The Data Analytics Journey - D204 questions with
verified answers
Boxplot Ans✓✓✓ provides a concise summary of the quartiles of numerical data
(i.e., cut points that divide the data into 25% percentile segments). This graph is
also convenient for detecting outliers and skewness.
Business Understanding/ Discovery phase Ans✓✓✓ An analyst defines the major
questions of interest that need to be answered, determines the needs of the
stakeholders, and assesses the resource constraints of the project. Define project
outcomes.
Tools:
1. Scope Statement
1. Stakeholder Register
2. Gannt Chart
3. Network Diagram
Techniques:
1. Critical Path Method
2. KPI
3. Budget estimation techniques
4. Schedule estimation techniques.
5. SWOT Analysis
Potential Problems
Lack of clear focus on:
· stakeholders,
· timeline,
· limitations, and
,· budget
could potentially derail an analysis
Data Pathway Terms
(4 Phases)
1.Planning:
Define goals.
Organize resources (software, hardware, staff)
Coordinate people
Schedule the project
Data Science
(6 Phases)
1. Find a question
Data Acquisition Ans✓✓✓ Collecting data phase. Data is collected and stored, for
easy retrieval from a database, perhaps a component of a data warehouse, by
using a language like SQL. Web scraping and surveys to are also used to acquire
data. Involves initial cleaning.
Tools:
1. SQL
2. Web Scrapping software
3. Survey
4. Input Data: Self-generated Data
5. NoSQL- Used to collect Unstructured Data.
Techniques:
, 1. ETL
2. API
3. Web Scrapping
Potential Problems
Missing Values may not be detected
Quality: uniqueness, relevance, reliability, validity, and accuracy.
Type of data: structured, unstructured, semi-structure, quantitative, qualitative.
Access: data may be difficult to access.
Data Pathway Terms
2.Wrangling:
· Get Data
Data Science
1. Collect the Data
Data Analytics Techniques Ans✓✓✓ Regression, Classification, Clustering, etc.
Data Analytics Tools and Techniques Ans✓✓✓ Python and R
Data Cleaning phase Ans✓✓✓ Also known as data cleansing, data wrangling,
data munging, and feature engineering. Analyst will use SQL, Python, R, or Excel
to perform data modifications and transformations
Tools:
1. Python
2. R
3. SQL
verified answers
Boxplot Ans✓✓✓ provides a concise summary of the quartiles of numerical data
(i.e., cut points that divide the data into 25% percentile segments). This graph is
also convenient for detecting outliers and skewness.
Business Understanding/ Discovery phase Ans✓✓✓ An analyst defines the major
questions of interest that need to be answered, determines the needs of the
stakeholders, and assesses the resource constraints of the project. Define project
outcomes.
Tools:
1. Scope Statement
1. Stakeholder Register
2. Gannt Chart
3. Network Diagram
Techniques:
1. Critical Path Method
2. KPI
3. Budget estimation techniques
4. Schedule estimation techniques.
5. SWOT Analysis
Potential Problems
Lack of clear focus on:
· stakeholders,
· timeline,
· limitations, and
,· budget
could potentially derail an analysis
Data Pathway Terms
(4 Phases)
1.Planning:
Define goals.
Organize resources (software, hardware, staff)
Coordinate people
Schedule the project
Data Science
(6 Phases)
1. Find a question
Data Acquisition Ans✓✓✓ Collecting data phase. Data is collected and stored, for
easy retrieval from a database, perhaps a component of a data warehouse, by
using a language like SQL. Web scraping and surveys to are also used to acquire
data. Involves initial cleaning.
Tools:
1. SQL
2. Web Scrapping software
3. Survey
4. Input Data: Self-generated Data
5. NoSQL- Used to collect Unstructured Data.
Techniques:
, 1. ETL
2. API
3. Web Scrapping
Potential Problems
Missing Values may not be detected
Quality: uniqueness, relevance, reliability, validity, and accuracy.
Type of data: structured, unstructured, semi-structure, quantitative, qualitative.
Access: data may be difficult to access.
Data Pathway Terms
2.Wrangling:
· Get Data
Data Science
1. Collect the Data
Data Analytics Techniques Ans✓✓✓ Regression, Classification, Clustering, etc.
Data Analytics Tools and Techniques Ans✓✓✓ Python and R
Data Cleaning phase Ans✓✓✓ Also known as data cleansing, data wrangling,
data munging, and feature engineering. Analyst will use SQL, Python, R, or Excel
to perform data modifications and transformations
Tools:
1. Python
2. R
3. SQL
