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Which Programming Language to Learn as a Beginner

If you are aware of job trends around the world, then you probably know that there has been an increase in demand for software programming jobs. In the past decade, there has been a drastic increase in software jobs, approximately an increase of 31%. It is an industry that is predicted to continue to grow until 2030.

Deciding to go into programming is tough because you have a wide-variety of options in terms of program languages you can learn. However, before you decide on which program you want to learn, it is important you figure out why you want to learn coding? If you are just looking to work on websites then you can learn basic coding such as HTML or something more complicated like PHP or CSS which are in demand today. However, if you want to go into application and software development then you have a tough decision to make.

When selecting the most suitable language for yourself, it is important to take into account the difficulty of the language and the demand for it in the market. So keeping both these in mind, which programming language is great for a beginner? C is probably the best option if you are a beginner looking to learn a programming language.

Why C?

C is a programming language that was developed by Microsoft and mostly runs on Windows. The language allows you to work with games, web development, and other development work within Microsoft. Initially, the language was just for Windows. However, Xamarin has developed Mono which is an open source project that allows for C to be ported to different platforms. So with it, it is easier to use the language for mobile applications for both iOS and Android.

Knowing C will also present you with the option of learning C++ since that is based on C. It is more powerful and is the language that is behind numerous applications and games that we have on our computer and mobiles.

If we look at the demand of programmers that know C or C++ the two tied with JavaScript in terms of jobs posted with rough 20% of programming jobs based on them in 2017. While there is a huge market for javascript developers there is still a lack of C programmers so the demand is high for it while the supply of developers is still low.

Data Mining Jobs for Pros

With the rise in e-commerce and the field of IT, we have seen a huge increase in data that is out there for organizations to just pick up. This has resulted in the creation of data mining, which requires data miners to use their technical skills to mine the data in the online world. While the field is still growing, it is part of computer science and considered to be on the business intelligence side of it. It is a major part of computer science as the data collected by data miners allows them to provide predictions for businesses on demand of products, services, along with the human resource talent. So many big businesses have started to employ data miners because they can help enhance their business.

Types of jobs related to data mining for profs

The most popular position in the field of data mining is that of an analyst. A data mining analyst is sought by a wide variety of industries. Their main job is to analyze data to help the industry to further enhance their business by identifying data sources, predicting patterns in the industry, synthesizing the data set, and presenting the information in an easy to understand manner to the organization that will help with their decision making. Data mining analyst is fairly popular in education, engineering, and government services.

Data engineer is the second most popular position in the data mining field. They work more like the traditional researcher and business analyst. With the collection of data, they can help identify problems for businesses, work out improvement for products and services, and tell organizations what their business requirements are.

The final job that is popular in data mining is that of a big data architect. They don’t really work on the collections and analysis of data but rather focus on the strategic plan and design of data. They design the IT system which basically allows for analysts and engineer to easily collect the data they need for their job.

Future of data mining

Since 2010, data mining has become a relevant field as businesses have realized how it can help transform their businesses and presents them with a chance to rise above their competition. The demand for a data analyst, engineers, and architect has been on a rise ever since as you see industries from IT firms to fashion utilize the help of these individuals to enhance their business.

Selenium Web Scraping Tutorial

Web scraping allows you to extract data from websites. The process is automatic in which the HTML is processed to extract data that can be manipulated and converted to the format of your liking for retrieval and or analysis. The process is commonly used for data mining.

What is Selenium?

Selenium is an automation tool for web browsers. It is primarily used for testing of websites, allowing you to test it before you put it live. It gives you the chance to perform the following tasks on the website:

  • Click buttons
  • Enter information within the website, forms
  • Search for information on the website

It is a tool that has been used for scraping website. But you must note that if you scrape a website too often, you risk the chance of having your IP banned from the website so approach with caution.

How to scrape with Selenium?

In order to scrape websites with Selenium you will need Python, either Python3.x. or Python2.x. Once you have that downloaded you will need the following driver and package:

Selenium package – allows you to interact with website from Python

Chrome Driver – a platform to perform and launch tasks on browser

Virtualenv – helps create an isolated Python environment

  1. In Python, you need to create a new project. You can create a file and name it setup.py and within it type in selenium as dependency.
  2. Then open the command line and you will need to create a virtual environment by typing the following command: $ virtualenv webscraping_example
  3. You will now need to run the dependency on virtualenv, you can do this by typing the following command in the terminal: $(webscraping_example) pip install -r setup.py
  4. Now going back to the folder in Python, create another file and you can name it, webscraping_example.py. Once done, you need to add the following code snippets:
    1. from selenium import webdriver
    2. from selenium.webdriver.common.by import By
    3. from selenium.webdriver.support.ui import WebDriverWait
    4. from selenium.webdriver.support import expected_conditions as EC
    5. from selenium.common.exceptions import TimeoutException
  5. You then need to put Chrome in Incognito mode, this is done in the webdriver by adding the incognito argument:
    1. option = webdriver.ChromeOptions()
    2. option.add_argument(“ — incognito”)
  6. You will then create a new instance with this code: browser = webdriver.Chrome(executable_path=’/Library/Application Support/Google/chromedriver’, chrome_options=option)
  7. You can now start making request you pass in the website url you want to scrape.
  8. You may need to create a user account with Github to do this but that is an easy process.
  9. You are now ready to scrape the data from the website.

