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IBM Data Science Professional Certificate > Tools for Data Science > Flashcards

Tools for Data Science Flashcards

IBM Data Science Professional Certificate (Course 2/10)

1
Q

Do I Visit Beautiful Destinations Most Autumns?

What data science categories does raw data need to pass through before it is deemed useful?

A
  • Data management
  • Data integration and transformation
  • Data visualisation
  • Model building
  • Model deployment
  • Model monitoring and assessment
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2
Q

Do Cats Dance Exquisitely Everywhere?

What tools are used to support the tasks performed in the Data Science Categories?

A
  • Data Asset Management
  • Code Asset Management
  • Development Environments
  • Execution Environments
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3
Q

What is data management?

A

The process of collecting, persisting, and retrieving data securely, efficiently, and cost-effectively.

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4
Q

Where is data collected from?

A

Many sources, including (but not limited to) Twitter, Flipkart, sensors, and the Internet.

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5
Q

Where should you store data so it is available whenever you need it?

A

Store the data in persistent storage

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6
Q

What is data integration and transformation?

A

The process of extracting, transforming, and loading data (ETL)

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7
Q

Give examples of repositories where data is commonly distributed

A
  • Databases
  • Data cubes
  • Flat files
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8
Q

When extracting data through the extraction process, where should you save this extracted data?

A

It is common practice to save extracted data in a central repository like a data warehouse.

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9
Q

What is a data warehouse primarily used for?

A

It is primarily used to collect and store massive amounts of data for data analysis.

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10
Q

What is data transformation?

A

The process of transforming the values, structure, and format of data.

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11
Q

What do you do after extracting data?

A

Transform the data

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12
Q

What happens to data after it has been transformed?

A

It is loaded back to the data warehouse

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13
Q

What is data visualisation?

A

It is the graphical representation of data and information.

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14
Q

What are some ways to visualise data?

A

You can visualise the data through charts, plots, maps, and animations.

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15
Q

Why is data visualisation a good thing?

A

It conveys data more effectively for decision-makers

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16
Q

What happens after data visualisation?

A

Model building

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17
Q

What is model building?

A

It is a step where you train the data and analyse patterns using suitable machine learning algorithms.

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18
Q

What happens after model building?

A

Model deployment

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19
Q

What is model deployment?

A

It is the process of integrating a model into a production environment

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20
Q

In model deployment, how is a machine learning model made available to third-party applications?

A

They are made available via APIs.

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21
Q

What goal do third-party applications help achieve during model deployment?

A

They allow business users to access and interact with data

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22
Q

What is the purpose of model monitoring?

A

To track the performance of deployed models.

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23
Q

Give an example of a tool used during model monitoring

A

Fiddler

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24
Q

What is the purpose of model assessment

A

To check for model accuracy, fairness, and monitor its robustness

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25
Q

What are some common metrics used during model assessment?

A
  • F1 Score
  • True positive rate
  • Sum of squared error
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26
Q

What is a popular Model Monitoring and Assessment tool?

A

IBM Watson Open Scale

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27
Q

What is Code Asset Management?

A

It is a unified view where you manage an inventory of assets.

It is a tool

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28
Q

What do developers use versioning for?

A

To track the changes made to a software project’s code

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29
Q

You use it

Give an example of a Code Asset Management platform

A

GitHub

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30
Q

What is Data Asset Management?

A

It is a platform for organising and managing data collected from different sources

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31
Q

What do DAM platforms typically support?

A

Replication, backup, and access right management for stored data

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32
Q

Do I Eat Tasty Donuts?

What do Development Environments provide a workspace to do?

A

IDEs provide a workspace and tools to develop, implement, execute, test, and deploy source code.

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33
Q

What do execution environments have?

A

They have libraries for code compiling and the system resources to execute and verify code.

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34
Q

Give an example of a fully-integrated visual tool

A

IBM Watson Studio

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35
Q

What are the most widely-used open-source data management tools?

A
  • MySQL
  • PostgreSQL
  • mongoDB (NoSQL)
  • Apache CouchDB (NoSQL)
  • Hadoop (file-based)
  • ceph (cloud-based system)
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36
Q

What is the task of data integration and transformation in the classic data warehousing world?

A

It is for ETL or ELT.

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37
Q

What are the most widely-used data integration and transformation tools?

