Data analysis is a technique in which data is collected or arranged in order that one will derive useful information from it. Data analysis is one of the honest professions of the time your are going to learn how python is helpful for data analysis . In other words we can say that , Data Analysis is explained as a procedure of cleaning, transforming, and modeling data to discover important information for business decision-making. In this process we extract useful information from data and taking decision based upon the Data Analysis .It is also used as a scripting language or glue language to join existing components. Python is very powerful and easy to learn, the syntax emphasizes readability, and Python lowers program maintenance costs.

The Python language supports modules and packages, which encourages program modularity and code reuse. NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. SciPy adds even additional MATLAB-like functionalities to Python. Python is rounded gets in the direction of MATLAB with the module Matplotlib, which provides MATLAB-like plotting practicality.


Why Python For Data Analysis

 Python and R are two very popular open-source programming languages for data analysis. Frequently, users debate as to which tool is more valuable, however both languages offer key features and can be used to complement one another. A common perception is that R offers more depth when it comes to data analysis, data modeling and machine learning, but Python is easier to learn and tends to present graphs in a slightly more polished way. There are many more reasons for using Python for Data Analysis which are given below:

  • Python is easy to use
  • Python is versatile
  • Python is better for building analytics tools
  • Python has in-built beautiful and efficient data structures
  • The Python community is growing
  • Python is better for deep learning
  • Great number of open source libraries/frameworks/tools available
  • Data visualisation with Python



What Types of Data Analysis are There?

There are a half-dozen popular types of data analysis available today, commonly employed in the worlds of technology and business. They are: 

  • Diagnostic Analysis. Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.
  • Predictive Analysis. Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.
  • Prescriptive Analysis. Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
  • Statistical Analysis. Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:

o   Descriptive. Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.

o   Inferential. Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.

  • Text Analysis. Also called “data mining,” text analysis uses databases and data mining tools to discover patterns residing in large datasets. It transforms raw data into useful business information. Text analysis is arguably the most straightforward and the most direct method of data analysis.

Next, we will get into the depths to understand about the data analysis methods.

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