WebPlotDigitizer Review

Our rating: 4.4 out of 5

Pros

  • Makes working with image graphs much easier
  • Fairly easy to use, once you get the hang of it

Cons

  • Not the most user-friendly software
  • Very dull design

WebPlotDigitizer is not a program that just anyone can use or even a one that you may need. However, if you work with a graph or are an engineer, it is definitely a program you should consider using. The program isn’t too old but has been out for enough years that it has a bit of following.

The program will not be something you use every day, but it is nice to have in your reserve for the times you need it. It does make your work a lot easier. If you work with graphs on a daily basis then this software is definitely one you should download. WebPlotDigitizer helps easily digitize image graphs into numerical data. You can work with any type of graph or map from bar to ternary diagram and it will extract the data for you to easily analyze.

So how does it work?

You should know that the program is not completely automatic, don’t expect to take a picture and all the data will appear. You can import the graph in form of an image, then you need to select specific points on the line and then go over the line so the points can be picked up by the program.

Is it easy to use?

Well, you don’t need to be a rocket scientist to use the program. It isn’t difficult to use but you will need to be a bit tech savvy to use the program. It took us a few tries to get the graph to properly digitalize the data but once you get the hang of it, it is fairly easy to use.

If you deal with a lot of graphs and want a program that can help you digitize the data with a bit of input from your end. It does make the process a lot easier if you have a graph in image format and need to extract the data. If not, then there is no point of having the software.

Data Mining Methods for Beginners

The term data mining has become so widely used that we are most likely going to find at least one shared article on the topic in our social media news feed. In fact, the extent of its overuse has often led to misunderstandings of what it is or they are explained in a difficult-to-understand manner that, in the end, we might as well be reading gibberish.

Technically, data mining is the process of finding certain information from a compilation of data and presenting the usable information in the hopes of resolving a specific problem. In a nutshell, data mining is the act of examining large database sets to create new information. There are different services involved in the process, such as text mining, web mining, audio and video mining, visual data mining, and social network data mining.

There are many major data mining techniques in development, and recent data mining projects include association, clustering, prediction, sequential patterns and decision tree. This guide will provide a brief examination of each of these techniques.

Association

Association is perhaps one of the more popular data mining techniques used today. In association, the user is attempting to discover a pattern based on uncovered links between items of a singular transaction. This is the reason why the association technique is also commonly referred to as relation technique. This technique is widely used in market basket analysis with the aim of identifying a set of products that consumers frequently purchase in one transaction.

Retail companies use the association technique to study the psychological decision-making process behind their customer’s purchases. For example, when looking at past sales data, companies might discover that customers who buy chips will also buy beer. Therefore, the company will put beers and chips in the same shopping aisle or in relatively close distances from one another. This could be a way of efficient shopping for customers and ultimately increase sales.

Classification

Classification is a traditional data mining technique based on machine learning. Essentially, this technique is utilized for classifying each item in a dataset into one single predefined set of groups. The classification technique uses mathematical approaches such as decision trees, linear programming, statistics and neural network.

In this technique, the user develops software to learn how to organize items into groups. For instance, classification technique can be applied in the application that “looking at records of employees who have left the company, predict who will leave the company next.” In this case, we separate records of employees into two classes named “remain” and “gone.” Our data mining software then classifies the employees into their groups based on their probability of exiting the company.

Clustering

In data mining, clustering refers to the process of categorizing a particular set of objects by looking at their characteristics and separating them based on their similarities. The clustering technique sets the classes and places each object in their respective class, whereas in the classification technique, objects are assigned into predefined categories.

To make things clearer, let’s look at the example of a library’s book management system. In a library, there is a large selection of books on a number of topics. The posing challenge is how to organize the books in a way so visitors can pick up several books on a certain topic without having to walk around the whole library. With the clustering technique, one cluster – or in this case, shelf – contains all books that are about a particular topic, and the cluster is given a meaningful, understandable name. If readers need to take a book on that topic, they just have to head to the aisle where those books are located instead of searching the entire building.

Prediction

As the name suggest, the prediction technique aims to discover the link between independent variables and define the relationship between independent and dependent variables. For example, this technique is can be used to predict future profits if sales are set as the independent variable and profits as the dependent variable. Using past sale and profit data, the user can draw a regression curve for predicting profits.

Sequential Patterns

Sequential patterns analysis aims to uncover or identify common patterns, regular events or trends in transactions data over a certain period. In sales, using past transactions data, a business can find a set of items that their customers purchase in one visit during certain months or seasons. Businesses use this information to offer better deals or discounts based on historical purchasing frequency.

Decision trees

A decision tree is one of the most widely used forms of data mining due to its model’s simplicity and understandability. With the decision tree technique, the root of the tree is a question or condition that can have multiple responses. Each response then leads to a set of questions or conditions that help in determining the data so the user can make a better final decision. For example, we can look at the following sequence of questions and answers and make a decision of whether we want to play basketball outdoors or indoors:

  • Outlook Is it sunny? If so, the how humid is it? If high humidity, then I’ll play indoors → If low humidity, then I’ll play outdoors
  • Outlook Is it raining? If not, how windy is it? If high winds, then I’ll play indoors → If low winds, then I’ll play outdoors
  • Outlook Is there overcast? If so, then I’ll play outdoors

Beginning at the root node, if the outlook is overcast then I will play basketball outdoors. If it’s raining, I’ll only play basketball outdoors only if it’s not windy. And if the sun is out and shining, I will only play basketball outdoors if it’s not too humid.

These are the six basic techniques used in data mining. Though some of them may appear to be similar in practice, they all have different aims in terms of data collection. We are free to two or more data mining technique simultaneously to form a process that meets what a business’ needs.

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