A
  • Apache Airflow
  • Kubeflow
  • Apache Nifi
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38
Q

What are the most widely-used data visualisation tools?

A
  • PixieDust
  • Hue
  • kibana
  • Apache Superset
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39
Q

What are some popular model deployment tools?

A
  • PredictionIO
  • SELDON
  • mleap
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40
Q

What are some popular model monitoring and assessment tools?

A
  • ModelDB
  • Prometheus
  • Adverserial Robustness 360 Toolbox
  • AI Explainability 360
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41
Q

Name one popular code asset management tool

A

git

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42
Q

What are some popular data asset management tools?

A
  • Apache Atlas
  • Kylo
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43
Q

What is the most popular development environment that data scientists are currently using?

A

Jupyter

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44
Q

What is the next version of Jupyter Notebooks?

A

Jupyter Lab

In the long term, it will replace Jupyter Notebooks

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45
Q

What are some characteristics of RStudio?

A
  • Exclusively runs R and its associated libraries
  • Enables Python development
  • Provides optimal user experience when tightly integrated in the tool
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46
Q

People Enjoy Delicious Red Dates Every Valentine’s

What is RStudio able to unify into one tool?

A
  • Programming
  • Execution
  • Debugging
  • Remote data access
  • Data exploration
  • Visualisation
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47
Q

What are the features of Spyder?

A

It integrates:
* Code
* Documentation
* Visualisation

to a single canvas

Not on par with the functionality of RStudio

Spyder tries to mimic RStudio’s behaviour in order to bring its functionality to the Python world.

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48
Q

What is the key feature of Apache Spark?

A

Linear scalability

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49
Q

What does linear scalability mean?

In the context of Apache Spark

A

It essentially means the servers there are in a cluster, the more the performance

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50
Q

What is the difference between Apache Spark and Flink?

A
  • Spark is a batch processing engine capable of processing huge amounts of data one by one or file by file
  • Flink is a stream processing image with a main focus of processing real-time data streams
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51
Q

What is a commercial tool?

In the context of data science

A

Commercial tools are software applications that are often licensed and used by businesses to perform various tasks related to data science.

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52
Q

What does open-source mean?

A

Open-source refers to a type of software where the source code is made publicly accessible. This means that anyone can view, modify, and distribute the code as they see fit.

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53
Q

Who delivers commercial support to data science tools?

A
  • Software vendors
  • Influential partners
  • Support networks
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54
Q

What do commercial tools do?

A

They support the most common tasks in data science

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55
Q

Name 2 commercial tools for data management

A
  1. Oracle Database
  2. Microsoft SQL Server
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56
Q

Name 2 commercial tools for data integration

A
  1. Informatica Powercenter
  2. IBM Infosphere DataStage
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57
Q

Name 2 commercial tools for model building

A
  1. SPSS Modeler
  2. SAS enterprise miner
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58
Q

Name 2 providers of commercial data asset management tools

A
  1. Informatica
  2. IBM
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59
Q

Name 1 fully-integrated commercial tool that covers the entire data science life cycle

A

IBM Watson Studio

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60
Q

Which 2 cloud-based tools cover the complete development life cycle for all data science, AI, and machine learning tasks?

A
  • Watson Studio
  • Watson OpenScale
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61
Q

In which category of data science tasks will you find an SaaS version of existing open-source and commercial tools?

with some exceptions, of course

A

Data management

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62
Q

What do Informatica Cloud Data Integration and IBM’s Data Refinery have in common?

A

They are both cloud-based commercial data integration tools

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63
Q

What is IBM’s Congos Business Intelligence suite an example of?

A

A cloud-based data visualisation tool

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64
Q

Name a cloud-based tool for model building

A

Watson Machine Learning

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65
Q

What is Amazon SageMaker Model Monitor an example of?

A

A cloud-based tool for monitoring deployed machine learning and deep learning models continuously

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66
Q

How do you decide which programming language to learn?

A

It largely depends on your needs, the problems you are trying to solve, and who you are solving the problem for

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67
Q

What are the popular languages within data science?

A

Python, R, SQL, Scala, Java, C++, and Julia

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68
Q

Which Python scientific computing libraries are commonly used in data science?

A

Pandas, Numpy, SciPy, Matplotlib

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69
Q

How can you use Python for Natural Language Processing (NLP)?

