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Simran Kaur Arora | 09 Aug, 2023

# What is Data Analysis? Methods, Techniques & Tools

## What Is Data Analysis?

The systematic application of statistical and logical techniques to describe the data scope, modularize the data structure, condense the data representation, illustrate via images, tables, and graphs, and evaluate statistical inclinations, probability data, and derive meaningful conclusions known as Data Analysis. These analytical procedures enable us to induce the underlying inference from data by eliminating the unnecessary chaos created by its rest. Data generation is a continual process; this makes data analysis a continuous, iterative process where the collection and performing data analysis simultaneously. Ensuring data integrity is one of the essential components of data analysis.

There are various examples where data analysis is used, ranging from transportation, risk and fraud detection, customer interaction, city planning healthcare, web search, digital advertisement, and more.

Considering the example of healthcare, as we have noticed recently that with the outbreak of the pandemic, Coronavirus hospitals are facing the challenge of coping up with the pressure in treating as many patients as possible, considering data analysis allows to monitor machine and data usage in such scenarios to achieve efficiency gain.

Before diving any more in-depth, make the following pre-requisites for proper Data Analysis:

• Ensure availability of the necessary analytical skills
• Ensure appropriate implementation of data collection methods and analysis.
• Determine the statistical significance
• Check for inappropriate analysis
• Ensure the presence of legitimate and unbiased inference
• Ensure the reliability and validity of data, data sources, data analysis methods, and inferences derived.
• Account for the extent of analysis

## Data Analysis Methods

There are two main methods of Data Analysis:

### 1. Qualitative Analysis

This approach mainly answers questions such as ‘why,’ ‘what’ or ‘how.’ Each of these questions is addressed via quantitative techniques such as questionnaires, attitude scaling, standard outcomes, and more. Such analysis is usually in the form of texts and narratives, which might also include audio and video representations.

### 2. Quantitative Analysis

Generally, this analysis is measured in terms of numbers. The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.

The other techniques include:

### 3. Text analysis

Text analysis is a technique to analyze texts to extract machine-readable facts. It aims to create structured data out of free and unstructured content. The process consists of slicing and dicing heaps of unstructured, heterogeneous files into easy-to-read, manage and interpret data pieces. It is also known as text mining, text analytics, and information extraction.

The ambiguity of human languages is the biggest challenge of text analysis. For example, humans know that “Red Sox Tames Bull” refers to a baseball match. Still, if this text is fed to a computer without background knowledge, it would generate several linguistically valid interpretations. Sometimes people who are not interested in baseball might have trouble understanding it too.

### 4. Statistical analysis

Statistics involves data collection, interpretation, and validation. Statistical analysis is the technique of performing several statistical operations to quantify the data and apply statistical analysis. Quantitative data involves descriptive data like surveys and observational data. It is also called a descriptive analysis. It includes various tools to perform statistical data analysis such as SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Stat soft, and more.

### 5. Diagnostic analysis

The diagnostic analysis is a step further to statistical analysis to provide a more in-depth analysis to answer the questions. It is also referred to as root cause analysis as it includes processes like data discovery, mining, and drill down and drill through.

The diagnostic analysis is a step further to statistical analysis to provide a more in-depth analysis to answer the questions. It is also referred to as root cause analysis as it includes processes like data discovery, mining, and drill down and drill through.

The functions of diagnostic analytics fall into three categories:

• Identify anomalies: After performing statistical analysis, analysts are required to identify areas requiring further study as such data raise questions that cannot be answered by looking at the data.
• Drill into the Analytics (discovery): Identification of the data sources helps analysts explain the anomalies. This step often requires analysts to look for patterns outside the existing data sets. It requires pulling in data from external sources, thus identifying correlations and determining if they are causal in nature.
• Determine Causal Relationships: Hidden relationships are uncovered by looking at events that might have resulted in the identified anomalies. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.

### 6. Predictive analysis

Predictive analysis uses historical data and feds it into the machine learning model to find critical patterns and trends. The model is applied to the current data to predict what would happen next. Many organizations prefer it because of its various advantages like volume and type of data, faster and cheaper computers, easy-to-use software, tighter economic conditions, and a need for competitive differentiation.

The following are the common uses of predictive analysis:

• Fraud Detection: Multiple analytics methods improves pattern detection and prevents criminal behavior.
• Optimizing Marketing Campaigns: Predictive models help businesses attract, retain, and grow their most profitable customers. It also helps in determining customer responses or purchases, promoting cross-sell opportunities.
• Improving Operations: The use of predictive models also involves forecasting inventory and managing resources. For example, airlines use predictive models to set ticket prices.
• Reducing Risk: The credit score used to assess a buyer’s likelihood of default for purchases is generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

### 7. Prescriptive Analysis

Prescriptive analytics suggests various courses of action and outlines the potential implications that could be reached after predictive analysis. Prescriptive analysis generating automated decisions or recommendations requires specific and unique algorithmic and clear direction from those utilizing the analytical techniques.

