Data analytics is the process that helps you analyse raw data to make conclusions or gain insights about the information the data contain. This field of science uses different tools, techniques, and disciplines, like computer programming, statistics, mathematics, data mining, and data modelling to collect, organise, and store data. Data analytics can help solve problems, optimise performance, or make informed decisions in various sectors or industries. In this article, we will find out how in demand data analytics is in today’s digital world. Read on to find out more.
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Importance of data analysis
Data analytics is important for businesses and organisations in the digital age because it can help them leverage the vast amount of data generated by various sources, like social media, sensors, and transactions, to name a few.
By using data analytics, businesses and organisations can uncover patterns, trends, correlations, and anomalies to reveal valuable insights about their customers, markets, operations, products, and services, just to name a few.
The purpose of these insights is to improve customer satisfaction, increase revenue, reduce costs, enhance efficiency, innovate solutions, or gain a competitive edge.
The demand of data analysts
According to a report by the U.S. Bureau of Labor Statistics (BLS), the data science field will grow by about 28% through 2026. This means that there will be more demand for data analysts and scientists than there is people with the right skills to fill those roles.
Apart from this, the BLS reports that the median annual wage for data analysts was $94,280 in May 2020. These statistics show that data analytics is a lucrative and growing career path for those who have the skills and knowledge to work with data.
The fundamental skills and tools
The primary skills and tools that data analysts need to master may include SQL, Python, R, Excel, and Tableau, to name a few.
Data analysts need to master a variety of skills and tools to collect, process, analyze, and communicate data effectively. Some of the most common and essential skills and tools are described below.
- SQL
SQL is short for Structured Query Language, which is the standard language used to communicate with databases. Knowing SQL allows you to update, organize, and query data stored in relational databases, as well as modify data structures (schema). Since almost all data analysts will need to use SQL to access data from a company’s database, it’s arguably the most important skill to learn for data analysts.
- Python or R
They both offer a wide range of libraries and packages that can help you perform various tasks, like data manipulation, statistical analysis, machine learning, and data visualisation, to name a few.
Python is more general-purpose and versatile, while R is more specialised and tailored for statistics and data science. You don’t need to learn both languages, but you should be proficient in at least one of them.
- Excel
Excel is spreadsheet software that can help you perform basic calculations, data analysis, and data visualisation. Apart from this, Excel is widely used in many industries and domains, and it can be a useful tool for quick and simple data exploration and presentation.
However, Excel has some limitations when it comes to handling large or complex data sets. Therefore, you should also be familiar with more advanced tools for data analysis.
- Tableau
Tableau is data visualisation software that can help you create interactive and engaging dashboards and reports. The good thing about Tableau is that it can help connect to various data sources, like databases, files, or web services. Apart from this, it allows you to explore and visualise data using drag-and-drop features.
Similarly, Tableau can help you share your insights with others through online publishing or embedding. Data visualisation is a crucial skill for data analysts, as it can help you communicate your findings effectively and persuasively.
Classifications of data analysis
Data analysis can be classified into four main types according to the purpose and scope of the analysis. Let’s talk about each classification.
- Descriptive analysis
Descriptive analysis aims to summarize what has happened in the past using historical data. It can help you understand the current state of a situation or phenomenon by providing basic statistics, like mean, median, mode, frequency, and distribution, to name a few.
Besides, descriptive analysis can help you identify patterns or trends in the data using charts or graphs. For instance, descriptive analysis can help you answer questions like “How many customers visited our website last month?” or “What are the most popular products in our store?”
- Diagnostic analysis
Diagnostic analysis goes beyond descriptive analysis by trying to explain why something has happened in the past using historical data. Apart from this, it can help you identify the root causes or factors that influenced a situation or outcome by using techniques such as correlation analysis, regression analysis, and hypothesis testing, to name a few.
In addition, diagnostic analysis can help you compare different groups or segments of data using techniques such as ANOVA or chi-square test. For instance, diagnostic analysis can help you answer questions like “Why did our sales drop last quarter?” or “What are the characteristics of our most loyal customers?”
- Predictive analysis
Predictive analysis aims to forecast what will happen in the future using historical and current data. It can help you anticipate future trends or outcomes by using techniques such as machine learning, time series analysis, and sentiment analysis, to name a few.
The good thing about predictive analysis is that it can help you evaluate different scenarios or alternatives by using techniques, like simulation or optimization. For instance, predictive analysis can help you answer questions like “What will be our revenue next year?
- Prescriptive analysis
Prescriptive analysis goes beyond predictive analysis by trying to recommend what should be done in the future using historical and current data. In other words, it can help you optimize your decisions or actions by using techniques such as decision trees, linear programming, and neural networks, to name a few.
Apart from this, prescriptive analysis can help you assess the risks or trade-offs of different options by using many techniques, like sensitivity analysis or Monte Carlo simulation.
For instance, prescriptive analysis can help you answer questions like “What is the best marketing strategy for our product?” or “How should we allocate our resources among different projects?”
In short, the information in this article may have given you a pretty good idea of how in demand data analysts are in the here and now.
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