Intro - Discover Data Analysis

Question 1

Problem: why do businesses need Data Analysts?

Answer 1

• Businesses still fail to treat data as a strategic asset and struggle to use their data in a productive way

Question 2

How can businesses get the most out of their data?

Answer 2

• Unlock data through accurate storytelling

Question 3

How can it be used to drive competitive advantage?

Answer 3

• Enables a business to make faster decisions on more precise information

Question 4

What is Data Analysis? How does this work?

Answer 4

• Processing - Data analysis is the process of identifying, cleaning, transforming, and modelling data to discover meaningful and useful information.
• Selling Story - The data is then crafted into a story through reports for analysis to support the critical decision-making process.

Question 5

What is descriptive analytics?

Answer 5

• Describes and summarises historical data

Question 6

Diagnostic analytics? What is the process?

Answer 6

• Diagnostic analytics answer questions about why events happened
• Diagnostic techniques supplement findings from descriptive statistics to uncover the cause of events (e.g. why these events became better or worse)
  (1) Identify anomaly
  (2) Collect data related to anomaly
  (3) Use statistical techniques to discover relationships in these patterns

Question 7

Predictive analytics?

Answer 7

• Predictive analytics techniques use historical data to identify trends and determine if they are likely to occur again in the future
• Usually one outcome
• Includes statistical and machine learning techniques

Question 8

Prescriptive analytics?

Answer 8

• Prescriptive analytics help answer questions about which actions should be taken to achieve a goal or target.
• Analyses past data to estimate the likelihood of different outcomes (multiple outcomes)
• Uses machine learning techniques

Question 9

Cognitive analytics?

Answer 9

• Similar to random forest that learn from past data to improve decisions (e.g. weak learners make strong learners)

Question 10

Why are there so many roles in data?

Answer 10

• Used to be roles with large remits such as Business Analyst and BI developers
• Large multi-stage projects and the expansion of Big Data and IoT has led to more specialised data roles

Question 11

Business analyst?

Answer 11

• Business Analysts are closer to business objectives and are specialists in interpreting data

Question 12

Data analyst

Answer 12

• A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools such as Microsoft Power BI
• Responsibilities - designing and building scalable and effective data models, enabling and implementing the advanced analytics capabilities into reports for analysis, and managing Power BI assets.

Question 13

Data engineer

Answer 13

• Provides Database: Data engineers provision and set up data platform technologies that are on-premises and in the cloud
• Manage data types - Data engineers also manage the flow of structured and unstructured data from multiple sources
• Wrangling - Data engineers process and wrangle large amounts of data so data scientists and data analysts can focus producing insights

Question 14

Data scientist

Answer 14

• Data scientists perform advanced analytics to extract value from data
• Can use descriptive (e.g. EDA) and predictive analytics (e.g. machine learning)

Question 15

Database administrator

Answer 15

• Operates Database - A database administrator implements and manages the operational aspects of cloud-native and hybrid data platform solutions that are built on Microsoft Azure data services and Microsoft SQL Server.
• Monitors - A database administrator monitors and manages the overall health of a database and the hardware that it resides on

Question 16

Prepare?

Answer 16

• Ultimate goal = to ensure data is accurate
• Data preparation is the process of profiling, cleaning, and transforming your data to get it ready to model and visualize.
• Data Integrity - correcting inaccurate data, identify missingness, improving readability
• Also involves (a) performance implications of connecting data (b) security assurances

Question 17

Model?

Answer 17

• Ultimate goal = create relationships between tables
• Data modelling is the process of determining how your tables are related to each other by defining and creating relationships between the tables.
• Good models help: (a) reports more accurate (b) deliver faster insights (c) create better reports (d) faster performance

Question 18

Visualize?

Answer 18

• Ultimate goal = to solve business problems through data storytelling

Question 19

Analyse?

Answer 19

• Ultimate goal - create insights (summary + synthesis) from the report to drive more meaningful insights
• Analytical capabilities - Power BI has analytical capabilities to identify trends, patterns and predict outcomes

Question 20

Manage?

Answer 20

• Ultimate goal - overseeing the sharing and distribution of Power BI assets
• Responsibilities = collaboration (e.g. knowledge sharing) , security (e.g. no leaks), reduce silos (e.g. many datasets brought into Power BI)

Question 21

Explain the dependencies between tasks?

Answer 21

• Modelling depends on Prepare to be in a proper state