So, what is Java programming Java programming? I will say according to Wikipedia, Java is a general purpose computer programming language. So, I will say in general Java is a high level object oriented programming language developed by or a kind some microsystem. java is designed based on C sharp programming, Bobby more extensions, Java, Java application or Java ah Java JAR files can be run on many operating system or platform including windows Mac or as and nuts as long as the operating system have the GIMP installed. Okay, so why is high level program In language, I will say that high level programming language is more towards the more similar to the human language and then low level programming language is more towards the machine language. So, for Java programming Java is a high level programming language and then for machine learning machine language you will be more more or less the assembly language.

So, I will say a high level programming language human language then machine language is something I will say binary like binary objects in 0101 and so on. So, I in this diagram shows why is the high level and low level programming languages object oriented program I'm in the procedural programming. I will say that our procedural programming like PCC programming is like writing a essay. So, the machine will or the compiler or interpreter execute program of course line by line. So, in general procedural programming language emphasize more on the step to end completions tasks, user has really control for object oriented programming. Java is one of the object oriented programming language.

C sharp and c++ is also object oriented programming language. So, for object oriented programming language, I will say that you represent more of the real world objects so, I will say object oriented programming I emphasize more on the use of objects and classes in programming and user has more control Okay, why is data size So, according to Wikipedia data size is more more the interdisciplinary field to extract knowledge or insight from data in various forms. I will say in data science, we usually create a data product. So, I in data science we need or we need to have the skill set in mathematics or statistics and we need to have the skill set in computer science or machine learning. And then domain expertise is based on the data we are Analyzing or processing. So, I will say if the data is more for the business is business data then domain expertise will be the business knowledge or expertise.

So, what is data mining data mining I will say is small closely related to data science. Data Mining can be is a process to discover patterns from data using machine learning statistics and database or data warehouse usually result in the equation or models for mechina prediction based on all data's So, in data mining. Firstly, we have the problem definition. Then we have data gathering and preparation. Then we have the model building and the evaluation and then we have Knowledge deployment okay ah what is proper definition I will say proper initiation is what is the problem we are trying to solve and why is the knowledge we want to stretch or analyze from the data. So, data gathering I will say I still collect our combined data from different sources and data preparation I will say is more or less on cleaning the data transforming the data.

So, for example, I will say is duplicate remove a combination of textual categorical variables to numeric categorical variables and data preparation we can have a feature of the robust selections and then, in data preparation we can also be doing some of the data visualizations and in model building I will say he is more or less on on the prediction modeling ah he will say predictive analytics. You can also say modeling is also on descriptive model the prescriptive model. So, usually data scientists or data analysis will build a prediction model trained on using the given data set to predict a variable. So, for model evaluation model evaluation is we want to see how accurate or how precise is the model that we created. And then for the last issue is the knowledge deployment. So, knowledge the primer can also mean that we apply the model.

Or we can also mean that we convert the model based on the model, we create a recommendation or prediction system. Or it can also mean that we use the model to create some other data product. So what is text mining? data mining? Usually we deal with numerical data that's mining. On the other hand, we deal with more or less on the texture data.

So that's mining uses. The same process for data mining, we have differences. And now Ah, in data mining, we usually use statistics or data analysis. In text mining, we usually be using our natural language processing. In s mining, we as strategy documents and transformation. We prepare the data we prepare the test.

So the textual data can be cleaned or transformed Musina Staller remover or stemming. And then we can also have some other data visualization using natural language processing. And then for feature extraction and dimension reduction, Ah, that's my knee I will say. We also have this feature. So we can be selecting, let's say, in in the law, the more importance we're all the same The author text or it can also mean that we select only the required variables. Then we have the regression classification or clustering This is more or less on the prediction modeling.

So, we can create a model prediction model to predict a variable or Category A categorical variable based on the texture input. So, I will say that three types of analytics descriptive analytics is like the using of these statistics or the descriptive statistics for the analyzing or understanding of the data. So, in descriptive analytics, you can be using descriptive statistics. I will say we may be also using inferential statistics and the regressions analysis all depends on the problem and then we can have also some other data visualizations. Then for predictive analytics, we usually try to predict one or the variables in the data. So, to predict a variable i will say we usually use the machine learning algorithm or the statistical and in AI algorithm.

So, forced statistical learning algorithm ID linear regressions. So, we want to predict a variable the variable book can be the Y. So y equals we'll say y equals mx plus c. So, I will say we train the model and found find find em and see me try to use this model to predict a variable. I will say in linear regression I will be y okay and then prescriptive analytics I will say is the essay we want to have a certain outcome or certain output y variables that we need to employ to get the outcomes. So, we can adjust variables in Book and then see why a more numerical level of the input we need to have to get the outcomes that we want. So, all three types of analysis can be developed by to model using the pattern, our SPSS, SPSS modeler, rapid miner, and so as a straw, it can be using our SAS Enterprise, enterprise mind No.

So okay, why is big data I will say big data is data, data is very huge, very large, and one computer cannot be used to process Stata data. So there are three properties characteristic v data. So, the first property of the velocity, velocity is the amount of the data grows continuously over time, then our volume is the amount of data that is huge that requires more computers to store and then very t there are many variation of variables in the data ah either closely relate or local, not closely related. So, how do we deal with the data usually for BD ta V, you use the ah I will say is the parallel system or the distributor system. Let's say using the Hadoop Apache Spark. I will see this Hadoop and Apache Spark users cluster computers in one cluster we have a lot of computers.

So, how do patches usually will be used to process or analyze the data. So, why learn Java programming language? Java programming language I will say Java is one of the top top 10 programming language and then do due to the development of NetBeans Java learning curve has been reduced. And then lastly I will see the Integrated Development Environment allow Java programming to offer fast and easy way to prototype your ideas. One final one I will say Java programming language Java application it can be run on the many operating system. So, let's say we create a Java application, then we can run this application on the nuts, Windows or the Mac OS as long as these are not on Windows or Mac OS has the GI runtime Runtime Environment installed.

So, in conclusion, we have completed the first chapter. So I will see in this chapter we have covered why's Java, why is high level low level programming language? Why is object oriented and procedural programming language? Why is data science? Why is data mining wise My name I will say also he touched on why is Javi data and then we also touch on why reasons for the needy Java programming. So I will see a in nice chapter we will be looking to install installing the NetBeans getting our computer ready for doing Java programming