As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.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.
Types of Data Analytics
Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type 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 helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance.
- Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse.
- This generally occurs in three steps: Identify anomalies in the data. These may be unexpected changes in a metric or a particular market. Data that is related to these anomalies is collected.
The Technical Concepts of Data Science
Here are some of the technical concepts you should know about before starting to learn what is data science.1. Machine LearningMachine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics. 2. Modeling Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models. 3. Statistics Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results. 4. Programming Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML. 5. Databases A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.
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