Data analysis is the process of inspecting, cleansing, transforming and modeling data to discover useful information and support decision-making. It is also known as predictive analytics.
Before beginning any data analysis, you should clean and convert the raw data to make it easier to analyze. This includes removing duplicate records and white spaces that may not be relevant to your analysis.
In an era where split-second decisions and massive information systems are the norm, Data Analytics is essential for businesses. It’s a subfield of data science that analyzes Structured and Unstructured data to extract meaningful insight from it.
The four different types of analysis are descriptive, diagnostic, predictive and prescriptive. These analytical methods vary in complexity, but they all have one thing in common: they help companies make informed and effective decisions.
Categorical data is information that can be grouped into categories such as customer demographics, product categories, or survey responses. Quantitative data is numerical information such as sales figures, production numbers, or survey scores.
Data analytics software is used to perform different types of analyses on data. These include optimization software, phenomenon analysis, and predictive modeling. Optimization software identifies inefficiencies in business processes or systems to increase efficiency. Phenomenon analysis uses the information contained in existing data to discover trends that can be used to predict future events, such as predicting earthquakes using seismic data, discovering hacker intrusions on websites, or finding cures with medical data.
Prescriptive analytics is a more technologically advanced form of data analysis that looks at future scenarios and recommends courses of action to achieve specific goals. It uses a variety of algorithms like machine learning, data visualization and statistical optimization to examine all possible options in a business model and then find the best one that meets the business’s objectives.
This type of analysis is commonly offered by analytics firms to large corporations in a variety of industries, including healthcare and financial services. For example, a bank may use a prescriptive analytics algorithm to scan transactional data and detect suspicious patterns, such as a sudden spike in spending that could indicate theft.
These types of algorithms can help a company gain a competitive advantage by optimizing resources, funds and efforts in order to achieve the highest possible results. They can also help companies avoid costly mistakes by reducing or eliminating risky decisions. They do this by separating relevant information from superfluous or inaccurate data that could have an unfavorable effect on the outcome.
Tactical crime analysis is a specific form of data analysis that helps law enforcement identify and understand criminal offense patterns in their jurisdiction. It can help them plan their strategy, respond to recent crime incidents and arrest offenders. This type of data analysis is often conducted by specialized units or departments within the police force.
Until recently detailed scientific investigations of team tactics in elite soccer have been scarce. This has partly been due to the lack of easily accessible and reliable data for such analyses. However, with the development of advanced tracking technologies this seems to be changing. Detailed game logs of individual player and team data as well as physiological training data are now readily available for research.
Nevertheless the shear volume of this new type of data presents major challenges for research in soccer analytics. Especially with respect to three key concepts: volume, variety and velocity. The present paper will provide an overview of how existing big data technologies from industrial data analytics domains could address these challenges and open up new opportunities to study tactical behavior in elite soccer.
Insights from data analysis are only as good as the quality of the underlying dataset. Garbage data in equals garbage insights out, and this is why it’s critical to clean your data before analyzing it.
Data cleaning or data scrubbing is the process of fixing incorrect, duplicate, misformatted or otherwise erroneous data within a dataset. This can include fixing spelling and syntax errors, removing duplicate data points, resolving inconsistencies between different data sets, ensuring that numerical observations are formatted consistently, and identifying and removing outliers from the dataset.
Many commercial and open source tools can be used to perform data cleansing. These tools can ingest and clean data from a wide variety of formats and can be used to standardize data, remove outliers, match data, and eliminate duplicate records. They can also be used to identify and replace missing values or add null entries based on other information within the dataset.