Micro and macro data analysis by experts has a history beyond what most people imagine. In the 1950s, decades before anyone coined the term “big data,” companies were using data analytics to uncover more information and predict user behavior.

Despite this, the biggest advantage of data analysis is the valuable information they give us and significantly increase the speed and efficiency of the organization in planning and execution.

Unlike twenty years ago, in the last decade, businesses are able to make their momentary decisions with more precision and accuracy by analyzing big data, apart from the above, and as a result, significantly reduce their operational errors.

What is big data?

Big data is data with a processing capacity greater than the processing capacity of conventional database systems. Data that is too large, grows too quickly, or does not fit within the design constraints of the database.

Now, what method should we take to use this data correctly?

Big data contains patterns and valuable information that at the end of the 20th century were always marginalized due to the high workload and our inability to control and analyze them, or were forcibly removed by normalizing a large number of them. .

With the emergence of technology giants such as Walmart and Google, the ability to analyze data at a very high cost became somewhat possible, but in the last decade, with the development of hardware equipment, cloud architecture, improved libraries and open source software, big data processing and detailed analysis. They were done much faster, easier and with a lower error rate.

The importance of big data for companies

The value of big data for an organization is twofold: analytical use and new product development.

Big data analytics reveals hidden insights in data (including peer-to-peer influence on customers, shopper transaction analysis, and social and geographic data) that would otherwise be too expensive to process. Despite the relatively static nature of predefined reports, being able to process each data item in a reasonable amount of time removes the pressing need for sampling and opens up an exploratory approach to data.

Successful web startups of the past decade are great examples of big data being used as an enabler for new products and devices. For example, by combining many signals from the reactions of users and their friends, Facebook was able to discover highly personalized user experiences and create a new type of advertising. It is by no means a coincidence that many of the basic big data ideas and tools have come from Google, Yahoo, Amazon, and Facebook.

On the other hand, the emergence of big data in companies has brought it a necessary counterpart: agility.

Successful exploitation of values in big data requires experimentation and exploration. Whether we’re creating new products or looking for ways to gain a competitive advantage.

Big data analysis

Big data is primarily measured by the volume of data and is divided into three main categories:

Structured data: data that can be grouped in a column and under a variable.

Semi-structured data: which includes both structured and unstructured data.

Unstructured data: data that cannot be stored in a spreadsheet.

Data analysis refers to a set of actions that we can use to extract quantitative or qualitative information from big data.

Currently, it can be said that medium and large businesses have turned themselves into a data-oriented organization and are putting data-oriented approaches on the agenda to collect more data.

By using data analysis, you can easily find the challenges and chats you face in the space of management, implementation and marketing and find the right answer for it.

Although the discussion of data analysis may seem simple, big data inherently has a very high volume, speed and variety. The same issue has limited the choice of data analysis tools for a data analysis expert to a few specific items and marginalized some of them according to the field of activity.

Why is Python the best big data tool?

The most important tools for discussing big data analysis; The programming languages are R, python and Java, each of which has advantages and disadvantages, but why has Python been able to present itself as the best data analysis tool? In the following, I intend to address this issue.

Python vs R

  • Learning the Python programming language is much easier than the R language.
  • Python allows you to write large extensible, maintainable, and more powerful code than R.
  • Python has fewer packages for statistical analysis than R, but on the other hand, it supports powerful libraries such as pandas, numpy, scikit-learn, seaborn, etc.
  • The community of programmers and data scientists using Python is much stronger than R.
  • Python programming market is more than R and many companies and organizations both inside and outside the country use this language.

Python vs Java

  • Python programming libraries and packages are much more powerful than Java in the areas of: processing, storage, display, shaping, visualization, classification, etc.
  • Java is more difficult to install, run and implement than Python.
  • In Java, the codes need to be compiled first and then executed, while Python does not need to go through this path.
  • The number of lines required to write a specific program in Python language is obviously more than Python programming language.

Python tools for data analysis and big data

As mentioned in this article, Python has libraries, tools and powerful data analysis and programming packages that can be used to analyze and visualize data. In the following, I will mention the most important review of this tool.

1) NumPy

NumPy is the most powerful package in Python for numerical calculations involving an n-dimensional array object. Also, this package has significantly resolved the issue of slowness of some calculations.

Among the most important features of NumPy, the following can be mentioned:

  • Array calculations
  • Support object-oriented approach
  • Fast and compact calculations
  • Predefined tasks that speed up your work and reduce you’re coding.

2) Pandas

Pandas is the most popular and widely used Python library in data science, which is used to analyze, analyze and refine data.

The following are the most important features of Pandas:

  • Contains high-level data structures.
  • Its programming instructions are self-explanatory and simple for beginners.
  • It is used in various fields such as statistics, finance and neuroscience.

3) Matplotlib

Matplotlib is used for data visualization.

The most important features of Matplotlib include the following:

  • It is the best alternative to MATLAB for visualization.
  • It supports dozens of background models for output.
  • Low memory usage and better runtime behavior.
  • It identifies outliers for you using a scatter plot.

4) Scikit-Learn

Scikit-Learn is basically an essential library for machine learning and supports almost all machine learning algorithms.

The following are the most important features of Scikit-Learn:

  • Data clustering
  • Classification of data
  • Model selection
  • Dimension reduction
  • Linear regression plot

5) Tensorflow

Tensorflow is a library for performing numerical calculations with high performance, which allows you to create computational objects based on its classes.

The following are the most important features of Tensorflow:

  • Illustration of computational graphs
  • Reducing the percentage of error in machine learning algorithms and predictions
  • Perform calculations to run advanced models
  • Integrated management of libraries
  • Fast update
  • Speech and image recognition
  • Time series analysis

6) Keras

Keras is a library to support machine learning and deep learning in data analysis. Cross is basically a high-level core network API and can run on CPU and GPU without any problems.

Cross makes it possible to build, design and create a neural network for machine learning beginners, and it can also meet the needs of experts at a high level.

The most important features of Keras include the following:

  • Cross has a wide collection of tags that can be used directly for loading.
  • This algorithm includes layers and parameters that can be used to build, configure, train and evaluate the neural network.

Important note: Cross has an advantage over its peers like Scikit-learn and PyTorch because it is powered by Tensoroverflow.

7) Seaborn

Seaborn is a data visualization library developed by Matplotlib. This library allows you to draw informative and statistical pictures along with graphs for you.

On the other hand, this visualization model is very suitable for examining the relationships between variables in color.

The following are the most important features of Seaborn:

  • Ability to visualize univariate and multivariate data
  • Fitting and visualization of linear regression models
  • Plotting statistical time series data
  • Compatibility with NumPy and Pandas data structures

8) Scipy

Scipy is one of the best Python libraries that has a large number of modules for integration, linear algebra, mathematical calculations, optimization, statistics, etc. This library allows data scientists to solve the problems of signal processing, imaging, and etc. well without any restrictions.

The following are the most important features of Scipy:

  • High-level commands for data manipulation and visualization
  • Multidimensional image processing with SciPy ndimage module
  • It has built-in functions for solving differential equations and Fourier transforms
  • Has optimization algorithms

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