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 taking the decision based upon the data analysis.
Whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.
Why Data Analysis?
Data Analysis Tools
Types of Data Analysis: Techniques and Methods
Data Analysis Process
Why Data Analysis?
Data Analysis Tools
Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools.
There are multiple facets and approaches with diverse techniques for the data analysis. The data analysis in statistics are generally divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). Data need to be cleaned. Data cleaning is the process of correcting the outliers and other incorrect and unwanted information. There are several types of data cleaning process to employ depends on the type of data to be cleaned. For quantitative data methods the outlier detection can be used to get rid of anomaly in the data. Spellcheckers can used to lessen the amount of mistyped words in case of textual data.
Business intelligence covers the data analysis that run heavily on aggregation, disaggregation, slicing and dicing, focusing on the business information. Predictive analytics is the application of statistical or structural models for predictive forecasting. Text analytics is the application of statistical, linguistic, and structural models to extract and classify information from texts. All these are varieties of data analysis.
Initial phase data analysis:
1.Data Cleaning :
This is the first process of data analysis where record matching, deduplication, and column segmentation are done to clean the raw data from different sources.
2.Quality Analysis:
Using frequency counts, descriptive statistics such as mean, standard deviation, median, normality histograms such as skewness, kurtosis, frequency, where the n variables are compared with variables of external to the data set.
3.Quality of Measurement:
Using confirmatory factor analysis and Analysis of homogeneity.
4.Analysis:
There are many analyses which can be done during the initial data analysis phase.
Univariate statistics ,single variable.
Bivariate associations correlations.
Graphical techniques scatter plots.
Nominal and ordinal variables.
Frequency counts in numbers and percentages.
Associations
Circumambulations
Types of Data Analysis: Techniques and Methods
There are several types of Data Analysis techniques that exist based on business and technology. However, the major types of data analysis are:
Text Analysis
Statistical Analysis
Diagnostic Analysis
Predictive Analysis
Prescriptive Analysis
Text Analysis
Text Analysis is also referred to as Data Mining. It is a method to discover a pattern in large data sets using databases or data mining tools. It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.
Statistical Analysis
Statistical Analysis shows "What happen?" by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis - Descriptive Analysis and Inferential Analysis.
Descriptive Analysis
analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.
Inferential Analysis
analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.
Diagnostic Analysis
Diagnostic Analysis shows "Why did it happen?" by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.
Predictive Analysis
Predictive Analysis shows "what is likely to happen" by using previous data. The simplest example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it's not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!
So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.
Prescriptive Analysis
Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.
Data Analysis Process
The Data Analysis Process is nothing but 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 of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.
Data Collection
After requirement gathering, you will get a clear idea about what things you have to measure and what should be your 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. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.
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. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.
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 which will help you 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 shown graphically so that it will be easier for the human brain to understand and process it. Data visualization often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.
Summary:
Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
