What is Data Analytics?

The process of collecting, organizing, and transforming data to support conclusions, projections, and well-informed decision-making is known as data analytics. Data Analysis is a subcategory of data analytics that deals specifically with extracting meaning from data.

Data analytics includes data science which refers to using data to theorize and forecast and data engineering which refers to building data systems.

How is Data Analytics used?

It allows companies to roll out targeted content and fine-tune it by analyzing real-time data. Data analytics also provides valuable insights into how marketing campaigns are performing.

Data Analysis Process

1. identifying the question
2. collecting raw data
3. cleaning data
4. analyzing data
5. interpreting the results.

Skills required in Data Analytics

Structured query language (SQL) is a programming language for storing and processing information in a relational database. A relational database stores information in tabular form, with rows and columns representing different data attributes and the various relationships between the data values. It is a programming language commonly used for databases

Machine learning (ML) is a branch of Artificial Intelligence and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn.

Statistical programming language typically refers to a programming language that is specifically designed and optimized for performing statistical analysis, data manipulation, and data visualization. R and Python are commonly used to create advanced data analysis programs.

Probability and statistics, in order to better analyze and interpret data trends.

Data visualization, or the ability to use charts and graphs to tell a story with data.

Importance of Data Analytics

Predict future trends: Businesses that employ predictive analysis may develop future-focused products and adapt rapidly to developing market trends, giving them a competitive advantage over their competitors. Depending on the application, the data studied may include historical records or new information processed for real-time analytics.

Measure performance. Data analytics provide organizations with metrics and key performance indicators (KPIs) to track progress, monitor performance and evaluate the success of business initiatives. This helps businesses respond promptly to changing market conditions and other operational challenges.

Improve security. Companies use data analytics methods to look at past security breaches and find the underlying vulnerabilities. Data analytics can also be integrated with monitoring and alerting systems to quickly notify security professionals in the event of a breach attempt.

Personalize customer experiences. By going beyond traditional data methods, data analytics connects insights with actions, enabling businesses to create personalized customer experiences and develop related digital products.

Reduce operational costs. By optimizing processes and resource allocation, data analytics can help reduce unnecessary expenses and identify cost-saving opportunities within the organization.

Provide risk management. Data analytics lets organizations identify and mitigate risks by detecting anomalies, fraud and potential compliance issues.