A Guide to Basic Python Programming

Reading Time: 3 minutes

To learn Python means to practice Python. Python is a popular high-level, interpreted, interactive, and object-oriented programming language invented by Guido van Rossum in 1991. Its popularity rests on its simplicity, readability, and ease of use, making it a popular option for novices and professionals. 

best big data analytics course

It may be processed in a procedural, object-oriented, or functional style and operates on an interpreter system. It features a clean and focused syntax centred on readability and a vast and active community offering assistance, tools, and libraries to help developers solve issues and build new things.

Getting started with Python

Here are some key points to consider:

Choose an Integrated Development Environment (IDE) or code editor to develop and execute Python code. Some prominent choices are:

  • PyCharm 
  • Visual Studio Code 
  • Sublime Text 
  • Atom 
  • IDLE

Install Python on your PC. You may grab the latest version of Python from the official website: https://www.python.org/downloads/

Learn the basic principles of Python programming language, including:

  • Variables and data types
  • Operators
  • Control structures (if/else statements, loops)
  • Functions
  • Modules and packages

Practice developing Python code by tackling exercises and challenges. Some resources for Python exercises are: https://pynative.com/python-exercises-with-solutions/

https://www.w3resource.com/python-exercises/python-basic-exercises.php

Join online groups and forums to network with other Python developers and seek support when required. Some popular communities are:

  • Reddit’s r/learnpython
  • Python Discord server
  • Stack Overflow

Read Python documentation and tutorials to learn more about the language and its capabilities. Some resources for Python tutorials are: 

https://docs.python.org/3/tutorial/index.html

https://realpython.com/ 

Data types in Python

Python includes various built-in data types used to represent different sorts of data. Here are the most popular data types in Python

Numeric types: int, complex, float 

String type: str

Sequence types: list, range, tuple

Set types: frozenset, set

Mapping type: dict

Boolean type: bool

None type: None

Binary types: memoryview, bytes, bytearray

Here are a few examples in detail:

Integers: Whole numbers, such as 1, 2, 3, and so on.

Floating-point numbers: They are decimals, such as 7.21, 5.168, etc.

Tuples: Tuples are ordered, immutable collections of elements, such as (1, 2, 3), (“apple”, “banana”, “cherry”), and others.

Strings: They are sequences of characters, such as “hello”, “world”, etc.

Dictionaries: Dictionaries are unordered collections of key-value pairs, such as {“name”: “John”, “age”: 30}, {“fruit”: “apple”, “colour”: “red”}, etc.

Lists: They are ordered collections of elements, such as [“apple”, “banana”, “cherry”], and others.

Here’s an example of how to create variables of different data types in Python:

a = 42

b = 3.14

c = 2 + 3j

str_var = “Hello, world!”

lst_var = [1, 2, 3]

tpl_var = (4, 5, 6)

rng_var = range(10)

dct_var = {“name”: “Alice”, “age”: 30}

st_var = {1, 2, 3}

fst_var = frozenset({4, 5, 6})

bool_var = True

bts_var = b”hello”

ba_var = bytearray(bts_var)

mv_var = memoryview(ba_var)

none_var = None

Functions in Python

Blocks of code called functions may be reused to carry out different tasks. Using the def keyword in Python, you may create your functions. Here is an example of a function that computes a number’s factorial:

def factorial(t):

    if t == 0:

        return 1

    else:

        return t * factorial(t – 1)

The following is how your Python code should call this function:

def factorial(n):

result = 1

for i in range(1, n + 1):

result *= i

return result

num = 5

print(factorial(num)) # Output: 120

Control structures in Python

Python has several control structures that let you manage the application’s flow. These consist of try-except blocks, for loops, while loops, and if-else expressions. An overview of various control structures is provided below:

If-else clauses: If-else clauses let you run several chunks of code in response to a condition. For instance:

if y > 0:

print(“y is positive”)

else:

print(“y is non-positive”)

For loops: For loops allow you to iterate over a sequence of elements. For example:

for k in range(20):

    print(k)

While loops: While loops let you continually run a section of code as long as a condition is met. For instance:

x = 0

while x < 10:

print(x)

x += 1

Try-except blocks: Try-except blocks allow you to manage potential exceptions in your code. For instance:

try:

    num = int(input(“Enter a number: “))

    result = 1 / num

except ValueError:

    print(“Invalid input”)

except for ZeroDivisionError:

    print(“Cannot divide by zero”)

else:

    print(“Result:”, result)

This guide covers Python fundamentals such as data types, control structures, functions, and modules. The best way to learn Python is to start with the basics and then slowly master it by practising coding or taking part in a workshop. 

