In an age where the value of data has drastically increased, the reliance on data to make important decisions by businesses, institutions and even individuals has increasingly become a norm.
Data analytics involves the process of collecting, organizing, transforming and analyzing data to draw insights. It helps business and institutions optimize their operations be it in terms of costs or the market. It is used to many different industries as discussed in the following article,
This guide aims to discuss the techniques and tools used in the process, I hope it will be helpful and for anything that needs correction or clarification, don't hesitate to reach out as this is my first ever technical article. So let's get into it then.

The techniques are used to analyze qualitative and quantitative data. Quantitative data which involves descriptive statistics and inferential statistics.

1. Descriptive Analytics
Descriptive statistics involves summarizing and organizing data to describe the current situation. It uses measures like mean, median, mode, and standard deviation to describe the main features of a data set.

Example: A company analyzes sales data to determine the monthly average sales over the past year. They calculate the mean sales figures and use charts to visualize the sales trends.

2. Diagnostic Analytics
Diagnostic analysis goes beyond descriptive statistics to understand why something happened and determine the causes of trends and correlations between variables. It looks at data to find the causes of events, mostly seeking to answer the "why" questions.

Example: After noticing a drop in sales, a retailer uses diagnostic analysis to investigate the reasons. They examine marketing efforts, economic conditions, and competitor actions to identify the cause.

3. Predictive Analytics
Predictive analytics is the process of using historical data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.
Example: An insurance company uses predictive analysis to assess the risk of claims by analyzing historical data on customer demographics, driving history, and claim history.

4. Prescriptive Analytics
Prescriptive analytics is the use of advanced processes and tools to analyze data and content to recommend the optimal course of action or strategy moving forward. Simply put, it seeks to answer the question, “What should we do?”
Example: An online retailer uses prescriptive analysis to optimize its inventory management. The system recommends the best products to stock based on demand forecasts and supplier lead times.

ESSENTIAL TOOLS FOR DATA ANALYTICS
There are many different tools that are used in data analytics. The different software and programming languages help to provide an environment that enables data analysts to efficiently clean, transform and analyze data.

Programming languages include:

• Python: Python has in-built mathematical libraries and functions, making it easier to calculate mathematical problems and to perform data analysis.

• R:R analytics (or R programming language) is a free, open-source software used for all kinds of data science, statistics, and visualization projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.
  These integrations include everything from statistical functions to predictive models, such as linear regression. R also allows you to build and run statistical models using Sisense data, automatically updating these as new information flows into the model.

Data visualization tools include: Tableau, Power BI, Google Data Studio. I will discuss the first two tools with a little more detail, because they are commonly used.

• Power BI: is a technology-driven business intelligence tool provided by Microsoft for analyzing and visualizing raw data to present actionable information. It combines business analytics, data visualization, and best practices that help an organization to make data-driven decisions. It converts data from different sources to build interactive dashboards and Business Intelligence reports. It is highly preferred because of the following reasons:
• Access to Volumes of Data from Multiple Sources Power BI can access vast volumes of data from multiple sources. It allows you to view, analyze, and visualize vast quantities of data that cannot be opened in Excel. Some of the important data sources available for Power BI are Excel, CSV, XML, JSON, pdf, etc. Power BI uses powerful compression algorithms to import and cache the data within the. PBIX file.

1. Interactive UI/UX Features
   Power BI makes things visually appealing. It has an easy drag and drops functionality, with features that allow you to copy all formatting across similar visualizations.

2. Exceptional Excel Integration
   Power BI helps to gather, analyze, publish, and share Excel business data. Anyone familiar with Office 365 can easily connect Excel queries, data models, and reports to Power BI Dashboards.

3. Accelerate Big Data Preparation with Azure
   Using Power BI with Azure allows you to analyze and share massive volumes of data. An azure data lake can reduce the time it takes to get insights and increase collaboration between business analysts, data engineers, and data scientists.

4. Turn Insights into Action
   Power BI allows you to gain insights from data and turn those insights into actions to make data-driven business decisions.

5. Real-time Stream Analytics
   Power BI will enable you to perform real-time stream analytics. It helps you fetch data from multiple sensors and social media sources to get access to real-time analytics, so you are always ready to make business decisions.

• Tableau: is an analytics solution that allows users to connect, analyze, and share their data. The software started as a visualization tool, growing into an enterprise platform with several deployment options until they were acquired by Salesforce in 2019.

• Statistical analysis tools include:
  SAS:is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation,[2] and predictive analytics.

• SPSS: is a suite of software programs that analyzes scientific data related to the social sciences. SPSS offers a fast-visual modeling environment that ranges from the smallest to the most complex models. The data obtained from SPSS is used for surveys, data mining, market research, etc.

Big data tools include:
Hadoop
Spark

The success of a data analytics depends on the tools and techniques used, just like a good cook who knows how to prepare their food. The tools and techniques chosen will depend on many factors including: the nature of data, purpose of the analysis, complexity of the problem, domain or industry, cost and resources, integration with existing systems, data privacy and security just to mention a few.

You can read more about the data analysis processes and even the history and its projected growth as we get into a world that relies more and more on data.