Basic Data Science

Duration :3.5 Months
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Python is an increasingly popular tool for data analysis. In recent years, several libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of python without sacrificing the functionality.

data analysis with python

This blog is displayed to offer an introduction to Python fundamentals, dig into data analysis and data viz using popular packages like Pandas, query database with SQL, and study statistics, among other things!

Basics of Python for Data Analysis:

Why learn python for data analysis?

Python has gathered a lot of interest recently as a choice of language for data analysis. It is a common language for data science and the production of web-based analytics products.

Here are some reasons which go in favor of learning python.

  • Open-source–free to install
  • Awesome online community
  • Very easy to learn

Python libraries and Data Structures:

The following are some data structures, which are used in python. One should be familiar with them to use them as appropriate.

  • Lists-List is defined by writing a list of commas separated values in square brackets. The can of the same or different types. Lists are mutable and individual elements.
  • Strings- Strings can simply be defined by the use of single, double, or triple inverted commas. Strings enclosed in triple quotes can span over multiple lines and are used frequently in docstrings.
  • Tuples- A tuple is represented by many values separated by commas. They are immutable and output is surrounded by parentheses. Tuples are faster in processing compared to Lists
  • Dictionary- Dictionary is an unordered set of key: value pairs, with the requirement that the keys are unique. A pair of braces creates an empty.

Python Libraries:

Let’s take one step ahead in our journey to learn Python by getting acquainted with some useful libraries that help in data analysis.

  • NumPy– Numerical Python. The most powerful feature of NumPy is an n-dimensional array. They also contain basic linear algebra functions, Fourier transforms, etc.
  • SciPy– Scientific Python. It is built on NumPy and one of the most useful libraries for a variety of high-level science and engineering modules.
  • Matplotlib: This is used for plotting a vast variety of graphs, line plots, and heat plots. One can use the Pylab feature in python notebook to use this plotting features inline.
  • Pandas: They are used for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas are added relatively recently to Python and have been instrumental in boosting Python’s usage in the data scientist community.
  • Seaborn: It is used for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphs in python. It is based on matplotlib.
  • Bokeh: it is used for creating interactive plots, dashboards, and data applications on modern web-browsers. It employs the used to generate and concise graphics in style.
  • Scrapy: Scrapy is for web crawling. It is a very useful framework for getting specific patterns of data. It can start a web site home URL and then dig through webpages within the website to gather information.
  • SymPy: It is used for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics, and quantum physics.
  • data analysis with python, panda

Conclusion:

Python is a great tool and is becoming an increasingly popular language among data scientists. So, learn python to perform the full life cycle of any data science project. It includes reading, analyzing, visualizing, and finally making predictions. It also opens up opportunities to learn data analysis with python.

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