Python Programming Language in Data Science

Introduction

Python Programming is one of the object-oriented (based around data), high-level (easy to understand) programming language. First released in 1992, it is developed in a way that it is relevantly intuitive to write and understand. As such, it is an ideal coding language for those who want to speed-up the development.

If you are thinking about the uses of Python Programming, you will find that most of the big companies in the world implement it in some form. NASA, Google, Netflix, Spotify, etc. uses the Python programming language to improve their services.

Why Python Programming is becoming popular?

According to the TIOBE index, which measures the popularity of programming languages, Python Programming is the third most popular programming language in the world, behind only Java and C. There are many reasons for the Python Programming to get famous;

• It is ease of use. Those who are new at development using coding and programming, Python Programming can be an excellent first step. It is comparatively easy to learn, making it a great deal to start developing your own programming.

• It is simple syntax. Python Programming is comparatively easy to read and understandable, as its syntax is more like English language. It is straightforward layout that you can figure out what each line of code is doing.

• It is thriving community. As it is an open-source language, anyone can use Python Programming to code. There is a community which supports and develops the environment, adding their own contributions and libraries.

• It is versatility. As we’ll explore in more detail, there are many uses for Python Programming. Whether you are interested in data visualization, artificial intelligence or web development, you can find a use for the language.

Why should we learn Python Programming?

We know why Python Programming is very famous now a days, but why should we learn and how to use it? Aside from the ease of use and versatility mentioned above, there are several good reasons to learn Python Programming:

• Python Programming developers are in demand. Across a wide range of area, there is jobs and vacancies for those with Python Programming skills. If you are looking to start or change your career, it could be a vital skill to help you.

• It could lead to a well-paid career. Data suggests that the median annual salary for those with Python Programming skills is around £65,000 in the UK.

• There will be many job opportunities. Python Programming language used in most of the emerging technologies, such as AI, machine learning, and data analytics, it is likely that it is a future-proof skill. Learning Python Programming now could benefit you across your career.

What is Python Programming used for?

Python Programming is a famous and on-demand programming language to learn. But what is Python Programming used for? We’ve already seen some of the areas it can be applied to, and we’ve expanded on these and more Python Programming examples below. Python Programming can be used for:

1. AI and machine learning

Because Python Programming is such a stable, flexible, and simple programming language, it is perfect for various machine learning (ML) and artificial intelligence (AI) projects. In fact, Python Programming is among the favorite languages among data scientists, and there are many Python Programming machine learning and AI libraries and packages available.

If you are interested in this application of Python Programming, our Deep Learning and Python Programming for AI with Microsoft Azure ExpertTrack can help you develop your skills in these areas. You can discover the uses of Python Programming and deep learning while boosting your career in AI.

2. Data analytics

Much like AI and machine learning, data analytics is another rapidly developing field that utilizes Python Programming. At a time when we’re creating more data than ever before, there is a need for those who can collect, manipulate and organize the information.

Python Programming for data science and analytics makes sense. The language is easy-to-learn, flexible, and well-supported, meaning it is relatively quick and easy to use for analyzing data. When working with large amounts of information, it is useful for manipulating data and carrying out repetitive tasks.

You can learn about data analytics using Python Programming with our ExpertTrack, which will help you develop practical data analytics skills.

3. Data visualization

Data visualization is another popular and developing area of interest. Again, it has many strengths of Python Programming. Also, it is flexible and it is open-source, Python Programming provides a variety of graphing libraries with all kinds of features.

Whether you are looking to create a simple graphical representation or a more interactive plot, you can find a library to match your needs. Examples include Pandas Visualization. The possibilities are various, allowing you to convert data into meaningful forms.

If data visualization with Python Programming sounds appealing. You will learn how to leverage Python Programming libraries to interpret and analyze data sets.

4. Programming applications

You can program all kinds of applications using Python Programming. The other programming language can be used to read and create file directories, create GUIs and APIs, and more. Even if it is blockchain applications, audio and video apps, or machine learning applications, you can develop them all with Python Programming.