## Data Analysis Process

Once you set out to collect data for analysis, you are overwhelmed by the amount of information you find to make a clear, concise decision. With so much data to handle, you need to identify relevant data for your analysis to derive an accurate conclusion and make informed decisions. The following simple steps help you identify and sort out your data for analysis.

### 1. Data Requirement Specification - define your scope:

• Define short and straightforward questions, the answers to which you finally need to make a decision.
• Define measurement parameters
• Define which parameter you take into account and which one you are willing to negotiate.
• Define your unit of measurement. Ex – Time, Currency, Salary, and more.

### 2. Data Collection

• Collect data from databases, websites, and many other sources. This data may not be structured or uniform, which takes us to the next step.

### 3. Data Processing

• Organize your data and make sure to add side notes, if any.
• Cross-check data with reliable sources.
• Convert the data as per the scale of measurement you have defined earlier.
• Exclude irrelevant data.

### 4. Data Analysis

• Once you have collected your data, perform sorting, plotting, and identifying correlations.
• As you manipulate and organize your data, you may need to traverse your steps again from the beginning. You may need to modify your question, redefine parameters, and reorganize your data.
• Make use of the different tools available for data analysis.

### 5. Infer and Interpret Results

• Review if you have considered all parameters for making the decision
• Review if there is any hindering factor for implementing the decision.
• Choose data visualization techniques to communicate the message better. These visualization techniques may be charts, graphs, color coding, and more.

Once you have an inference, always remember it is only a hypothesis. Real-life scenarios may always interfere with your results. In Data Analysis, there are a few related terminologies that identity with different phases of the process.

#### 1. Data Mining

This process involves methods in finding patterns in the data sample.

#### 2. Data Modelling

This refers to how an organization organizes and manages its data.

## Data Analysis Techniques

There are different techniques for Data Analysis depending upon the question at hand, the type of data, and the amount of data gathered. Each focuses on taking onto the new data, mining insights, and drilling down into the information to transform facts and figures into decision-making parameters. Accordingly, the different techniques of data analysis can be categorized as follows:

### 1. Techniques based on Mathematics and Statistics

• Descriptive Analysis: Descriptive Analysis considers the historical data, Key Performance Indicators and describes the performance based on a chosen benchmark. It takes into account past trends and how they might influence future performance.
• Dispersion Analysis: Dispersion in the area onto which a data set is spread. This technique allows data analysts to determine the variability of the factors under study.
• Regression Analysis: This technique works by modeling the relationship between a dependent variable and one or more independent variables. A regression model can be linear, multiple, logistic, ridge, non-linear, life data, and more.
• Factor Analysis: This technique helps to determine if there exists any relationship between a set of variables. This process reveals other factors or variables that describe the patterns in the relationship among the original variables. Factor Analysis leaps forward into useful clustering and classification procedures.
• Discriminant Analysis: It is a classification technique in data mining. It identifies the different points on different groups based on variable measurements. In simple terms, it identifies what makes two groups different from one another; this helps to identify new items.
• Time Series Analysis: In this kind of analysis, measurements are spanned across time, which gives us a collection of organized data known as time series.

### 2. Techniques based on Artificial Intelligence and Machine Learning

• Artificial Neural Networks: a Neural network is a biologically-inspired programming paradigm that presents a brain metaphor for processing information. An Artificial Neural Network is a system that changes its structure based on information that flows through the network. ANN can accept noisy data and are highly accurate. They can be considered highly dependable in business classification and forecasting applications.
• Decision Trees: As the name stands, it is a tree-shaped model representing a classification or regression model. It divides a data set into smaller subsets, simultaneously developing into a related decision tree.
• Evolutionary Programming: This technique combines the different types of data analysis using evolutionary algorithms. It is a domain-independent technique, which can explore ample search space and manages attribute interaction very efficiently.
• Fuzzy Logic: It is a data analysis technique based on the probability that helps handle the uncertainties in data mining techniques.

