Data Science and Data Analytics are two buzz words of the year. Today, data is more than oil to the industries. Data is collected into raw form and processed according to the requirement of a company and then take this data for the decision making purpose. All this process, helps the business to grow in the market. But, who will do this work? Who will process the data? etc. Everything is done by a Data Analytics and a Data Scientist. This Data Analytics tutorial if specially designed for beginners, to provide information about Data Analytics from scratch to the end.
So, let’s start the Data Analysis Tutorial.
1. What is Data Analytics?
Data or information is in raw format. With increasing data size, it has become a need for inspecting, cleaning, transforming, and modeling data with the goal of finding useful information, making conclusions, and supporting decision making. This process is known as data analysis.
Data mining is a particular data analysis technique where modeling and knowledge discovery for predictive rather than purely descriptive purposes is focused. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide business analytics into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
So, the Data wave has changed the ways in which industries function. With Big Data has emerged the requirement to implement advanced analytics to it. Now experts can make more accurate and profitable decisions.
In this session of Data Analytics tutorial for beginners, we are going to see everything which is related to data analytics.
2. Data Analysis vs Reporting
The analysis is an interactive process of a person tackling a problem, finding the data required to get an answer, analyzing that data, and interpreting the results in order to provide a recommendation for action.
A reporting environment or business intelligence (BI) environment involves calling and execution of reports. So, outputs are then printed in the desired form.Reporting refers to the process of organizing and summarizing data in an easily readable format to communicate important information. Reports help organizations in monitoring different areas of performance and improving customer satisfaction. In other words, you can consider reporting as the process of converting raw data into useful information, while analysis transforms information into insights.
Difference between Data analysis and Data Reporting
So, let us understand the comparison between data analysis and data reporting
- A report will show the user what had happened in the past, to avoid inferences and help to get a feel for the data while analysis provides answers to any question or issue. An analysis process takes any steps needed to get the answers to those questions.
- Reporting just provides the data that ask for while analysis provides the information or the answer that need actually.
- We perform the reporting in a standardized way, but we can customize the analysis. There are fixed standard formats for reporting while we perform the analysis as per the requirement; we customize it as needed.
- We can do Reporting using a tool and it generally does not involve any person while in the analysis. A person requires who is doing analysis and who will lead the process. He guides the complete analysis process.
- Reporting is inflexible while analysis is flexible. Reporting provides no or limited context about what’s happening in the data and hence is inflexible while analysis emphasizes data points that are significant, unique, or special, and it explains why they are important to the business.
Do you know – What is Data Science and how it is used?
3. Data Analysis Process
Now in the Data Analytics Tutorial, we are going to see the analytic process or how analyzing data can be done?
3.1. Business Understanding
The very first step consists of business understanding. Whenever any requirement occurs, firstly we need to determine the business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.
3.2. Data Exploration
The second step consists of Data understanding. For the further process, we need to gather initial data, describe and explore the data and verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and the need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.
3.2. Data Preparation
Next, come Data preparation. From the data collected in the last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally, we need to format the data to get the appropriate data. Data is selected, cleaned, and integrated into the format finalized for the analysis in this phase.
Also, refer – Data Science Vs Data Analytics
3.3. Data Modeling
Once data is gathered, we need to do data modeling. For this, we need to select a modeling technique, generate test design, build a model and assess the model built. The data model is build to analyze relationships between various selected objects in the data, test cases are built for assessing the model and model is tested and implemented on the data in this phase.
3.4. Data Evaluation
Next is data evaluation, where we evaluate the results from the last step, review the scope of error, and determine the next steps to perform. We evaluate the results of the test cases and review the scope of errors in this phase.
3.5. Deployment
The final step in the analytic process is deployment. Here we need to plan the deployment and monitoring and maintenance, we need to produce a final report and review the project. In this phase, we deploy the results of the analysis. This is also known as reviewing the project.
We call the above process as business analytics process.
4. Types of Data Analysis
There are 4 types of techniques used for Data Analysis are-
4.1 Descriptive Analysis
With the help of descriptive analysis, we analyze and describe the features of a data. Descriptive Analysis deals with the summarization of information. Descriptive Analysis, when coupled with visual analysis provides us with a comprehensive structure of data.
In the descriptive analysis, we deal with the past data to draw conclusions and present our data in the form of dashboards. In businesses, Descriptive Analysis is used for determining the Key Performance Indicator or KPI to evaluate the performance of the business.