5. Web development

Python Programming is a great choice for web development. This is largely due to the fact that there are many Python Programming web development frameworks to choose from, such as Django, Pyramid, and Flask. These development frameworks have been used to create sites and services such as Spotify, Reddit and Mozilla.

Very grateful to the updated libraries and modules which comes with Python Programming language, functions like access to databases, content management, and authorization are all possible and easily retrievable. Given the versatility, it is very surprising that Python Programming is so widely used in web development.

6. Game development

Python Programming does have its uses in the industry for gaming development. It is possible to develop simple games using the programming language, which means it can be a useful tool for a rapid development a prototype. Similarly, certain function like dialogue tree development is possible in Python Programming.

If you are newbie in game development using Python Programming, then you can also find out how to make a text-based game in Python Programming. In doing so, you can work on various of skills and improve your knowledge in various areas.

7. Language development

The simple and elegant framework of Python Programming and its syntax convention that it has inspired the creation of new programming languages. Languages such as CoffeeScript, Cobra, and Go all use a similar syntax conventions to Python Programming.

This fact also means that Python Programming is a useful language. So, if you are totally new to programming, understanding Python Programming can help you determine other areas more easily.

8. Finance

Python Programming is rapidly being utilized in the world of finance and banking, often in fields like where quantitative and qualitative analysis is required. It can be a useful tool in finding out asset price trends and predictions, as well as in automating workflows across various data sources.

As mentioned already, Python Programming is an ideal tool for working with big data sets, and there are many libraries available to help with compiling and processing information. As such, it is one of the preferred languages in the finance industry.

9. SEO

Python Programming uses is in the field of search engine optimization (SEO). It is an area that often benefits from automation for web crawler and search engine robots, which is certainly possible through Python Programming. If it is implementing changes across multiple pages or categorizing keywords, Python Programming can guide.

Emerging technologies such as natural language processing (NLP) are also likely to be relative to those working in SEO. Python Programming is a powerful tool in developing these NLP skills and understanding how people search and how search engines return results.

10. Design

When asking ‘what is Python Programming used for?’ you probably were not expecting design to feature on the list. However, Python Programming is used to develop graphical design applications. The language is used in various 2D imaging software, such as Paint Shop Pro and Gimp.

Python Programming is even used in 3D animation software such as Lightwave, Blender, and Cinema 4D, showing just how versatile the language is.

Use of Python Programming Language in Data Science

The programming requirements of data science wants a very versatile yet flexible language which is easy and simple to write the code but can handle highly complex mathematical operations. Python is most suitable for such requirements as it has already established itself both as a language for general computing as well as scientific computing. More over it is being regularly upgraded in form of new addition to its libraries aimed at different programming need.

Mostly Python has got a very large collection of libraries which serve as special purpose analysis tools. For example – the NumPy package deals with scientific computing and its array needs much less memory than the conventional python list for managing numerical data. And the number of such packages is continuously growing.

Data Science: Challenges and solutions

Data Science: Data Science is a domain which involves working with a huge amount of data and use it for developing prediction, prescriptive and prescriptive model for analysis. It is about digging the information from data, capturing the data, (creating the model) analyzing(validating the analytical model) and utilizing the data(implementing the best model). It is a blended field of Computer Science, Business and Statistics altogether. It is an intersection of Data and computation.

Applications of Data Science

1. Search on Internet

Search engines uses various data science algorithms to display the best results for search queries within seconds.

2. Advertisement on digital platform

The digital marketing uses the data science methods (from display banners to digital hoarding). This is the significant reason that digital advertisement platform have higher click-through rates than traditional advertisement platform.

3. Systems for recommendation

The recommendation systems not only make it easy to search related products from millions of available products, but they also add more to the user experience. Many companies use this system to promote products and suggestions in accordance with the customer's demand and related information. The recommendations are based on the user’s previous demand.

Data Science Challenges and solutions:

1. Identifying the problem

One of the major task in analyzing a problem and designing a solution is to determine the problem properly and state each aspects of it. Mostly, data scientists opt for a manual approach and start working on data and tools without a clear information of the business problem or the client demand.