A

By making use of the Natural Language Toolkit

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70
Q

What are the similarities between open source and free software?

A
  • Both are free to use
  • Both commonly refer to the same set of licences
  • Both support collaboration
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71
Q

What are the differences between open source and free software

A

Open source is more business-focused while free software is more focused on a set of values

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72
Q

Who is R for?

A
  • Statisticians, mathematicians, and data engineers
  • People with minimal or no programming experience
  • For learners with a data science career
  • R is popular in academia
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73
Q

What can you use R for?

A

For developing statistical software, graphing, as well as data analysis

74
Q

What makes SQL great?

A
  • Knowing SQL will help you get a job in data science and data engineering
  • It speeds up workflow executions
  • It acts as an interpreter between you and the database
  • It is an ANSI standard
75
Q

How is SQL different from other development languages?

A

It is a non-procedural language

76
Q

What is SQL’s scope?

A

It is limited to querying and managing data

77
Q

What was SQL designed for?

A

Managing data in relational databases

78
Q

What are the most popular languages in data science?

A

Python, R, SQL, Scala, Java, C++, and Julia.

79
Q

What is a substantial benefit of learning SQL?

A

If you learn SQL and use it with one database, you can apply your SQL knowledge with many other databases easily

80
Q

What data science tools are built with Java?

A

Weka, Java-ML, Apache MLlib, and Deeplearning4

81
Q

What popular program is built with Scala?

A

Apache Spark which includes Shark, MLlib, GraphX, and Spark Streaming

82
Q

What data science programs are built with JavaScript?

A

TensorFlow.js and R-js

83
Q

What’s a great application for Julia in data science?

84
Q

What are libraries?

A

Libraries are a collection of functions and methods that allow you to perform many actions without writing the code

85
Q

What are the popular scientific computing libraries in Python?

A
  • Pandas (used for data structures and tools)
  • Numpy (based on arrays and matrices)
86
Q

*

What are data visualisation libraries used for?

A

They are used to communicate with others and explain meaningful results of an analysis

87
Q

What are some popular data visualisation libraries in Python?

A
  • Matplotlib, it is used mostly for plots and graphs
  • Seaborn, popular for its plots (e.g. time series, heat maps, violin plots)
88
Q

What are popular Machine Learning and Deep Learning libraries in Python?

A
  • Scikit-learn (Machine Learning: regression, classification, clustering)
  • Keras (Deep Learning Neural Networks)
89
Q

What are popular Deep Learning libraries in Python?

A
  • TensorFlow (Deep Learning: Production and Deployment)
  • PyTorch (Deep Learning: regression, classification)
90
Q

What is TensorFlow?

A

A low-level framework used in large scale production of deep learning models

91
Q

What does REST API stand for?

A

Representational State Transfer Application Programming Interface

92
Q

What do REST APIs allow you to do?

A
  • They allow you to communicate through the internet
  • They enable you to use resources like storage, data, and artificially intelligent algorithms
93
Q

What are REST APIs used for?

A

They are used to interact with web services, however, there are rules regarding communication, input or request, and output or response when using these web services.

94
Q

What does an API do?

A

It allows communication between two pieces of software

95
Q

What is a data set?

A

A structured collection of data

96
Q

What are the types of data ownership?

A
  • Private data
  • Open data
97
Q

What are characteristics of private data?

A
  • It is confidential
  • It is commercially sensitive
98
Q

What are characteristics of open data?

A
  • It is publically available
    *
99
Q

What has open data contributed to

A

The growth of data science, machine learning, and artificial intelligence

100
Q

When can you find open data?

A
  • An open data portal list from around the world
  • Governmental, intergovernmental, and organisation websites
  • Kaggle
101
Q

What is the CDLA-Sharing Licence for?

A

It grants you permission to use and modify data.
The licence stipulates that if you publish your modified version of the data, you must do so under the same licence terms as the original data.

102
Q

What is the CDLA-Permissive Licence for?

A

This licence also grants you permission to use and modify data. However, you are not required to share changes to the data.

103
Q

What is important about the CDLA-Permissive and CDLA-Restrictive Licences?

A

Neither licence imposes any restrictions on the results you might derive from the data.

104
Q

Why is the Community Data Licence Agreement important?

