We live in a world of information technology and mobile communications. Fast paced lifestyle of globally interconnected people demand efficiency. The key to success in this modern world is speed and accuracy. Knowledge comes from information, and information is extracted from data. Data is basically a coded representation of virtually anything that concerns human beings. In the context of computing, Data is text, numbers, formula, images, animation, video, etc.
Data is organized into databases for efficient storage, access, and modification. Structured query languages (SQL) and relational database management systems (RDBMs) achieved a lot of success. They helped in the automation of modern businesses and evolution of enterprise level information systems. But internet and Smartphones have totally changed the game in recent years. We now live in the world of disruptive technologies and the buzzwords are Big Data, Artificial Intelligence, and Machine Learning.
Streaming video is familiar even to young children, while Google deals with Terabytes (TB). Big data refers to the voluminous growth of information sources across the globe. Analytics is one aspect of the science that handles large and diverse types of data. Meaningful information cannot be generated without data processing. And Python comes in here as an excellent programming language.
Other contemporary software technologies include Java, R, Oracle NoSQL, Apache Spark or Hadoop, SAS, etc., These products are variously described as programming languages, software suites, analytical tools, platforms and computing frameworks. The professionals are also designated as data scientists, analysts, statisticians, database administrators, and architects. Their skills include engineering math, computing, algorithmic analysis, AI skills, and machine learning.
Tools and Technologies
Big data has specific traits such as volume, velocity, veracity, and variety. There is large volume of diverse data like text, images, audio, and video. In addition, the analysts have to use reliable software tools that can cope with rapid data collection. Python stands out as a contemporary language with rich set of "ready to use" libraries. The language is easy to learn, and clever programmers can write concise, readable code.
What Happens in Data Analysis?
Raw or structured data has to be subjected to elaborate processing. The procedures or methods aim to explore the various facets of data sets. In a database, the analyst submits queries and extracts useful information. The basic idea is to manipulate data, collect leads and make informed decisions for a business organization. Traditionally, the beneficiaries were IT starts ups, web services like Google, and business corporations.
However, Big Data requires much more than simple database queries and numerical reports. A comprehensive field of study known as data science is currently in high demand. The scientists aspire to achieve predetermined goals using a sequence of steps. They carry out data retrieval, preparation, analysis, and modeling. Statistical techniques, visualization, and data mining activities are crucial for success.
Why Big Data?
Let us look at the different types of big data to comprehend the underlying challenges -
- Structured - A database system for payroll, HR, inventory, sales management, etc., An excel spreadsheet with student grades, library book catalogue, pharmacy drug list, etc.,
- Unstructured - Good examples are web or mobile based text exchange or email. Twitter or micro-blog messages are also in plain English.
- Natural Language - Domain specific communication and comprehension of languages. It relates to linguistic concepts like syntax, structure, semantics, etc.
- Graph - In computer science, this is a hierarchical representation of related data. Graphs and trees are traversed to establish networks and understand connections.
- Multimedia, Streaming, etc. - Audio, video, pictures, and streamed data is Methods include image sensing, video screen processing, deep learning, 3D modeling, and event handling.
What Are Its Uses?
Internet is not just about accessing websites for information or entertainment. It is also not restricted to online businesses. Social media has boomed, and Smartphone dependency is a reality. The volume of diverse data has grown by leaps and bounds from Kilo or Mega Bytes to Giga, Tera, and Peta Bytes. The future is being described in terms of Predictive Analytics, Cloud Computing, Artificial Intelligence, and Quantum Computers. The beneficiaries include corporate world, government organizations, academia, and global technocracy.
Python is a highly versatile and multi-purpose language with reusable code or packages. A developer can learn and adapt quickly to the programming environment and tools. There is support for natural language processing toolkits or NLTKs. Web integration is smooth and dependable, as are extensibility and scalability.
Power of Python
Advantages of Python
- A practical and pragmatic choice that has easy to learn syntax and semantics.
- Extensive support for structured and object-oriented programming concepts.
- User-friendly and flexible integrated development environments (IDEs).
- Even non-programmers can learn quickly due to easily readable code.
- Advanced features like modularity, exceptional handling and code reusability.
- Packages for a wide range of applications (bioinformatics, AI, social sciences).
- Extensive support for networking, databases, web technologies, and data science.
- Efficient coding and data processing on multiple platforms and operating systems.
Data Scientist Support
The R programming language is a force to reckon with in statistical analysis. Python does not lag behind as it has many powerful packages for data scientists.
- Hadoop - It is an open source, Apache platform for big data solutions. Python integrates well with Hadoop HDFS API. The PyDoop package facilitates complex problem solving and data retrieval. It can also be used to develop MapReduce applications (parallel, distributed algorithms for big data processing).
- Computing - Modules like NumPy, SciPy, Pandas, etc., assist in math and numerical analysis. Programmers work with multi-dimensional arrays, matrices, linear algebra, and calculus. High level data structures and optimal code perform analysis, transformation, and mapping of data sets.
- Machine Learning - Efficient algorithmic analysis and processing through PyBrain, TensorFlow, etc., Scikit-learn is used with SciPy for data clustering, regression, and classification.
- Visualization - Matplotlib, Statsmodels, and Gensim assist in data visualization, graph plotting, statistical and topical modeling.
We live in a world of high performance computers and networking technologies. Data and information have become vital for business and personal success. Information systems with relational databases and user-friendly front ends do not suffice. Social media is on the upswing, and progress is defined by informed choices and faster data access. Humans cannot handle information overload, and data science has become popular.
Large, voluminous amounts of data are generated in the form of text, numbers, pictures, audio, and video. Data scientists develop models and techniques to access and manipulate this Big Data. Their data analysis is crucial for information retrieval and business profits. Python and R have been rated as sophisticated software to achieve these goals. Python has powerful packages for developing applications in Engineering, AI, Bio-informatics, and Social Sciences (Media, Politics, Governance, Twitter, Facebook, etc.).