Understanding the concept of data analytics: Flashcards
Types of data analytics:
- descriptive
- diagnostic
- predictive
- prescriptive
Role of data analytics:
- Increase efficiency and improve performance by discovering patterns in data.
- data mining, data management, data presentation are important tasks for data analysts.
Why is data analytics important?
Analysing big data can optimise efficiency in many different industries.
Improving performance enables businesses to succeed in an increasingly competitive world.
Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.
Data Analytic Techniques
regression analysis
monte carlo simulation
factor analysis
cohort analysis
cluster analysis
time series analysis
Data analysis process:
- defining the question
- collecting the data
- cleaning the data
- analysing the data
- visualising and sharing your findings
Best tools for data anlysis:
- Microsoft
- Python
- R
- Jupyter Notebook
- Apache Spark
- SAS
- Microsoft Power BI
Top challenges in implementing data analytics:
Collecting meaningful data
Selecting the right tool
Consolidate data from multiple sources
Quality of data collected
Building a data culture among employees
Data security
Data visualisation
Ethical frameworks to consider for data analytics:
Regardless of what regulations exist (or eventually arise) to mitigate unfair algorithmic behavior, data scientists are on the front lines of ethical thinking regarding data analytics
Methods of data loading:
Cloud-based
Batch processing
Open source
Data Transformation:
Data transformation is the process of changing the format, structure, or values of data.
Benefits of data transformation:
Transformed data may be easier for both humans and computers to use.
Properly formatted and validated data improves data quality and protects applications from potential landmines
Data transformation facilitates compatibility between applications, systems, and types of data.
Challenges of data transformation:
Data transformation can be expensive.
Data transformation processes can be resource intensive.
Lack of expertise and carelessness can introduce problems during transformation.
Enterprises can perform transformations that don’t suit their needs.
How to transfer data:
Data transformation can increase the efficiency of analytic and business processes and enable better data-driven decision-making. The first phase of data transformations should include things like data type conversion and flattening of hierarchical data.
Data modeling:
Data modelling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows.
Why is data modelling done?
Data modelling is a core data management discipline. By providing a visual representation of data sets and their business context, it helps pinpoint information needs for different business processes..
Data modelling can also help establish common data definitions and internal data standards, often in connection with data governance programs. In addition, it plays a big role in data architecture processes.