A

It makes it easier to share open data

105
Q

Why might open data sets not meet enterprise requirements?

A
  • Open data sets may not always be accurate or of high quality. They could contain errors, inconsistencies, or outdated information, which could lead to incorrect insights or decisions if used in an enterprise setting.
  • The data in open data sets might not be relevant to the specific needs of the enterprise. Businesses often require very specific data tailored to their operations, market, and customers.
106
Q

What are open datasets?

A

Datasets that are freely available for anyone to access, use, modify, and share.

107
Q

What are attributes of proprietary datasets?

A
  • They contain data primarily owned and controlled by specific individuals or organizations.
  • This data is limited in distribution because it is sold with a licensing agreement.
  • Some data from private sources cannot be easily disclosed, like public data.
108
Q

What are some examples of proprietary data?

A

National security data, geological, geophysical, and biological data

109
Q

What does the Data Asset eXchange provide?

A

It provides a curated collection of open datasets, both from IBM research and trusted third-party sources.

This data is ready for use in enterprise applications.

110
Q

What do Machine Learning (ML) models do?

A

They identify patterns in data

111
Q

What is model training?

A

The process by which the model learns the data patterns

112
Q

What can a trained model be used for?

A

It can be used to make predictions

113
Q

What are the types of Machine Learning?

A
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
114
Q

What is the most commonly used type of machine learning?

A

Supervised learning

115
Q

What does a supervised learning model do?

A

The model identifies relationships and dependencies between the input data and the correct output

116
Q

What are the types of supervised learning models?

A

Regression and classification

117
Q

What are regression models used for?

A

To predict numeric (or “real”) values

118
Q

Give an example of something you can use a regression model for

A

Predicting the estimated sales prices for homes in an area

119
Q

What are classification models used for?

A

To predict whether some information or data belongs to a category (or “class”)

120
Q

Give an example of something you can use a classification model for

A

A set of emails along with a designation you can classify whether they are to be considered as spam or not

121
Q

What happens in unsupervised learning?

A

The data is unlabeled and the model tries to identify patterns without external help

122
Q

Give an example of an application of unsupervised learning

A

Clustering

123
Q

What is anomaly detection used for?

A

Identifying outliers in a dataset

124
Q

What has reinforcement learning been likened to?

A

It has been likened to the learning process of a human; e.g. learning something through trial and error.

125
Q

How does a reinforcement learning model work?

A

a reinforcement learning model learns the best set of actions to take, given its current environment, to get the most rewards over time.

126
Q

What is deep learning?

A

It is a specialised type of machine learning that loosely emulates the way the human brain solves a wide range of problems, using a general set of models and techniques

127
Q

What are some applications of deep learning?

A
  • Natural language processing
  • Image, audio, and video analysis
  • Time series forecasting
128
Q

What are some deep learning requirements?

A
  • it requires large datasets of labeled data and is compute intensive
  • it requires special purpose hardware
129
Q

Which popular frameworks are used to implement deep learning models?

A
  • TensorFlow
  • PyTorch
  • Keras
130
Q

What are model zoos?

A

They are pre-trained state-of-the-art models from repositories

131
Q

What are the high-level tasks involved in building a model?

A
  • Prepare data
  • Build the model
  • Train the model

This is an iterative process requiring data, expertise, time, and resources

Then you can deploy the model and use the model.

132
Q

What is the Model Asset eXchange (MAX)?

A

It is a free open source repository for ready-to-use and customizable deep learning microservices.

133
Q

How can you use the time to value of a project?

A

By making use of pre-trained models

134
Q

Where are MAX model-serving microservices built and distributed?

A

On GitHub as open source Docker images

135
Q

What is Red Hat OpenShift?

A

It is a Kubernetes platform used to automate deployment, scaling, and management of microservices

136
Q

What is useful about Ml-exchange.org?

A

It has multiple predefined models

137
Q

What does the The Community Data License Agreement (CDLA) facilitate?

A

It facilitates open data sharing by providing clear licensing terms for distribution and use

138
Q

In a sentence, what do machine learning models do?

A

Machine learning models analyse data and identify patterns to make predictions and automate complex tasks

139
Q

What do Python libraries provide the tools for?

A
  • data manipulation,
  • mathematical operations,
  • and simplified machine learning model development
140
Q

What is the best way to represent network data?

