Data Analysis

About Data Analysis Using Python

Data analysis is a technique within which data is collected and arranged in order that one will derive useful info from it. In different words, the most purpose of data analysis is to seem at what the data is trying to tell us.for instance, what will the data show or do? What will the data not show or do? In present time Data Analyst is one of the hottest professions of the time and here in our training program you are going to learn how Python is helpful for data analysis. Once you are a Python expert, you will be able to solve any data analysis problem with an ease.

In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years.

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Why Python For Data Analysis

 Python and R are two very popular open-source programming languages for data analysis. Frequently, users debate as to which tool is more valuable, however both languages offer key features and can be used to complement one another. A common perception is that R offers more depth when it comes to data analysis, data modeling and machine learning, but Python is easier to learn and tends to present graphs in a slightly more polished way. There are many more reasons for using Python for Data Analysis which are given below:

  • Python is easy to use
  • Python is versatile
  • Python is better for building analytics tools
  • Python has in-built beautiful and efficient data structures
  • The Python community is growing
  • Python is better for deep learning
  • Great number of open source libraries/frameworks/tools available
  • Data visualisation with Python

Data Analysis

Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.

Companies that want to not only stay in business but also thrive can improve their odds of success by making smart choices. And how does an individual or organization make these choices? They do it by collecting as much useful, actionable information as possible, then using it to make better-informed decisions!

This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on ignorance. Organizations need information; they need data.

This need for data is why the discipline of data analysis enters into the picture. This article is your primer on data analysis, what the phrase means, the available types and processes, popular data analysis methods, and how to do data analysis.

Now, before get into the details about the data analysis methods, let us first understand what is data analysis.

What is Data Analysis?

Although many groups, organizations, and experts have different ways to approach data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.

It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”

What is the Data Analysis Process?

The data analysis process, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process consists of:

  • Data Requirement Gathering. Ask yourself why you’re doing this analysis, what type of data analysis you want to use, and what data you are planning on analyzing.
  • Data Collection. Guided by the requirements you’ve identified, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
  • Data Cleaning. Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
  • Data Analysis. Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
  • Data Interpretation. Now that you have your results, you need to interpret them and come up with the best courses of action, based on your findings.
  • Data Visualization. Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships. 

What Types of Data Analysis are There?

There are a half-dozen popular types of data analysis available today, commonly employed in the worlds of technology and business. They are: 

  • Diagnostic Analysis. Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.
  • Predictive Analysis. Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.
  • Prescriptive Analysis. Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
  • Statistical Analysis. Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:

o   Descriptive. Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.

o   Inferential. Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.

  • Text Analysis. Also called “data mining,” text analysis uses databases and data mining tools to discover patterns residing in large datasets. It transforms raw data into useful business information. Text analysis is arguably the most straightforward and the most direct method of data analysis.

Next, we will get into the depths to understand about the data analysis methods.

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