Information Systems and Data Analytics 2 (INFO2006)
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Summary: Information Systems and Data Analytics 2 (INFO2006) - Data Analytics
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Course
Information Systems and Data Analytics 2 (INFO2006)
Institution
University Of The Witwatersrand (wits)
This quick summary contains everything you need to know for Data Analytics for the INFO2006 course. These 4 pages condense the lecturer's notes as well as her explanations from lectures. Perfect for a cram session the night before an exam.
Information Systems and Data Analytics 2 (INFO2006)
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Understanding The Concept of Data Analytics
1. What is Data Analytics?
The data analytics process has some components that can help a variety of initiatives.
By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.
2. Types of Data Analytics
There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics.
Each has a different goal and a different place in the data analysis process.
These are also the primary data analytics applications in business.
Descriptive Analytics Predictive Analytics
Helps answer questions about what happened. Helps answer questions about what will happen in the future.
Summarises large datasets to describe outcomes to stakeholders. Uses historical data - identify trends and determine if they are likely to
By developing key performance indicators (KPIs,) these strategies can recur.
help track successes or failures. Provides valuable insight into what may happen in the future and
This process requires the collection of relevant data, processing of the includes a variety of statistical and machine learning techniques, such
data, data analysis and data visualisation. as: neural networks, decision trees, and regression.
This process provides essential insight into past performance.
Prescriptive Analytics
Diagnostic Analytics Helps answer questions about what should be done.
Helps answer questions about why things happened. By using insights from predictive analytics, data-driven decisions can be
They take the findings from descriptive analytics and dig deeper to find made.
the cause. Allows businesses to make informed decisions in the face of
The performance indicators are further investigated to discover why uncertainty.
they got better or worse. Relies on machine learning strategies that can find patterns in large
This occurs in three steps: datasets.
o Identify anomalies in the data - These may be unexpected By analysing past decisions and events, the likelihood of different
changes in a metric or a particular market. outcomes can be estimated.
o Data that is related to these anomalies is collected.
o Statistical techniques are used to find relationships and
trends that explain these anomalies.
These types of data analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination they provide a well-rounded understanding of a
company’s needs and opportunities.
3. What is the Role of Data Analytics?
The primary goal - increase efficiency and improve performance by discovering patterns in data.
The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation.
The importance and balance of these steps depend on the data being used and the goal of the analysis.
Data Mining
An essential process. Data Management or Data Warehousing
Involves extracting data from unstructured data sources. Another key aspect.
The key steps in this process are to extract, transform, and load data Involves designing and implementing databases that allow easy access
(often called ETL.) to the results of data mining. This step generally involves creating and
These steps convert raw data into a useful and manageable format. managing SQL databases.
This prepares data for storage and analysis.
Data mining is the most time-intensive step in the data analysis
pipeline.
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