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.