What is Data Analytics?

Data Analytics is the process of collecting, organizing and studying data to find useful information understand what’s happening and make better decisions. In simple words it helps people and businesses learn from data like what worked in the past, what is happening now and what might happen in the future.

People often mix up data analytics and data analysis but they’re not exactly the same. Data analysis is just one part of data analytics it focuses on finding meaning in data. On the other hand data analytics includes more than just analysis. It also involves things like coming up with ideas and predictions from data and building the tools and systems needed to handle large amounts of data.

Importance and Usage of Data Analytics

Data analytics is used in many fields like banking, farming, shopping, government and more. It helps in many ways:

• Helps in Decision Making: It gives clear facts and patterns from data which help people make smarter choices.
• Helps in Problem Solving: It points out what's going wrong and why making it easier to fix problems.
• Helps Identify Opportunities: It shows trends and new chances for growth that might not be obvious.
• Improved Efficiency: It helps reduce waste, saves time and makes work smoother by finding better ways to do things.

Process of Data Analytics

Data analysts, data scientists and data engineers together create data pipelines which helps to set up the model and do further analysis. Data Analytics can be done in the following steps which are mentioned below:

1. Data Collection : Data collection is the first step where raw information is gathered from different places like websites, apps, surveys or machines. Sometimes data comes from many sources and needs to be joined together. Other times only a small useful part of the data is selected.
2. Data Cleansing : Once the data is collected it usually contains mistakes like wrong entries, missing values or repeated rows. In this step the data is cleaned to fix those problems and remove anything that isn’t needed. Clean data makes the results more accurate and trustworthy.
3. Data Analysis and Data Interpretation: After cleaning the data is studied using tools like Excel, Python, R or SQL. Analysts look for patterns, trends or useful information that can help solve problems or answer questions. The goal here is to understand what the data is telling us.
4. Data Visualization: Data visualization is the process of creating visual representation of data using the plots, charts and graphs which helps to analyze the patterns, trends and get the valuable insights of the data. By comparing the datasets and analyzing it data analysts find the useful data from the raw data.

Types of Data Analytics

There are different types of data analysis in which raw data is converted into valuable insights. Some of the types of data analysis are mentioned below:

1. Descriptive Data Analytics : Descriptive data analytics helps to summarize and understand past data. It shows what has happened by using tables, charts and averages. Companies use it to compare results, find strengths and weaknesses and spot any unusual patterns.
2. Diagnostic Data Analytics: Diagnostic data analytics looks at why something happened in the past. It uses tools like correlation, regression or comparison to find the cause of a problem. This helps companies understand the reason behind a drop in sales or a sudden change in performance.
3. Predictive Data Analytics: Predictive data analytics is used to guess what might happen in the future. It looks at current and past data to find patterns and make forecasts. Businesses use it to predict things like customer behavior, future sales or possible risks.
4. Prescriptive Data Analytics: Prescriptive data analytics helps to choose the best action or solution. It looks at different options and suggests what should be done next. Companies use it for things like loan approval, pricing decisions and managing machines or schedules.

Methods of Data Analytics

There are two types of methods in data analytics which are mentioned below:

1. Qualitative Data Analytics

Qualitative data analysis doesn’t use statistics and derives data from the words, pictures and symbols. Some common qualitative methods are:

• Narrative Analytics is used for working with data acquired from diaries, interviews and so on.
• Content Analytics is used for Analytics of verbal data and behaviour.
• Grounded theory is used to explain some given event by studying.

2. Quantitative Data Analysis

Quantitative data Analytics is used to collect data and then process it into the numerical data. Some of the quantitative methods are mentioned below:

• Hypothesis testing assesses the given hypothesis of the data set.
• Sample size determination is the method of taking a small sample from a large group of people and then analysing it.
• Average or mean of a subject is dividing the sum total numbers in the list by the number of items present in that list.

Skills Required for Data Analytics

There are multiple skills which are required to be a Data analyst. Some of the main skills are mentioned below:

• Some of the common programming languages which are used are R and Python.
• For databases Structured Query Language (SQL) is a programming language used.
• Machine Learning is used in data analysis.
• In order to better analyse and interpret probability and statistics are used.
• For collecting and organising data, Data Management is used in data analysis.
• To use charts and graphs Data visualisation is used.

Data analytics helps businesses grow by turning raw data into useful insights. It supports smarter decisions, better planning and improve customer service across many industries not just finance. From retail to government data analytics plays a key role in today’s world by helping organizations find patterns, solve problems and plan for the future.