4.2 Predictive Analysis
With the help of predictive analysis, we determine the future outcome. Based on the analysis of the historical data, we are able to forecast the future. It makes use of descriptive analysis to generate predictions about the future. With the help of technological advancements and machine learning, we are able to obtain predictive insights about the future.
Predictive Analytics is a complex field that requires a large amount of data, skilled implementation of predictive models and its tuning to obtain accurate predictions. This requires a skilled workforce that is well versed in machine learning to develop effective models.
4.3 Diagnostic Analysis
At times, businesses are required to think critically about the nature of data and understand the descriptive analysis in depth. In order to find issues in the data, we need to find anomalous patterns that might contribute towards the poor performance of our model.
With diagnostic analysis, you are able to diagnose various problems that are exhibited through your data. Businesses use this technique to reduce their losses and optimize their performances. Some of the examples where businesses use diagnostic analysis are –
- Businesses implement diagnostic analysis to reduce latency in logistics and optimize their production process.
- Using diagnostic analysis in sales to update marketing strategies that would otherwise lead to a fall in revenue.
4.4. Prescriptive Analysis
Prescriptive Analysis combines the insights from all of the above analytical techniques. It is referred to as the final frontier of data analytics. Through the details provided by the descriptive and predictive analytics, prescriptive analytics allows the companies to make decisions based on them. It makes heavy usage of artificial intelligence in order to facilitate companies into making careful business decisions.
Major industrial players like Facebook, Netflix, Amazon, and Google are using prescriptive analytics to make key business decisions. Furthermore, financial institutions are gradually leveraging the power of this technique to increase their revenue.
5. Introduction to Data Mining
Data mining also called data or knowledge discovery means analyzing data from different perspectives and summarizing it into useful information – information that we can use to take important decisions. So, we discuss it in this Data Analytics tutorial. It is the technique of exploring, analyzing, and detecting patterns in large amounts of data. The goal of data mining is either data classification or data prediction. In classification, we sort the data into groups while in prediction, predict the value of a continuous variable.
In today’s world, data mining use in several sectors like Retail, sales analytics, Financial, Communication, Marketing Organizations etc. For example, a marketer may want to find who did and did not respond to a promotion. In prediction, the idea is to predict the value of a continuous (ie non-discrete) variable; for example, a marketer may be interested in finding who will respond to a promotion.
Some examples of Data Mining are:
5.1. Classification of trees
These are Tree-shaped structures that represent sets of decisions.
5.2. Logistic regression
It predicts the probability of an outcome that can only have two values.
5.3. Neural networks
These are non-linear predictive models that resemble biological neural networks in structure and learn through training.
5.4. Clustering techniques like the K-nearest neighbors
This is the technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes we call it the k-nearest neighbor technique.
5.5. Anomaly detection
It is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.
6. Characteristics of Data Analysis
We have already seen characteristics of Big Data like volume, velocity, and variety. Let us now see in this Data Analytics Tutorial, characteristics of Data Analytics which make it different from traditional kind of analysis.
Data analysis has the following characteristics:
6.1. Programmatic
There might need to write a program for data analysis by using code to manipulate it or do any kind of exploration because of the scale of the data.
6.2. Data-driven
It means progress in an activity compel by data and program statements describe the data that match and the processing require rather than taking steps of defining a sequence. Many analysts use a hypothesis-driven approach to data analysis, Data can use the massive amount of data to drive the analysis.
Read more about – Hypothesis Testing Using R
6.3. Attributes usage
For proper and accurate analysis of data, it can use a lot of attributes. In the past, analysts dealt with hundreds of attributes or characteristics of the data source, with Big Data there are now thousands of attributes and millions of observations.
6.4. Iterative
As whole data is broken into samples and samples are then analyzed, data analytics can be iterative in nature. More compute power enables iteration of the models until Data analysts are satisfied. This has led to the development of new applications designed for addressing analysis requirements and time frames.
7. Applications of Data Analysis
Following are some of the most popular applications of data analysis –
7.1 Fraud Detection & Risk Analytics
In Banking, Data Analytics is heavily utilized for analyzing anomalous transaction and customer details. Banks also use data analytics to analyze loan defaulters and credit scores for their customers in order to minimize losses and prevent frauds.
7.2 Optimizing Transport Routes
Companies like Uber and Ola are heavily dependent on data analytics to optimize routes and fare for their customers. They use an analytical platform that analyzes the best route and calculates percentage rise and drop in taxi fares based on several parameters.
7.3. Providing Better Healthcare
With the help of data analytics, hospitals and healthcare centers are able to predict early onset of chronic diseases. They are able to predict diseases that might occur in the future and help the patients to take early action that would help them to reduce medical expenditure.