### 3. Techniques based on Visualization and Graphs

• Column Chart, Bar Chart: Both these charts are used to present numerical differences between categories. The column chart takes to the height of the columns to reflect the differences. Axes interchange in the case of the bar chart.
• Line Chart: This chart represents the change of data over a continuous interval of time.
• Area Chart: This concept is based on the line chart. It also fills the area between the polyline and the axis with color, representing better trend information.
• Pie Chart: It is used to represent the proportion of different classifications. It is only suitable for only one series of data. However, it can be made multi-layered to represent the proportion of data in different categories.
• Funnel Chart: This chart represents the proportion of each stage and reflects the size of each module. It helps in comparing rankings.
• Word Cloud Chart: It is a visual representation of text data. It requires a large amount of data, and the degree of discrimination needs to be high for users to perceive the most prominent one. It is not a very accurate analytical technique.
• Gantt Chart: It shows the actual timing and the progress of the activity compared to the requirements.
• Radar Chart: It is used to compare multiple quantized charts. It represents which variables in the data have higher values and which have lower values. A radar chart is used for comparing classification and series along with proportional representation.
• Scatter Plot: It shows the distribution of variables in points over a rectangular coordinate system. The distribution in the data points can reveal the correlation between the variables.
• Bubble Chart: It is a variation of the scatter plot. Here, in addition to the x and y coordinates, the bubble area represents the 3rd value.
• Gauge: It is a kind of materialized chart. Here the scale represents the metric, and the pointer represents the dimension. It is a suitable technique to represent interval comparisons.
• Frame Diagram: It is a visual representation of a hierarchy in an inverted tree structure.
• Rectangular Tree Diagram: This technique is used to represent hierarchical relationships but at the same level. It makes efficient use of space and represents the proportion represented by each rectangular area.
• Map
• Regional Map: It uses color to represent value distribution over a map partition.
• Point Map: It represents the geographical distribution of data in points on a geographical background. When the points are the same in size, it becomes meaningless for single data, but if the points are as a bubble, it also represents the size of the data in each region.
• Flow Map: It represents the relationship between an inflow area and an outflow area. It represents a line connecting the geometric centers of gravity of the spatial elements. The use of dynamic flow lines helps reduce visual clutter.
• Heat Map: This represents the weight of each point in a geographic area. The color here represents the density.

Let us now read about a few tools used in data analysis in research.

## Data Analysis Tools

There are several data analysis tools available in the market, each with its own set of functions. The selection of tools should always be based on the type of analysis performed and the type of data worked. Here is a list of a few compelling tools for Data Analysis.

### 1. Excel

It has various compelling features, and with additional plugins installed, it can handle a massive amount of data. So, if you have data that does not come near the significant data margin, Excel can be a versatile tool for data analysis.

Looking to learn Excel? Data Analysis with Excel Pivot Tables course is the highest-rated Excel course on udemy.

### 2. Tableau

It falls under the BI Tool category, made for the sole purpose of data analysis. The essence of Tableau is the Pivot Table and Pivot Chart and works towards representing data in the most user-friendly way. It additionally has a data cleaning feature along with brilliant analytical functions.

If you want to learn Tableau, udemy's online course Hands-On Tableau Training For Data Science can be a great asset for you.

### 3. Power BI

It initially started as a plugin for Excel, but later on, detached from it to develop in one of the most data analytics tools. It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can implement sophisticated advanced analytics similar to writing Excel formulas.

### 4. Fine Report

Fine Report comes with a straightforward drag and drops operation, which helps design various reports and build a data decision analysis system. It can directly connect to all kinds of databases, and its format is similar to that of Excel. Additionally, it also provides a variety of dashboard templates and several self-developed visual plug-in libraries.

### 5. R & Python

These are programming languages that are very powerful and flexible. R is best at statistical analysis, such as normal distribution, cluster classification algorithms, and regression analysis. It also performs individual predictive analyses like customer behavior, spending, items preferred by him based on his browsing history, and more. It also involves concepts of machine learning and artificial intelligence.

### 6. SAS

It is a programming language for data analytics and data manipulation, which can easily access data from any source. SAS has introduced a broad set of customer profiling products for web, social media, and marketing analytics. It can predict their behaviors, manage, and optimize communications.

## Conclusion

This is our complete beginner's guide on "What is Data Analysis". If you want to learn more about data analysis, Complete Introduction to Business Data Analysis is a great introductory course.

Data Analysis is the key to any business, whether starting up a new venture, making marketing decisions, continuing with a particular course of action, or going for a complete shut-down. The inferences and the statistical probabilities calculated from data analysis help base the most critical decisions by ruling out all human bias. Different analytical tools have overlapping functions and different limitations, but they are also complementary tools. Before choosing a data analytical tool, it is essential to consider the scope of work, infrastructure limitations, economic feasibility, and the final report to be prepared.

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By Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.

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