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Data analysis Flashcards

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1
Q

What is the main learning objective of data analysis?

A

To explain how data can be analysed to provide business intelligence.

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2
Q

What is Knowledge Discovery in Databases (KDD)?

A

A process of analysing data to gather knowledge, consisting of five basic stages.

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3
Q

List the five basic stages of Knowledge Discovery in Databases.

A
  • Selection of the data
  • Pre-processing of the data
  • Transformation of the dataset
  • Data mining to find patterns
  • Evaluation of the findings
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4
Q

What is the purpose of the selection stage in data analysis?

A

To determine specific questions the business wants to answer.

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5
Q

What is noise in a dataset?

A

Corrupted or unwanted data within a dataset.

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6
Q

What are outliers in data analysis?

A

Data points that fall significantly outside the range of other data.

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7
Q

True or False: Outliers should always be removed from a dataset.

A

False.

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8
Q

What is the role of finance professionals in the data cleaning process?

A
  • Correcting faulty data
  • Reviewing suggested outliers
  • Collecting missing data
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9
Q

What is the transformation stage in data analysis?

A

The process of preparing the data for analysis.

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10
Q

What is sampling in data analysis?

A

Choosing a representative sample to analyse instead of the whole population.

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11
Q

What does aggregation in data analysis involve?

A

Combining several features together to summarise data.

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12
Q

What does ETL stand for in data warehousing?

A

Extraction, Transformation, Loading.

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13
Q

What is the difference between ETL and ELT?

A
  • ETL: Data is transformed before loading into the warehouse.
  • ELT: Data is loaded in its raw form and transformed later.
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14
Q

What is the significance of artificial intelligence (AI) in data analytics?

A

AI allows for the processing of large volumes of data quickly and can automate decision-making.

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15
Q

List some key capabilities of AI that are transforming data analytics.

A
  • Object recognition
  • Natural language processing
  • Human-AI interaction
  • Machine learning
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16
Q

What is machine learning?

A

The ability of AI algorithms to learn and improve their analytical skills over time.

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17
Q

Name the three main types of machine learning used in data analytics.

A
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
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18
Q

What does supervised learning involve?

A

Training the algorithm to recognize key features of the data.

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19
Q

Fill in the blank: The pre-processing stage is designed to _______.

A

[clean data to ensure it is of good quality]

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20
Q

What is the challenge of using aggregation in data analysis?

A

It may lead to false or over-generalised conclusions.

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21
Q

What is the primary requirement for machine learning to learn effectively?

A

Large volumes of disaggregated and diverse data

Disaggregated data means that it is broken down as far as possible, and diverse data is gathered from a wide variety of sources.

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22
Q

What are the two important uses of supervised learning in data analytics?

A
  • Classification
  • Regression
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23
Q

What is classification in the context of machine learning?

A

The process of separating data within a dataset into different categories.

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24
Q

What type of classification uses a yes/no split?

A

Binary classification

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25
What is multi-class classification?
Data can be split between multiple categories, with each item fitting into only one.
26
What is multi-label classification?
Items may fit into multiple categories at the same time.
27
What is the first step in the supervised learning process?
Labelling the dataset.
28
What is the purpose of the training step in supervised learning?
To allow the machine to identify features of the data and differentiate between categories.
29
What does the learning step in supervised learning involve?
Refining rules based on feedback from further analysis.
30
What is cross-validation in machine learning?
An iterative process where the algorithm improves its accuracy through repeated training.
31
Define precision in the context of classification accuracy.
The proportion of items identified as correctly belonging to a specific class.
32
Define recall in the context of classification accuracy.
The proportion of items belonging to a specific class that were correctly classified.
33
What are the two potential problems in supervised machine learning?
* Overfitting * Underfitting
34
What does overfitting describe?
The algorithm produces rules that are too specific to the training dataset, hindering generalization.
35
What causes underfitting in machine learning?
Insufficient training time for the algorithm to understand relationships between items.
36
What is unsupervised learning?
A technique where the algorithm searches the data for new patterns without pre-labeled data.
37
What is cluster analysis in unsupervised learning?
A technique where the machine groups data items based on shared qualities.
38
What is anomaly detection?
An unsupervised learning technique used to identify unusual data within a dataset.
39
What is association rules mining?
A process that uses logic to infer possible structures, patterns, or relationships from data.
40
What is reinforcement learning?
A type of machine learning where algorithms improve through rewards based on performance.
41
How does reinforcement learning operate?
The algorithm is rewarded for achieving objectives set by a human programmer.
42
What key roles are involved in the data analytics process?
* Data scientists * Data analysts * Business analysts * Finance professionals
43
What is the role of a data scientist?
Involves mining large datasets, cleaning data, developing processes, and designing algorithms.
44
What does a data analyst primarily focus on?
Using data to answer questions and help businesses make decisions.
45
What is the function of a business analyst?
Links business stakeholders with data analysts and helps define goals for data projects.
46
What role do finance professionals play in the data analytics process?
They exercise skepticism, calculate costs and benefits, and help align data outcomes with business objectives.
47
What must finance professionals ensure regarding data analysis?
That the right questions are being asked to generate useful information.
48
What should management consider when setting tasks for AI?
The parameters for available paths the AI might take to achieve its goals.
49
What must a finance professional recognize in data analytics?
The costs and benefits of investing in data analytics ## Footnote This ensures that data analytics is used to increase business value.
50
What is a common issue with management's enthusiasm for technology?
Over-enthusiasm can lead to collecting data with no real use or analyzing data for insights that cannot be acted upon ## Footnote This often occurs in the field of data analytics.
51
What should a finance professional understand to assist ICT specialists?
Factors affecting business performance and appropriate data sources ## Footnote This knowledge helps in providing necessary details for analysis.
52
What tools can finance professionals use to answer management's questions?
Standard accounting tools ## Footnote Examples include breakdowns of sales figures or forecasts for production costs.
53
What ethical considerations should finance professionals ensure during data analytics?
Data protection and elimination of bias ## Footnote A strong grounding in ethics is crucial for this role.
54
What role does the finance professional play in reviewing conclusions?
Applying professional judgement and scepticism to ensure conclusions are appropriate and realistic ## Footnote This involves critical evaluation of findings.
55
What is a vital finance role in data analytics?
Interpreting and explaining findings to decision makers in a clear, non-technical way ## Footnote This helps align findings with key performance indicators, budgets, or forecasts.
56
What is essential for effective collaboration in data analytics projects?
A range of specialists and stakeholders sharing their in-depth knowledge ## Footnote The finance professional's communication skills are key to this process.
57
In the scenario, who is the role player?
Akito Mori ## Footnote This character receives scene-setting information about the company's activities.
58
What support is available to the role player in the scenario?
A virtual mentor named Bee ## Footnote Bee provides help similar to a colleague in a real workplace.
59
What stages are involved in preparing data for analysis?
Cleaning data to ensure high quality and transforming it for suitability ## Footnote Stages may vary depending on storage methods like ETL for data warehouses and ELT for data lakes.
60
What unique capabilities of AI are discussed in data analytics?
Object recognition and natural language processing ## Footnote These capabilities provide new insights for businesses.
61
What forms of machine learning are covered in data analytics?
Supervised, unsupervised, and reinforcement learning ## Footnote Each form can be used to derive insights for businesses.
62
What is the next focus after explaining data collection and analysis?
How data is used to inform and support business decision making ## Footnote This will be covered in the next course in the competency area.
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