Analysis of data is a vital part of running a successful business. When data is used effectively, it leads to a better understanding of a business’s previous performance and better decision-making for its future activities. There are many ways that data can be utilized, at all levels of a company’s operations. Companies around the globe generate vast volumes of data daily, in the form of log files, web servers, transactional data, and various customer-related data. In addition to this, social media websites also generate enormous amounts of data. Companies ideally need to use all of their generated data to derive value from it and make impactful business decisions. Data analytics is used to drive this purpose. In this article, readers can take a deep dive into a data analyst course.
Types of data analyst
There are four types of data analysis that are in use across all industries. While separating these into categories, they are all linked together and build upon each other.
- Descriptive analysis
The first type of data analysis is descriptive analysis. It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.
The biggest use of descriptive analysis in business is to track Key Performance Indicators. KPIs describe how a business is performing based on chosen benchmarks.
- Diagnostic Analysis
In the data analyst course, diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.
A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, it is possible you have already collected certain data pertaining to the issue. By already having the data at your disposal, end up having to repeat work and make all problems interconnected.
- Predictive Analysis
In the data analyst course, predictive analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data.
While descriptive and diagnostic analysis are common practices in business, predictive analysis is where many organizations begin show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or are not prepared to educate current teams.
- Prescriptive Analysis
The final type of data analysis is the most sought-after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision. Prescriptive analysis utilizes state-of-the-art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.
Data analyst tools
Python
Python is an object-oriented open-source programming language. It supports a range of libraries for data manipulation, data visualization, and data modeling.
R
R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data analysis and visualization.
Tableau
It is a simplified data visualization and analytics tool. This helps you create a variety of visualizations to present the data interactively, and build reports, and dashboards to showcase insights and trends.
Power BI
Power BI is a business intelligence tool that has an easy ‘drag-and-drop functionality. It supports multiple data sources with features that visually appeal to data. Power BI supports features that help to ask questions about data and get immediate insights.
QlikView
QlikView offers interactive analytics with in-memory storage technology to analyze vast volumes of data and use data discoveries to support decision-making. It provides social data discovery and interactive guided analytics. It can manipulate colossal data sets instantly with accuracy.
In Summary
Data analyst course can improve anyone’s decision-making and it can also eliminate guesswork and manual tasks. Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.