Solution:

There should be a well-defined workout before starting the actual data analysis work. The first step in this process is to identify the problem, then designing a solution, and finally analyze the results.

2. Access to the right data

For correct analysis, it is significant to lay the hands on the right type of data. Acquiring access to data in the most appropriate form is very difficult as well as time-consuming task. There could be an issues ranging from concealed data, insufficient volume of data or less variety of data. Data could be spread unevenly across variety of business so getting the access of that data can also a challenging task.

Solution:

Data Scientists has to be smart in data management systems and other data integration tools like Stream analytics software which is helpful for filtering and classifying of data. Many Data integration software also permit connection with external data sources and the seamless inclusion in the workflow.

3. Data Cleansing

Working with data which is full of inconsistencies and anomalies is every data scientist’s nightmare. Dirty or invalid data leads to vague results. Data scientists work with terabytes to exabytes of data and when they have to spend most of the time just cleaning the data before starting the analysis.

Solution:

Data Scientists should create Data Governance tools for overall accuracy, consistency and formatting of data. Moreover, maintaining data quality should be the main aim. Business operations across the enterprise take advantage from good quality data. There should be some people employed in departments as data quality managers.

4. Lack of domain expert

Data scientists has to be good at high-end tools and techniques, is one of the crucial challenge. Data Scientists also need to have good domain knowledge and acquire subject matter expertise. The biggest task for data scientists is to apply domain knowledge to business solutions. Data scientists are a bridge between the top management and the IT department. Domain expertise is needed to convey the needs of the top management to IT Department and vice versa.

Solution:

Data scientists have to work on gaining scenario of business, understand the real problem and work on analyzing and modeling the effective solutions. Along the mastering statistical and technical tools, Data scientists also need to concentrate on the business requirements.

5. Data security issues

Now a days, data security is a biggest issue. Since data is acquired and retrieved through a lot of channels like, social media, etc. there is increased vulnerability of attack by hackers. Due to the confidentiality of data, Data scientists are facing problems in data extraction, utilization, developing algorithms. The procedure of taking consent from users is causing a major delay in time and expensiveness.

Solution:

For this issue, there are no shortcuts. One has to follow the pre-defined global data protection rules. There should be additional security checks and make use of cloud platforms for data storage. Organizations also actively need to take help of advanced solutions that involve Machine Learning to secure against cyber-crimes and fraudulent practices.

Bigdata: Challenges and solutions

Big Data: It is very huge, quite large or abundant amount of data, information or the co-related statistics collected by the big organizations. Most of the software and data storage developed and prepared, as it is tough to evaluate the big data, manually. It is used to find out patterns and trends to make decisions concerning human, and interactive technology.

Applications of Big Data

1. Banking and Financial Services

All Credit card companies, retail banks, private wealth management services, insurance companies, and institutional investment houses use big data analysis for their financial services. The problem among them is that the massive amount of is multi-structured data stored in multiple systems, which big data can solve in quick time to make decisions. Big data is used in many ways, such as:

• Customer analytics

• Compliance analytics

• Fraud analytics

• Operational analytics

2. Big Data in telecommunications

Gaining new customers to subscribe, retaining the customers, and expanding within current customer base are top priorities for telephone communication companies. The solutions to these challenges is in the ability to collate and analyze the customer-generated data and/or machine-generated data that is being created day by day.

3. Big Data for Retail marketing

Whether the company is an online retailer or offline construction company, They all want to understand the demand of the customers and change in their needs. This need is to analyze all different data sources (data-mart) that companies deal day to day, including the customer transaction data, weblogs, social media, credit card data, and reward/coupon program data.

Bigdata challenges and solution

1. Lack of understanding of Big Data

Many organizations fail in their Big Data initiatives due to lack of understanding. Employees might not be knowing what data is, its storage methods, operations on data, importance, and data sources. Data professionals may know what needs to be done, but others may not have a clear view.

For example, if an employee do not understand the significance of data storage, he may not keep the backup of confidential or sensitive data. They might not use database systems properly for storage. As a result, when this data is required and needs to be accessed, it cannot be retrieved, easily.

