## What is Data Analytics?

Data analytics examines, cleans, transforms, and models data to extract useful information, draw conclusions, and support decision-making.

It involves using various techniques and tools from statistics, machine learning, and data visualisation to gain insights from data and help organisations make data-driven decisions.

## How is data analytics used?

Data analytics is used in a wide range of industries and applications. Some examples include:

• Business: Companies use data analytics to improve their operations, identify new opportunities, and gain a competitive advantage. For example, retailers use data analytics to optimise pricing, manage inventory, and improve marketing campaigns.
• Healthcare: Data analytics improves patient outcomes, reduces costs, and increases efficiency in healthcare organisations. For example, data analytics can be used to identify patterns in patient data to predict which patients are at high risk of readmission or identify potential infectious disease outbreaks.
• Finance: Data analytics is used to detect fraud, manage risk, and make investment decisions. For example, financial institutions use data analytics to detect fraudulent transactions and to identify potential hazards in their loan portfolio.
• Transportation: Data analytics optimises routes, reduces fuel consumption, and improves vehicle maintenance schedules.
• Government: Data analytics is used to improve service delivery, increase efficiency, and make better policy decisions. For example, government agencies use data analytics to identify patterns in crime data to help allocate resources or improve social programs' effectiveness.

These are just a few examples, but data analytics can be used in almost any industry or organisation where data is collected and stored.

## Data analytics: Key concepts

Some key concepts in data analytics include:

• Data: Data is the raw material that is analysed in data analytics. It can come in many forms, such as numbers, text, images, or audio.
• Data Cleaning: Data cleaning removes or corrects inaccuracies, inconsistencies, and missing values from data. This is essential in data analytics, as dirty data can lead to inaccurate or unreliable conclusions.
• Data Exploration: Data exploration analyses data to understand its structure, distribution, and relationships. This includes summarising data, identifying patterns, and detecting outliers.
• Data Visualization: Data visualisation creates graphical representations of data to make it easier to understand and communicate. This can include creating charts, graphs, maps, and other visualisations.
• Data Modeling: Data modelling is creating mathematical representations of data to help understand and make predictions about it. This can include tasks such as creating statistical models, machine learning models, and simulation models.
• Data Governance: Data Governance is managing, maintaining and protecting data from unauthorised access, usage or modification. It includes controlling access to data, maintaining data quality, and ensuring data security.
• Data Ethics: Data ethics is the study of how data is collected, used, and shared and how it can be used ethically and responsibly. It includes understanding data privacy, data bias, and data ownership issues.
• Big Data: Big data refers to data that is too large or complex to be handled by traditional data processing tools. Processing and analysing big data requires specialised technologies such as Hadoop, Spark, and NoSQL databases.

These are some key concepts in data analytics, but many other ideas and techniques are used depending on the specific problem or application.

## Data analytics skills

There are a variety of skills that are important for data analytics, including:

• Programming: Data analysts often use programming languages such as Python, R, SQL, and SAS to clean, transform, and analyse data.
• Statistical Analysis: Data analysts use statistical techniques to understand and predict data. This includes hypothesis testing, regression analysis, and machine learning.
• Data Visualization: Data analysts use data visualisation tools to create graphical representations of data to make it easier to understand and communicate.
• Business Acumen: Data analysts need to understand business problems and use data to help organisations make better decisions.
• Communication: Data analysts need to be able to communicate their findings and recommendations to non-technical stakeholders, both verbally and in written forms.
• Critical Thinking: Data analysts need to be able to evaluate data and draw logical conclusions from it critically.
• Machine Learning: Data analysts need to be familiar with machine learning algorithms, such as supervised and unsupervised learning, to predict patterns and make predictions.
• Problem-Solving: Data analysts need to be able to solve problems, think critically, and make decisions based on data.
• Database Management: Data analysts need to be familiar with database management systems, such as SQL and NoSQL, to extract and manipulate data from databases and big data platforms.
• Cloud computing: Data analysts need to be familiar with cloud computing platforms, such as AWS, Azure, and GCP, to process and store a large amount of data.

These are some critical skills required for data analytics, but the specific skills needed will depend on the job and the industry.

## Data analytics careers and Job scope with salary

Data analytics careers can be found in various industries, including finance, healthcare, retail, technology, and government. Some examples of job titles that involve data analytics include:

• Data Analyst: A data analyst is responsible for collecting, cleaning, and analysing data to extract insights and support decision-making. This can include creating reports, dashboards, and visualisations to communicate findings. The average salary for a Data Analyst is around $65,000 -$85,000 per year.
• Business Intelligence Analyst: A business intelligence analyst is responsible for using data to support decision-making in an organisation. This can include creating reports, dashboards, and visualisations to help managers and executives understand key metrics and trends. The average salary for a Business Intelligence Analyst is around $80,000 -$120,000 per year.
• Data Scientist: A data scientist uses data to solve complex problems and support decision-making. This can include developing statistical models, machine learning models, and simulations to extract insights from data. The average salary for a Data Scientist is around $100,000 -$150,000 per year.
• Data Engineer: A data engineer is responsible for designing, building, and maintaining the systems and infrastructure used to store, process, and analyse data. The average salary for a Data Engineer is around $90,000 -$130,000 per year.
• Machine Learning Engineer: A machine learning engineer is responsible for developing and deploying machine learning models to automate decision-making and improve the performance of an organisation. The average salary for a Machine Learning Engineer is around $120,000 -$170,000 per year.
• Data Governance Analyst: A data governance analyst is responsible for managing, maintaining, and protecting data from unauthorised access, usage or modification. This includes controlling access to data, maintaining data quality, and ensuring data security. The average salary for a Data Governance Analyst is around $70,000 -$100,000 per year.

These are just averages; salaries can vary depending on location, experience, and the specific company or industry.

It's also worth noting that data analytics is in high demand, and salaries can be higher in specific industries such as technology, finance, and consulting. With the increasing importance of data-driven decision-making, the field is expected to grow in the coming years.

The job scope may vary depending on the company, size, and type of industry. Data analytics professionals are responsible for using data to improve the performance and decision-making of an organisation. They use various tools, techniques and methods to extract insights and make predictions. They also need to communicate results and wisdom to the stakeholders.