Data Analytics Methods and Techniques
- (FitzRandolph Gate, Princeton University - Kimberly Chen)
- Overview
Big data is being obtained from multiple sources at an astonishing rate, volume and variety. To extract meaningful value from big data, you need the best processing power, analytics and skills.
In most business use cases, any single source of data by itself is useless. The real value often comes from combining these streams of big data sources with each other and analyzing them to generate new insights. Organizations that can quickly extract insights from and leverage data gain an advantage.
Analyzing big data sets, so-called big data, will be a key foundation for competition, underpinning a new wave of productivity growth, innovation and consumer surplus. Leaders in every industry must grapple with the impact of big data, not just a handful of data-oriented managers.
For the foreseeable future, the volume and detail of information captured by businesses continues to increase, and the rise of multimedia, social media, and the Internet of Things will drive exponential growth in data.
As more companies adopt big data analytics, more techniques will be developed to provide more accurate forecasts. It's like a chain where one factor affects another, if all factors just add up and work together to help the market, big data analytics will only grow and create more changes.
While big data is poised to grow, it is still somewhat of a raw, unstructured field. Of course, it has helped a lot of companies and it has helped the market, but there is still a need to understand how to use big data analytics more effectively
- Data Analytics Methods and Techniques
Data analytics is a tool that can help businesses understand their data and make more informed decisions.
Here are some data analytics methods and techniques:
- Predictive analytics: Uses machine learning, statistical models, and data mining to predict future events or trends.
- Statistical analysis: Involves collecting and analyzing data samples.
- Diagnostic analysis: Uses techniques like data discovery, correlations, data mining, and drill-down to understand data connections and identify patterns.
- Descriptive analytics: Uses simple mathematical operations to perform statements about measurements and samples. It's one of the most common ways companies use analytics to stay updated on trends and operational performance.
- Prescriptive analytics: Uses data, statistical analysis, and modeling techniques to identify the best step to take in a given situation. It provides recommendations on actions to achieve the desired outcome.
- Text analytics: Also known as natural language processing (NLP) or text mining, it involves analyzing large volumes of text data to identify insights, trends, and patterns that can inform business decisions.
- Data visualization: Transforms information into a visual context to make it easier for the human brain to understand.
- Artificial intelligence and machine learning: Use these techniques to analyze data sets and identify patterns. They can help organizations make better decisions by understanding the behavior of employees, customers, and other stakeholders.
There are two main types of statistical analysis: descriptive and inferential, also known as modeling.
Descriptive statistics can be useful for two things:
- Providing basic information about variables in a dataset
- Highlighting potential relationships between variables
Descriptive statistics provide a summary of data in the form of mean, median, and mode.
[More to come ...]
