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Data Analysis Tutorial

Last Updated : 17 May, 2023
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Data Analysis is a process of studying, cleaning, modeling, and transforming data with the purpose of finding useful information, suggesting conclusions, and supporting decision-making. This Data Analytics Tutorial will cover all the basic to advanced concepts of Excel data analysis like data visualization, data preprocessing, time series, data analysis tools, etc.

Data Analysis Process

Data Analysis is developed by the statistician John Tukey in the 1970s. It is a procedure for analyzing data, methods for interpreting the results of such systems, and modes of planning the group of data to make its analysis easier, more accurate, or more factual.

Therefore, data analysis is a process for getting large, unstructured data from different sources and converting it into information that is gone through the below process:

  • Data Requirements Specification
  • Data Collection
  • Data Processing
  • Data Cleaning
  • Data Analysis
  • Communication

Need for Data Analysis

Data analytics is significant for business optimization performance. An organization can also use data analytics to make better business decisions and support analyzing customer trends and fulfillment, which can lead to unknown and better products and services. Executing it into the business model indicates businesses can help reduce costs by recognizing more efficient modes of doing business. 

Tools Used in Data Analysis

  • Microsoft Excel
  • Python
  • R
  • Jupyter Notebook
  • Apache Spark
  • SAS
  • Microsoft Power BI
  • Tableau
  • KNIME

Applications of Data Analysis

  • Better decision-making: The Key advantage of data analysis is better decision-making in the long term. Rather than depending only on knowledge, businesses are increasingly looking at data before deciding. 
  • Identification of potential risks: Companies in today’s world succeed in high-risk conditions, but those environments require critical risk management processes, and extensive data has contributed to developing new risk management solutions. Data can enhance the effectiveness of actual simulations to predict future risks and create better planning.
  • Increase the efficiency of work: Data analysis allows you to analyze a large set of data and present it in a structured way to help reach your organization’s objectives. Possibilities and progress within the organization are reflected, and activities can increase work efficiency and productivity. It enables a culture of efficiency and collaboration by allowing managers to share detailed data with employees.
  • Delivering relevant products: Products are the oil for every organization, and often the most important asset of organizations. The role of the product management team is to determine trends that drive strategic creation, and activity plans for unique functions and services.
  • Track customer behavioral changes: Consumers have a lot to choose from in products available in the markets. Organizations have to pay attention to consumer demands and expectations, So to analyze the behavior of the customer data analysis is very important.

 

Introduction:

Read the data set:

Data Visualization:

Exploratory Data Analysis

Data Preprocessing:

Time Series Data Analysis:

Data Analysis Tools:

FAQs on Data Analysis

Q.1 What are the four types of Data Analysis?

Answer: There are four types of data Analysis:

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive

Q.2 Why is data analytics so important?

Answer: Data analytics is more than simply showing numbers and figures to the administration. It is about analyzing and understanding your data and using that information to drive actions. Data analytics displays the patterns and trends within the data, which strengthen or otherwise remain unknown. 

Q.3 What are the tools useful for data analysis?

Answer: Some of the tools useful for data analysis include: 

  • RapidMiner 
  • KNIME 
  • Google Search Operators 
  • Google Fusion Tables 
  • Solver 
  • NodeXL 
  • OpenRefine 
  • Wolfram Alpha 
  • io 
  • Tableau, etc.

Q.4 What are the differences between Data Mining and Data Profiling?

Data Mining

Data Profiting

Data mining is the procedure of finding suitable data that has not yet been determined before. Data profiling is done to estimate a dataset for its uniqueness, logic, and consistency.
In data mining, raw data is converted into useful information. It cannot identify incorrect data values.


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