the mean, median, and mode.

23. What is classification in machine learning?

Classification is a supervised machine learning task where the algorithm learns from labeled training data to create a model that can predict the class of new data points. For example, given a set of newspaper articles, the algorithm can learn to predict whether a new article is about sports, politics, or business.

25. What is clustering in machine learning?

Clustering is an unsupervised machine learning task where the algorithm learns from unlabeled training data to create a model that can group data points into clusters. For example, given a set of blood pressure measurements, the algorithm can learn to group people into clusters based on their blood pressure.

9. What is correlation?

Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. The most common measure of correlation is the Pearson correlation coefficient.

3. What are the elements of data science?

Data science is a combination of computer science, mathematics, statistics, and domain knowledge.

5. What are the data structures?

Data structures are a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

6. What is descriptive analytics?

Descriptive analytics is the process of analyzing data to summarize and describe the data in a meaningful way, to find patterns, and to identify trends.

19. What are the Gestalt principles of visual perception?

Gestalt principles of visual perception are a set of principles that describe how humans perceive and organize visual elements into meaningful patterns. 1. Proximity: Objects that are close to each other tend to be perceived as part of the same group. 2. Similarity: Objects that are similar to each other tend to be perceived as part of the same group. 3. Continuity: Objects are perceived as smooth, continuous patterns rather than discontinuous ones. 4. Connectedness: Objects that are connected tend to be perceived as part of the same group. 5. Common Fate: Objects that move in the same direction tend to be perceived as part of the same group.

20. What is machine learning?

Machine learning is a a subset of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.

10. What are outliers and how can we identify them?

Outliers are data points that are distant from other observations. Outliers can be identified by finding Q1 (the first quartile), Q3 (the third quartile), and IQR (the interquartile range), which is the difference between Q3 and Q1. An outlier is a data point that is either below Q1 1.5 * IQR or above Q3 + 1.5 * IQR.

12. What is predictive analytics?

Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

13. What is prescriptive analytics?

Prescriptive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the best course of action to achieve a desired outcome.

24. What is regression in machine learning?

Regression is a supervised machine learning task where the algorithm learns from labeled training data to create a model that can predict a continuous value. For example, given a set of blood pressure measurements, the algorithm can learn to predict a person's blood pressure given their age and weight.

21. What is supervised learning?

Supervised learning is a machine learning task where the algorithm learns from labeled training data. Examples of supervised learning are classification and regression.

22. What is unsupervised learning?

Unsupervised learning is a machine learning task where the algorithm learns from unlabeled training data. Examples of unsupervised learning are clustering and dimension reduction.

8. What is variability? -

Variability is a measure of the spread of a probability distribution or data set. The most common measures of variability are range, variance, and standard deviation.

16. What are two important visualization considerations?

Visualization is a way to communicate information clearly and effectively through graphical means. 1. clearly: The visualization should be clear and easy to understand. 2. effectively: The visualization should be effective in communicating the information.

2. What is the data analysis pipeline?

is a process of collecting, cleaning, transforming, and modeling data to discover useful information such as patterns and trends for business decision-making.

How data analysis and data-driven business analysis are different? -

◦ Data analysis is the process of collecting, cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and make decisions based on it. ◦ With data-driven business analysis, businesses are able to gain insight into their operations and make better decisions.