Over the past two decades, Python has emerged as the top programming language in the IT industry, with over 8 million developers using it globally. The digital revolution had already put developers at the centre of the workforce, and this trend has multiplied tremendously in the post-Covid world. As a result, programming languages have become an essential element of our lives. The modern world runs on the successful application of various programming languages. Python enjoys a privileged position among programming languages because of its dynamic versatility, low wordiness, flexibility, and user-friendliness. Several global software leaders, including Google, Facebook, and Netflix, use Python for their daily development operations tasks, automation, and data organization.
Whether you are a Python professional or someone just starting off, you’d know that data types are a fundamental concept in Python programming. Data types in Python are simply the classification of data items. Each classification of data has a specific value, and every value in Python has its unique data type. These data types represent classes, and instances of these classes, or objects, are called variables. The object is the primary unit of Python. Data types in Python help ascertain the kind of operation that can be performed on a particular data. Learning and understanding data types are the foremost steps to becoming proficient in Python.
This article discusses the different kinds of data types in Python. Read on to find out how to crack a successful career path in data science.
Table of Contents
Standard Or Built-in Data Types In Python
There are 5 data types in Python that are built-in.
1. Numeric Type
Numeric data types in Python refer to data with a numeric value. This numeric value can be an integer, floating number, or a complex number is defined as int, float, and complex class, respectively. As the names suggest, int class contains positive or negative whole numbers, float class has real numbers with decimal points, and the complex class contains complex numbers written in the form x + yj, where x is the real part and y the imaginary part.
2. Sequence Type
A sequence is an ordered collection in Python which allows the storage of multiple values in an organized manner. There are three main sequence types in Python.
A string in Python, represented by an str class, is a collection of one or more characters within a single, double or triple quote. All characters appearing between the quotes are items of that particular string. This is called a sequence of Unicode characters. Depending upon the memory capacity of the system, a string can store n numbers of characters. But it is important to remember that deletion or alteration of strings isn’t allowed because of the risk of an error.
A list is defined simply as an ordered sequence of items, and it is one of the most flexible data types in Python. The best characteristic about a list data type is that it does not require the values in the list to be of the same data type. This makes a list highly versatile in terms of usage.
A tuple is a non-modifiable ordered sequence of items represented by parentheses and separated by commas. Because they cannot be altered, tuples have a faster execution and are generally used for write-protect data.
3. Boolean Type
Represented by the class bool, boolean is a data type in Python that can be assigned either a true or false value. Non-boolean objects can also be evaluated in Boolean contexts in Python. This concept is rooted in Boolean algebra and mathematical logic.
4. Set Type
A set is an unordered data type in Python that is a collection of unique items. It is modifiable and does not have duplicate elements. Braces define a set, and commas are used to separate values. The utility of a set data type is heightened because operations like intersection and union can be performed on two sets together.
5. Dictionary Type
Like the set, a dictionary is also an unordered collection of data values that holds the key: value pairs. Unlike other single-value data types, this data type is used to store and deal with high volumes of complex data, such as maps. One unique feature of the dictionary data type is the optimized process of retrieving data wherein one can only retrieve a certain value if they know its key.
That summarises the different kinds of data types in Python. Data types in Python are among the foundational concepts of the Python programming language that become important when applying for different data science job roles. While programming languages are a pivotal tool in data science, Python is one of the most widely used languages. So if you are someone aspiring for a data science career, it’s worth investing time in strengthening these core Python concepts.
It’s hardly a surprise that data scientists and analysts specializing in Python are among the most highly sought-after professionals in the job market. As a data science professional, one can find a well-rewarding position in several technology sectors such as web development, machine learning, mobile or web app development, or even game development.
Indeed reports that the average base salary of a US data scientist is $113,821 per annum, which is a handsome salary by American living standards. Apart from being lucrative, the job also comes with long-term stability because of the invaluable stronghold that data scientists and developers have come to command in the world today.
How To Be Successful In Data Science?
The first step to being successful in Data science is to build a strong foundation in the subject and develop relevant programming language and coding skills. Most candidates do this by enrolling in a Bachelor’s degree in computer science, data engineering, or any related field. Companies also value sound industry knowledge along with past work or internship experience in potential candidates. Additionally, having an advanced certification from a globally reputed program adds a plus to your resume as it gives you a winning edge over other candidates.
If you are looking to upskill in data science, upGrad offers an Advanced Program in Data Science in partnership with IIIT-B, a top technology institute of global fame. This 12 months program builds industry expertise with 20+ industry projects and one-to-one mentorship sessions with industry experts. Designed for working professionals, the course lends flexibility in learning hours and provides access to Python, MySQL, Tableau, and many more industry tools. Additionally, one may also opt for an honors certificate on 3+ CGPA from this program.
upGrad is well-known for having a learner base spanning 85+ countries, 40,000+ paid learners globally, and 500,000+ working professionals. The programs guarantee 360-degree career assistance and peer-to-peer learning, making for a fantastic learning experience for any candidate.
The impact of data science in the digital sector is growing every day, and so is the job market for it. Thus, if you are looking to jump on the bandwagon, now is the best time to do it, as the demand for skilled data science experts will only increase in the future.
How easy is Data Science?
Data science is a highly technical subject and field of work. As such, learning often comes with a steep curve. However, if one can master the primary concepts and applications, data science becomes easier to grasp. An organized program helps in this journey.
How much time does it take to master Python?
Learning Python can take anywhere from a few weeks to a few months, depending on the individual’s existing level of knowledge and expertise, the frequency of practice, dedication, and external support. Python is one of the easiest programming languages to learn, and most people can gain fluency in a couple of weeks. However, the more one practices, the more finesse they acquire.
What is the minimum eligibility for upGrad’s course?
The minimum eligibility for the advanced program in data science is a Bachelor’s degree or diploma. No coding experience is essential, and final year undergraduate learners are also eligible to apply.