7.4. Managing Energy Expenditure
Public-sector energy companies are using data analytics to monitor the usage of energy by households and industries. Based on the usage patterns, they are optimizing energy supply in order to reduce costs and cut down on energy consumption.
7.5. Improving Search Results
Companies like Google are using data analytics to provide search results to users based on their preferences and search history. Furthermore, companies like Airbnb use search analytics to provide the best accommodation to its customers. Amazon also makes use of search analytics to provide recommendations to customers.
7.6. Optimization of Logistics
Various companies are relying on Big Data Analytics to analyze supply chains and reduce latency in logistics. Companies like Amazon are using consumer analytics to analyze their requirements and send them products without any latency.
8. How to Get a Better Analysis?
In order to have a great analysis, it is necessary to ask the right question, gather the right data to address it, and design the right analysis to answer the question. Then only analysis we can call as correct and successful. So, let’s discuss this in detail in this Data Analytics tutorial for beginners.
Recommended Reading – Top Data Analysis Tools
The framing of a problem means ensuring that must ask important questions and layout critical assumptions. For example, is the goal of a new initiative to drive more revenue or more profit? The choice leads to a huge difference in the analysis and actions that follow. Is all the data required available, or is it necessary to collect some more data? Without framing the problem, the rest of the work is useless.
For a great analysis, we frame the problem correctly. So, this includes assessing the data correctly, developing a solid analysis plan, and taking into account the various technical and practical considerations in play.
We can analyze any business problem for 2 issues:
8.1. Statistical Significance
How the problem is statistically important for decision making. Statistical significance testing takes some assumptions and determines the probability of happening of results if the assumptions are correct.
8.2. Business Importance
It means how the problem is related to business and its importance. Always put the results in a business context as part of the final validation process.
9. Skills required to be a Data Analyst
Data Analytics tutorial is incomplete without discussing the skills. In today’s world, there is an increasing demand for analytical professionals. It is taking time for academic programs to adapt and scale to develop more talent.
All the data collected and the models created are of no use if the organization lacks skilled Data analysts. A Data analyst requires both skill and knowledge for getting good data analytics jobs.
Must Read – 5 Skills needed to become a Data Scientist
To be a successful analyst, a professional requires expertise on the various data analytical tools like R & SAS. He should be able to use these business analytics tools properly and gather the required details. He should also be able to take decisions which are both statistically significant and important to the business.
Even if you know how to use a data analysis tool of any type, you also need to have the right skills, experience and perspective to use it. An analytics tool may save a user some programming but he or she still needs to understand the analytics that occurs. Then only we can call a person as a successful Data analyst.
Business people with no analytical expertise may want to leverage analytics, but they do not need to do the actual heavy lifting. The job of the analytics team is to enable business people to drive analytics through the organization. Let business people spend their time selling the power of analytics upstream and changing the business processes they manage to make use of analytics. If analytics teams do what they do best and business teams do what they do best, it will be a winning combination.
10. Technical & Business Skills for Data Analytics
In this part of Data analytics tutorial, we will discuss the required technical and business skills.
Technical skills for Data analytics –
- Packages and Statistical methods
- BI Platform and Data Warehousing
- Data base design
- Data Visualization and munging
- Reporting methods
- Knowledge of Hadoop and MapReduce
- Data Mining
Business Skills Data analytics –
- Effective communication skills
- Creative thinking
- Industry knowledge
- Analytic problem solving
11. Introduction of Big Data Analytics
Big Data Analytics has transformed the way industries perceived data. Traditionally, companies made use of statistical tools and surveying to gather data and perform analysis on the limited amount of information. Most of the times, the deductions and inferences that were produced based on the information were not adequate and did not lead to positive results. Because of this, companies had to incur losses.
However, with the advancements in technology and a massive increase in the computational capabilities contributed by High-Performance Computing, industries are able to expand their domain of knowledge. What comprised of a few gigabytes in the past is now in the size of quintillions. This is contributed by the massive expanse in mobile phones, IoT devices and other internet services. To make sense of this, Industries have resorted to Big Data Analytics.
A Big Data Analytics platform is a comprehensive platform that provides both the analytical capabilities as well as massive storage capacity. Some popular Big Data tools like Hadoop, Spark, Flink and Kafka have the capability to not only store massive bulk of data but also perform analysis on the data. As a result, they provide comprehensive solutions to companies with their big data needs.
So, this was all on Data Analytics Tutorial.
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