Data Analytics Tutorial | Data Analytics for Beginners
1. Data Analytics Tutorial
Today, in this Big Data Analytics Tutorial, we will learn about data analysis. Moreover, we will look at data analytics meaning, data analytics examples, and the various business intelligence tools for analyzing data. Also, we will learn about data mining, the difference between analysis vs reporting, and various data mining techniques. Along with this, we discuss features of data analysis, statistical significance and business importance in terms of business analysis and the skills required to learn data analytics and data analyst.
So, let’s start the Data Analysis Tutorial.
2. 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 characteristics and need for data analysis.
3. Analysis vs Reporting
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 difference between data analysis and data reporting in this Data Analytics Tutorial:
- 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.
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4. Data Analysis Process
Now in the Data Analytics Tutorial, we are going to see the analytic process or how analyzing data can be done?
a. 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.
b. 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.
c. 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 appropriate data. Data is selected, cleaned, and integrated into the format finalized for the analysis in this phase.
d. 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.
e. 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.
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.
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:
a. Classification of trees
These are Tree-shaped structures that represent sets of decisions.
b. Logistic regression
It predicts the probability of an outcome that can only have two values.
c. Neural networks
These are non-linear predictive models that resemble biological neural networks in structure and learn through training.
d. 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.
e. 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 Driven
- Attributes usage
Data analysis has the following characteristics:
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.
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.
c. 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.
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. 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:
a. 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.
b. 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.
8. Skills required to be a Data Analyst
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.
9. Technical & Business Skills for Data Analytics
In this part of Data analytics tutorial, we will discuss 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
So, this was all on Data Analytics Tutorial.
10. Summary for Data Analytics Tutorial
Hence, in this Data Analytics Tutorial, we discussed the introduction to Data Analytics. Moreover, we looked at Analysis vs Reporting and Data Analysis Process. Along with this, we discussed Data Mining and the characteristics of Data Analysis. Also, we looked at how to get better analysis and skills required for Data Analyst.
Still, if you have any question related to Data Analytics Tutorial, ask in the comment tab.
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