What Is Data Analysis?
Data analysis is a term that refers to the study of data. Data analysis is the method of reviewing, cleaning, transforming, and modeling data with the aim of finding useful facts, drawing conclusions, and assisting in decision-making. For data analysis, there are several facets and methods, as well as a variety of techniques.
In Simple Words Data analysis‘ key goal is to find value in data so that the information gained can be used to make better decisions.
What is the significance of data analysis? Types of data analysis?
Data-driven businesses make data-driven decisions, which means they can be more assured that their choices will result in results because they have stats to support them up.
In industry, data analytics is used to assist companies in making informed decisions. Whether it’s market research, product research, positioning, consumer feedback, opinion analysis, or any other topic on which data is available, analyzing data can provide companies with the information they need to make the best decisions possible. Data can be found in a variety of places, including spreadsheets, the sales funnel, social media sites, customer satisfaction surveys, and customer service tickets. It’s developed at breakneck rates in our modern information era, and when properly analyzed, it can be a company’s most valuable asset.
Types of data analysis:
Text Analysis: Text analysis is the process of parsing texts to extract machine-readable facts. Text Analysis is used to generate structured data from unstructured text. Slicing and dicing heaps of unstructured, heterogeneous documents into easy-to-manage and understand data bits is how the method works.
Descriptive Analysis: Descriptive data analysis examines historical data to determine what occurred. This is commonly used to monitor Key Performance Indicators (KPIs), income, and sales leads, among other things.
Inferential Analysis: Inferential Analysis uses a sample of data from the original data to make inferences and estimates about large amounts of data. It draws conclusions based on probabilities. The term “inferential Analysis” refers to the method of “inferring” observations from a collection of data.
Diagnostic Analysis: The aim of diagnostic data analysis is to figure out why something happened. Once your descriptive analysis has shown that something negative or positive occurred, you will conduct a diagnostic analysis to determine the cause.
Predictive Analysis: Predictive data analysis forecasts what will most likely occur in the future. Trends are derived from historical data and used to make assumptions about the future in this type of analysis. For example, data from previous years will be analysed to forecast sales for the coming year. If revenue has increased by 25% per year for many years, revenue next year is expected to be 25% higher than this year. This is a basic example, but predictive analysis can be used to solve much more complex problems like risk evaluation, revenue forecasting, and lead qualification.
Prescriptive Analysis: Prescriptive data analysis brings together the findings from the previous types of data analysis to create a strategy for the company to address the problem or make a decision. The data-driven decisions are taken here.
Data Analysis Initial Phases
1.Data Cleaning: This is the first step in the data Analysis process, and it involves record matching, deduplication, and column segmentation to clean the raw data from various sources.
2.Quality Analysis: Using frequency counts, descriptive statistics like mean, standard deviation, and median, as well as normality histograms like skewness, kurtosis, and frequency, the n variables are compared to variables from outside the data set.
3.Measurement Quality: Confirmatory factor analysis and homogeneity analysis
4.Analysis: During the initial data analysis process, a variety of analysis may be performed.
• Single variable, univariate statistics
• Bivariate associations correlations.
• Graphical techniques scatter plots.
• Nominal and ordinal variables.
• Frequency is measured in percentages and numbers.
• Hierarchical loglinear analysis
• Loglinear analysis is used to identify critical variables and potential confounders.
• If subgroups are small, use exact tests or bootstrapping.
• Computation of new variables.
• Continuous variables
• Statistics – M, SD, variance, skewness and kurtosis.
• Stem and leaf displays.
• Box plots.