Data Exploration

Question 1

define data exploration

Answer 1

Data Exploration is the process of exploring the data and putting it in a uniform way for a better understanding.

Question 2

big data as a field?

Answer 2

big data is a field that treats ways to analyze, extract information and deal with data sets that are too massive/ complex to be dealth with by traditional data processing software.

Question 3

big data define

Answer 3

big data refers to data that is so large and complex that it is an almost impossible task to be processed by traditional methods.

Question 4

how do people deal with big data

Answer 4

people work with the cloud and networks of endless normal servers and pwerful algorithms. this allows them to process over a million pieces of data within the matter of minutes.

Question 5

name and explain parameters of data

Answer 5

-volume( size of data)-velocity( speed of data accumulation)-variety structured/semi structured/un structured data-veracity- accuraccy and trustworthiness of data-value- rumuneration of the data

Question 6

advantages of data exploration

Answer 6

-gain better understanding and new insights of data-identify the trends, relationships and patterns present in the data.- decide the course of action to take in the AI project cycle.-allow us to visually represent the data- helps in validating or verification of the collected data

Question 7

define data visualisation

Answer 7

data visualisation is the process of representing the data in the form of graphics to gain better understanding of new insights of data.

Question 8

advantages of visualisation

Answer 8

Quickly get a sense of the trends, relationships and patterns contained within the data.• Define strategy for which model to use at a later stage.• Communicate the same to others effectively.

Question 9

steps of visualization

Answer 9

1. Start with understanding the data,2. Scoping the problem and extracting authentic data.3. And present it using different graphs.

Question 10

How to read the graph chart?

Answer 10

1. DESCRIBE: This part explains how the figure/table is set up. like:1) representation on axis. 2)Units of axis. 3)sclae on each axis.4) symbol and legend on a figure. 5) Observe the patterns in the data.2. INTERPRET: It will explain how to interpret the data.As it is important to understand how to draw conclusion from the pattern described.

Question 11

name tools used to represent data

Answer 11

1. MS Excel2. Apple Numbers3.Google Charts4. OnlineChartTool.com5.Python Graph gallery

Question 12

components of a chart

Answer 12

-data series-axes(x and y acxis)-chart area-legend

Question 13

define chart

Answer 13

A chart is a graphical representation of information which helps in presenting relationships and patterns to reach a hypotheses.

Question 14

Differentiate between chart and graph.

Answer 14

Chart It is the representation of data diagrammatically in the form of graphs, shapes etc. It is used to divide data into subsets.Ex : Pie chart, Bar chart etc GraphIt is a way to represent data visually which shows the arithmetic relation between the data.It is used to find patterns and trends in data.Not all graphs are charts. But all graphs are chatsEx : Line graph, Bar graph etc

Question 15

all graphs are charts but not all charts are graphs.

Answer 15

charts is a data visualisation technique used to represent data diagramtically like shapes etc. graphs are a type of chart used to represent the arithmetic relationship between data visually. charts usually aid in dividing data into subsets while graphs are used to identify trends or paterns. so graphs fall under the catgory of the chart making all graphs charts.

Question 16

Why is data normally unstructured in nature? . Explain with an example.

Answer 16

)Data found in nature is usually unstructured because the development in all resources available has substantially increased the amount of data. Due to the large amount of data present, most of it is unstructured. The technology available is still not sophisticated enough to structure data on its own. The most it can do with big data is store it, organizing has to be done manually. For example, satellites capture many images of the earth’s atmosphere to detect rain. The machine on its own cannot figure out whether it will rain or not by analysing the picture.