A

A graph is often used to represent connections between people on a social networking website

141
Q

What does a tabular data set comprise?

A

It comprises a collection of rows containing columns that store the information

142
Q

Name a popular tabular data format

A

Comma-separated values or “.csv”

143
Q

What are hierarchical or network data structures typically used for?

A

They are used to represent relationships between data

144
Q

What format are hierarchical data structures organised in?

A

They are organised in a tree-like format

145
Q

What format are network structures organised in?

A

This data is organised in a graph

146
Q

Name a popular dataset for data science

A

The Modified National Institute of Standards and Technology. It contains images of handwritten digits and is commonly used to train image processing systems.

147
Q

What did Jupyter Notebooks originate as?

148
Q

What are the key functionalities of a Jupyter Notebook?

A
  • It records data science experiments
  • it allows combining text, code blocks, and code output in a single file
  • it exports the notebook to a PDF or HTML file format
149
Q

What are the key functionalities of JupyterLab?

A
  • It allows access to multiple Jupyter Notebook files, other code, and data files
  • It enables working in an integrated manner
  • It is compatible with several file formats
  • It is an open source
150
Q

What is a kernel?

A

It is a computational engine that executes the code contained in a Notebook file

151
Q

What do Jupyter notebooks represent?

A

They represent code, metadata, contents, and outputs

152
Q

What does Jupyter implement?

A

A two-process model with a kernel and a client

153
Q

What is the Notebook server responsible for?

A

Saving and loading the notebooks

154
Q

What does the kernel execute?

A

The cells of code contained in the notebook

155
Q

What does the Jupyter architecture use to convert files to other formats?

A

The NB convert tool

156
Q

What do computational notebooks do?

A

They combine code, computational output, explanatory text, and multimedia resources in a single document

157
Q

What is JupyterLite

A

It is a lightweight tool built from Jupyterlab components that executes entirely in the browser

158
Q

What is R?

A

R is a statistical programming language

159
Q

What is R used for?

A
  • Data processing and manipulation
  • Statistical inference, data analysis and machine learning
160
Q

Where is R used the most?

A

Academia, healthcare, and the government

161
Q

Why is R a preferred language for some data scientists?

A
  • It is easy to use compared to some other data science tools
  • It is a great tool for visualistion
  • It doesn’t require installing packages for basic data analysis
162
Q

What are some popular R libraries for Data Science?

A
  • dplyr (for data manipulation)
  • stringr (for string manipulation)
  • ggplot (for data visualisation)
  • caret (for machine learning)
163
Q

What are some data visualisation packages in R?

A
  • ggplot (histograms, bar charts, scatterplots)
  • plotly (web-based data visualisations)
  • lattice (complex, multi-variable data sets)
  • leaflet (interactive plots)
164
Q

What does version control do?

A

It allows you to keep track of changes to your documents

165
Q

What is git?

A

It is a distributed version control system

166
Q

What is one of the most common version control systems in the world?

167
Q

What is the SSH protocol?

A

A method for secure remote login from one computer to another

168
Q

What is a repository?

A

The folders of your project that are set up for version control

169
Q

What do repositories do?

A

They store documents (including your source code) and they enable version control

170
Q

What is a fork?

A

A copy of a repository

171
Q

What is a pull request?

A

The process you use to request that someone reviews and approves your changes before they become final.
* They serve as a way of proposing changes to the main branch
* Other team members review the changes and approve the merging to the master branch

172
Q

What is a working directory?

A

A directory on your file system, including its files and subdirectories, that is associated with a git repository

173
Q

What is special about the Git Repository Model?

A
  • It is a distributed version control system
  • It tracks source code
  • It allows collaboration among programmers
  • It enables non-linear workflows
174
Q

What is GitHub

A

The online hosting service for git repositories

175
Q

What is a branch?

A

A snapshot of your repository

176
Q

What is the master branch?

A

The official version of the project

177
Q

What does a child branch do?

A

It creates a copy of the master branch

178
Q

When using git repositories, where are edits and changes made?

A

In the child branch.

In this branch, you can build, make edits, test the changes, and when you are satisfied with them, you can merge them back to the master branch; where you can prepare the model for deployment

179
Q

What is a key benefit of using child branches?

A

Branches allow for simultaneous development and testing by multiple team members

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