Solution:

Big Data workshops and hands-on practice must be conducted for everyone. Basic training programs must be conducted for all the employees who are handling data, daily and as a part of the Big Data projects. A basic understanding of concept of Bigdata must be inculcated by all organization.

2. Data growth issues

One of the most complex challenge of Big Data is storing all these voluminous data, properly. The abundance of data being stored in data marts and databases of companies is growing, rapidly.

As these data grow rapidly with time, it will be difficult to handle in the future. The data is unstructured and comes from documents, audios, videos, text files and other sources. It means that you cannot search them in databases.

Solution:

In order to maintain these large data sets, companies are going for present techniques, such as compression, tiering (level-wise storage), and de-duplication. Compression is used for reducing the redundancies in the data, thus reducing its overall size upto some extent witout changing the meaning of data. De-duplication is the process of eradicating duplicate and unwanted data from a data. Data tiering allows companies to store the data in different storage tiers to ensure the data is residing in the most appropriate storage space. Data tiers can be private cloud, public cloud, and flash storage, depending on the data size and significance.

3. Confusion in selecting Bigdata tool

The companies sometimes get confused while selecting the best tool for Big Data analysis and storage. There are many questions arises like;

Is HBase or Cassandra the best technology for storage?

Is Hadoop or MapReduce good enough or Spark be a better choice for data analytics and storage?

Above questions bother companies and often they are unable to find the answers. They end up making poor decisions and select a technology which is not suitable. Therefore, money, time, and efforts are wasted.

Solution:

The best way to seek professional assistance. You can either hire experienced Bigdata professionals who knows much more about the tools. Another way is to go for Big Data consultancy for proper advice. Here, consultants will give some advice and recommend best tools, based on the company’s scenario. Based on their advice, you can make a strategy and then select the best tool for the betterment of the company.

4. Lack of data professionals

To utilize these novice technologies and Big Data tools, companies need to have skilled data professionals. These data professionals include data scientists, data analysts and data engineers who are experienced in working with the data handling tools and making sense out of voluminous data sets. Companies face lack of Big Data professionals in current scenario. This is because data handling tools have evolved, rapidly, but in many cases, the data professionals have not evolved as compared to.

Solution:

The companies are investing more and more money in hiring skilled professionals. They also have to offer free training programs to the existing staff to get the most out of them.

Another significant step taken by companies is to purchase the data analytics software/tools that are powered by artificial intelligence and /or machine learning. These tools can be used by professionals who are not data science experts but have preliminary knowledge.

5. Securing the data

Securing the huge data is one of the challenges task of Big Data. Often many big companies are also busy in collecting, understanding, storing, and analyzing the data that arises data security for later stages. But, this is not a good move as unprotected data repositories may become breeding grounds for hackers. Companies can lose the data with their revenue.

Solution:

Companies should recruit cyber-security professionals to protect the data. Other steps taken for securing data; such as:

• Data encryption

• Data segregation

• Identity and access control

• Implementation of endpoint security

• Real-time security monitoring

• Use Big Data security tools

6. Integrating data from a various sources

Data in company comes from a variety of sources or data marts, such as social media pages, ERP applications, MIS applications, customer logs, financial reports, e-mails, presentations and data reports created by employees. Combining all these types data to prepare a single reports is a challenging task. This is field often neglected by firms. But, data integration is important for analysis, reporting and business intelligence, so it has to be worked out.

Solution:

Companies have to resolve the data integration problems by buying the right data handling tools. Few of them are mentioned below:

• Talend Data Integration

• Centerprise Data Integrator

• ArcESB

• IBM InfoSphere

• Xplenty

• Informatica PowerCenter

• CloverDX

• Microsoft SQL

• QlikView

• Oracle Data Service Integrator

Types of open access publishing

There are 2 (two) types of open access

a. Gold open access: As per Elsevier

• An author can publish in an open access journal or hybrid journal which supports open access.

• The published article is freely available to everyone but the author can choose commercial or non-commercial Creative Commons license.

