In today’s world, most of the researcher’s job is to go through data i.e literally the definition of “research”. However, today’s information age produces a tremendous amount of data, enough to overwhelm the most dedicated researcher.

Data analysis, therefore, plays a key role in extracting this information into a more accurate and relevant form, making it easier for researchers to do their job.

So, to sum it up, data analysis offers researchers better data and better ways to analyze and study said data.

What is Data?

Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things.

Data can be qualitative or quantitative.

• Qualitative Data is descriptive information (it describes something) eg. It was great fun
• Quantitative Data is numerical information (numbers). It can be discrete or continuous.
  • Discrete Data can only take certain values (like whole numbers) eg. 7
  • Continuous Data can only take the value (within a range) eg. 5.345

Discrete Data is counted and Continuous Data is measured.

Data has changed our world over the last ten years. The innumerable emails, text messages we share, and videos we watch are part of the huge amount of data generated daily across the world. Businesses deal with a massive amount of data and a lot depends on their ability to glean meaningful insights from them. A data analyst does precisely that. They interpret statistical data and turn it into useful information that businesses and organizations can use for critical decision-making.

As a result, data analysis has become one of the highest in-demand jobs worldwide and data analysts are sought after by the world’s biggest organizations.

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and make a decision based on the data analysis.

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, you have to look back, acknowledge your mistakes, and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business grow more. All you need to do is analyze your business data and business processes.

Who is Data Analyst?

A Data Analyst is an expert who gathers information from different sources and examines the data from different viewpoints and afterward at long last creates the reports. Along these lines, reports are then conveyed to individual groups to examine data and give an improvement in the business.

“Data analysts’ work differs by relying upon the kind of data that they’re working with (deals, web-based media, stock, and so forth).

Data Analysis Process

The Data Analysis Process is gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.

Data Analysis consists of the following phases:

• Data Requirement Gathering
• Data Collection
• Data Cleaning
• Data Analysis
• Data Interpretation
• Data Visualization

Data Requirement Gathering

First, you have to think about why do you want to do this data analysis? You have to find out the purpose or aim of doing the Analysis of data. You have to decide the type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it.

Data Collection

After requirement gathering, you will get a clear idea about things to measure and their findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis.

Data Cleaning

Now whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces, or errors. The data should be cleaned and error-free.

Data Analysis

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software to understand, interpret, and derive conclusions based on the requirements.

Data Interpretation

After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.

Data Visualization

Data visualization is very common in your day-to-day life; they often appear in the form of charts and graphs. In other words, data is shown graphically so that it will be easier for the human brain to understand and process it. Data visualization is often used to discover unknown facts and trends.

Data Analyst’s Tasks and Responsibilities

The role includes plenty of time spent with data but entails communicating findings too. 

Here’s what many data analysts do on a day-to-day basis:

• Gather data: Analysts often collect data themselves by conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.
• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations are correct. 
• Model data: This entails creating and designing the structures of a database by choosing types of data to store and collect, establishing how data categories are related to each other, and working through how the data actually appears.
• Interpret data: Interpreting data will involve finding patterns or trends in data that will help you answer the question at hand.
• Present: Communicating the results of your findings will be a key part of putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

Data Analyst Technical Skills

• Database tools:  Microsoft Excel and SQL should be mainstays in any data analyst’s toolbox.
• Programming languages: Learning a statistical programming language like Python or R will let you handle large sets of data and perform complex equations.  
• Data visualization: Knowing how best to present information through charts and graphs will make sure colleagues, employers, and stakeholders will understand your work. Tableau, Jupyter Notebook, and Excel are among the tools used to create visuals.
• Statistics and math: Having a solid grasp of statistics and math will help you determine which tools are best to use to solve a particular problem.

Data Analyst Workplace Skills

• Problem-solving: A data analyst needs to have a good understanding of the question being asked and the problem that needs to be solved. They also should be able to find patterns or trends that might reveal a story.
• Communication: Strong written and speaking skills to communicate with colleagues and other stakeholders are good assets for data analysts.
• Industry knowledge: Knowing about the industry you work in—health care, business, finance, or otherwise—will give you an advantage in your work and in job applications.

Top 10 Data Analytics Tools

1. Programming languages-(Python & R)

• R is an open-source instrument utilized for statistics and analytics though Python is an undeniable level, an interpreted language with simple syntax and dynamic semantics.

2. Microsoft Excel

• Being one of the most famous apparatuses for Data Analytics, Microsoft Excel gives the clients elements like sharing workbooks, working on the latest version for real-time collaboration, and adding data to Excel straightforwardly from a photograph, etc.

3. Tableau

• Tableau is a market-driving Business Intelligence tool used to analyze and picture data in a simple arrangement.

4. RapidMiner

• RapidMiner is a  stage for data processing, building Machine Learning models, and arrangement.

5. KNIME

• Konstanz Information Miner or generally ordinarily known as KNIME is a free and open-source data analytics, reporting, and integration stage worked for analytics on a GUI-based work process.

6. Power BI

• Power BI intelligent representations with self-administration business insight capacities, where end clients can make dashboards and reports without help from anyone else.

7. Apache Spark

• Is open-source and is utilized for real-time processing. It accompanies an incredible open-source community and a point of interaction for programming.

8. Qlikview

• It expects to speed up business esteem through data by giving elements like Data Integration, Data Literacy, and Data Analytics.

9. Talend

• Talend is one of the most remarkable data integration ETL tools accessible in the market and is created in the Eclipse graphical development environment.
• This tool allows you effectively to deal with every one of the means engaged with the ETL interaction and plans to deliver compliant, open, and clean data for everybody

10. Splunk

• Splunk is a stage used to search, analyze, and visualize the machine-created information accumulated from the applications, and websites.

Summary

Data analysis means a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, and Data Visualization.