Data Science with Python for Beginners


Python is the world’s most widely known programming language used in the field of Data Science. It holds this position in the sector because of a number of very good reasons. The first reason is that it has a syntax which can easily be learned and understood by beginners. It is this feature of Python which attracts droves of beginners who aspire to be programmers and data scientists. The second reason is that  it has a large community and that community has made a huge number of libraries and frameworks. These libraries and frameworks are meant for solving common programming and data science problems. These reasons have also caused more and more individuals to try to learn how to enter the field of data science with Python. Let us look at some features of Python which a beginner might encounter.

Why is Python’s code colour different in different places?

Most programming text editors and IDEs colour the code automatically according to certain rules and coding conventions. For example, the numerals will be displayed in a different colour, the variables will be displayed in a different colour, and the character names will be displayed in a different colour and so on. This is done to help a programmer differentiate between different sections of the code. If the whole code will be displayed in only one colour it will not be easy to read for a human being.

So, in order to improve readability Python code is displayed in different colours. It also helps programmer’s spot errors in the code. If a string is not being displayed in a particular colour, it means that the string has not been closed with a double quote. This is just one example of how colouring the code helps programmers to spot errors automatically. This readability of Python is also why so many people want to learn Python for data science.

Why do we put spaces before and after some characters?

Most programming languages use a pair of characters such as curly braces to demarcate the beginning or end of a code construct. Programming languages such as C, C++, Java, Lisp etc. do this. But Python employs another method to demarcate the beginning and end of a code construct. It makes use of whitespace. So the amounts of whitespace before and if else construct or before a do while loop will determine which level of indentation and nesting is present in the code. The Python interpreter requires whitespace for demarcating both indentation and nesting.

If one wants to make a block a child of a parent block they can indent it one level deeper than the parent block. In this way, the Python interpreter reads whitespace as a part of the code structure. This use of whitespace is usually explained in any good data science certification course.

Why do we insert blank lines in Python code?

Python code usually has blank lines before and after some characters and between different lines of the code too. The reason Python programmers put blank lines is that it makes the code more readable. If the whole code is written without putting any blank lines between the lines, it will become very dense. It will become very hard to decipher what the purpose of a particular function or a particular code construct is. It will also become difficult to distinguish between different sections of the code since there won’t be any blank lines at the end of a section and before the beginning of a section.

For a machine such as a computer there is no difference between code with blank lines and code without blank lines and it will execute both the program codes in exactly the same way. But for a human being it becomes easier to parse code with blank lines.

Why is the code split into different lines?

This is required by the Python interpreter. It requires the different code lines to be split into different lines in order to demarcate the beginning and end of one code line. There is an option in Python to end lines with a semicolon. In this way multiple lines of code can be written in a single line. But Python programmers do not use this feature and it is considered a bad coding practice to do so.

Without the code being split into different lines it would be displayed as a single extremely long line of code split with semicolons. This would make the code extremely difficult to parse for a human being. They would not be able to easily understand what the different parts of the single extremely long line do. They would also not be able to divide the code into different sections. Usually Python programmers divide the code into different functions so that each little task is encapsulated in its own piece of code. But this would not be easy in code which is not split into different lines. While this feature might not be immediately understandable for beginners, it is an essential part of doing data science with Python for beginners.

Does capitalization matter in Python?

Yes, capitalization does matter in Python. Programming languages like Python, where capitalization matters, are called case-sensitive languages. The variables Google, google, and gOOgle all refer to different identifiers or variables in the code.

About Careerera’s Data Science with Python

Careerera offers a very high quality data science certification course the name of which is ‘Data Science with Python.’ It has been especially designed for beginners who want to start learning Python in the context of Data science. In this course, all the core concepts and topics of Python are taught from the very beginning to the advanced stages such as data structures, recursion, machine learning with Python, artificial intelligence algorithms implemented in Python, NumPy for mathematical computing, Scipy for scientific computing, Data visualization with Matplotlib, Dimensionality Reduction, and Time Series Forecasting.

With this stellar data science training one will be able to enter the field in the real world immediately as a full-fledged data science professional. Some learning objectives of the course are -

  • Should be able to download and run a wide array of data cleaning and data warehousing algorithms on data using Python programs.
  • Should be able to handle and manipulate ordinal, categorical, and encoding data.
  • Generate data visualizations such as heat maps, cartograms, density maps, flow maps, histograms, stacked bar graphs, venn diagrams, pie charts, scatter plots, alluvial diagrams, word clouds, node link diagrams, matrix charts, sunburst diagrams, ring charts, tree diagrams, line graphs, timelines, line graphs, time series sequences, polar area diagrams, scatter plots etc.

How long does it take to learn python for data science?

Usually a beginner can pick up Python in a couple of months. But to learn how to use Python for data science takes much longer. It can take from 6 months to a whole year to properly learn and understand the various applications of Python and common Python libraries in Data science. So the final estimate is from 6 months to 1 year.

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