• If there is fee then publishing cost should be covered by the author or the institution or funding agency.

• All the open access journals can be found on the Directory of open access journals (https://doaj.org).

• DOAJ contains the journals in various 80 languages from 126 countries having 16665 journals and 6376560 articles till date.

b. Green open access: As per Elsevier

• The article can be published under a subscription model by linking and self-archiving the article for sharing as per policy.

• All accepted manuscripts should have Creative Commons use license, thus author retain the right to the article for further use.

• No fee is required to pay by the author for the publication as it is covered under the subscription.

• All the self-archiving publishers with policies can be found of SHERPA/RoMEO database.

• Open Access SHERPA-RoMEO project for free self-archiving.

• Sharing the research information, freely in the form of article/paper

• Self-archiving also known as "Green Open Access" in institutional repository.

• SHERPA project commenced in 2002 by JISC and CURL

• RoMEO is Publisher's copyright and archiving policies

• This project is backed by UNESCO.

Open Access Publishing

1. Many publishing house owns the rights of the articles published in their journals and they charge fee from whoever wants to read them.

2. Therefore, the idea of open access publishing is introduced where the authors and the readers are free from publishing and accessing, respectively.

3. In research, open access is a set of rule and regulation by which a research scholar can access the resource of relevant field of research for further research.

4. Open access publish the research article for free and give access to other researcher for further study for free.

5. The main principle of open access publishing is peer reviewed literature for the research and eradicates copying and reuse by open access licensing.

6. Open access publications are applicable to all types of publications like peer-reviewed journals, research papers, theses, books, etc.

7. Open access publishing is backed by UNESCO to provided free and unrestricted online resource for the research purpose. 

8. As per UNESCO, "the content is universally and freely accessible, at no cost to the reader, via the Internet or otherwise." 

9. As per UNESCO, the author or copyright owner irrevocably grants to all users, for an unlimited period, "the right to use, copy, or distribute the article, on condition that proper attribution is given."

10. As per UNESCO, "it is deposited, immediately, in full and in a suitable electronic form, in at least one widely and internationally recognized open access repository committed to open access."

11. All digital contents like audio, video, text, graph, images, software, data, executable code, articles, theses, etc. are free to publish by the author and free to access by the person who wants to use it, ethically.

12. Advantages of open access publications are

• Free for all the authors and readers who wants to use, ethically

• Increase in accessing the resource

• Free access to the researchers in all the countries

13. Disadvantages of open access publications are

• Sustainability in longer run for publication houses

• Lack in quality of the written materials as quantity increases

• No fee leads many publications houses to be shut down without fund

Turnitin similarity check

1. Turnitin is an online software based on SAAS (Software as a Service) to check for the similarity in content for plagiarism.

2. Turnitin is not an open source therefore the institutions, universities have to pay for the software to use.

3. Turnitin was developed by John Barrie and Christian Storm launched in 1997 for educational industry.

4. Turnitin is a privately owned company which provides similarity checking by online based services on payment basis.

5. It gives the similarity check on plagiarized content to get similarity report also known as plagiarism report.

6. The research scholar or scientist can check for the similarity score on Turnitin by submitting their content.

7. In Turnitin, the similarity score is shown in terms of percentage of the plagiarized content in a report.

8. High similarity score does not always mean that the content is plagiarized because some sentences can be written, generally.

9. Turnitin provides easier interpretation by color coding, various filters and source for comparison for the similarity in content.

10. It compares the similarity in content from the database repository of various industries, academic institutions for plagiarism.

11. It enables students and researchers to use the paraphrasing, citation, quoting and sources of content, appropriately.

12. It protects the institutions and researchers from the research misconduct like plagiarism, Salami-slicing, contract cheating, etc. and follow the research ethics for writing thesis and/or research article.

13. It is considered that 10% or below similarity score is allowed to submit the research paper and thesis of Ph.D.

14. The researcher has to remove the uploaded file from the Turnitin after checking the similarity score and made the changes as required to get the limitation of 10% or below similarity score.