Conclusion 

Imarticus Learning offers a Postgraduate Programme in Data Science and Analytics (PGA). This programme uses data science and creates analytical models that enhance corporate performance. Machine learning and Python programming are among the basic and advanced data science and analytics techniques covered in the curriculum. The course is one of the best to learn Python for beginners. It offers certification programmes and job assurance.

Python is better than javascript. Here’s why

Reading Time: 3 minutes

Python is better than javascript. Here’s why

In this digital age, everything around us functions as a result of some programming. Therefore, programming is an incredibly beneficial skill to have in 2022. There are several online programming languages that you can learn. Currently, Python is an in-demand programming language for various reasons and is widely used by beginners and seasoned programmers alike.

Before choosing which language to learn, you must first know what you wish to accomplish by learning the language. Python is the best option for data analytics. With its vast number of libraries, it can handle almost any task that you throw its way. Meanwhile, JavaScript is the best choice for web development. It’s versatile and easy to use, and it is the ideal choice for designing various apps.

You might also want to engage with recruiters, senior developers, or employees at firms across industries to find out which languages are preferred in a particular industry. This will help you understand what language is ideal for you for the task you wish to accomplish or the type of firm you want to join.

In this blog, let’s see how Python is better than JavaScript:

Easy to learn and beginner-friendly:

Python is widely known for its simplicity. Here’s why beginners choose Python over JavaScript:

  • It is easy to understand and write; thus, it’s more readable. On the other hand, JavaScript is comparatively difficult to debug.
  • There are fewer lines of code when programming in Python than in other languages like C or C++.
  • There are fewer syntactical rules in Python. For example, you do not have to add a semicolon after every line of code. On the other hand, in JavaScript, many additional characters like curly brackets and semicolons are required to make the code meaningful.
  • It has a wide range of frameworks available (Python libraries) which contain pre-written code that will help you to speed up your project creation.
  • There are many Python Tutorials available on the internet that allow you to learn Python online for free. Hence, it is a very accessible programming language.

This being said, interest is also a factor in w. The more you are interested in learning the language, the easier it will be to learn. More importantly, you must spend more time developing actual projects rather than just learning the syntax.

Python is a versatile language:

Contrary to the belief that Python is only for the back-end, you can actually use it for general purposes too! It is versatile in terms of the application, as it is used on the server side as well as the client side. However, do note that Python can be used on the front end—but it is not advisable to replace languages designed for the front end with Python. 

Python is gaining a lot of popularity among developers. Given its applications, it can be considered the future of programming languages. There are a lot of frameworks (Python libraries) available which can be used for data analysis, artificial intelligence, and machine learning.

Preferred language for data analysis:

Although JavaScript is very popular, it cannot perform all types of tasks. Data science is a booming field where specialists have to analyse and manipulate data to achieve an end. Data scientists have to use many languages to accomplish a single task, and one of them is Python. You can use JavaScript for data science, but specialists do not prefer to use it. Why? 

  • Python offers many data science packages and built-in functions, whereas JavaScript has limited options.
  • Python is a better choice for data science, as you will find many “how-to Python” tutorials online and other related tips.
  • You can perform actions quickly through Python or R languages.

Hence, if you are inclined towards a data science career, Python is the way to go, as it is more efficient and structured for this path. There are several Python training courses or Python tutorials available online to help students and tech enthusiasts learn this magical tech language.

Data Analytics and Machine Learning Course by Imarticus Learning is an ideal program for recent graduates and professionals who want to build a successful career in data science and analytics. This program covers a combination of the Data Science and Analytics industry and will help you acquire the required professional skills desired by the world’s largest employers in the industry. It covers capstone projects, real case studies, and business projects with mentorship from top industry experts. 

The syllabus consists of SQL Programming, Python Programming, Statistics, Machine Learning with Python, R and Data Science, Big Data & Hadoop, Big Data Analytics with Spark, Data Visualization with Tableau, and Data Visualization with PowerBI.

This course by Imarticus Learning offers two different learning tracks—one for recent graduates and another for working professionals. Each track is designed to suit the different requirements of the target audience. Both courses are designed per industry standards to deliver the best learning outcomes.

Keen to explore how Imarticus Learning can help you expedite your programming journey? Schedule a FREE counseling session with us by contacting our chat support, or simply drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Want to Learn Advanced Analytics For Marketing Purposes, In What Order Do You Need to Learn Big Data?

Reading Time: 2 minutes

Generally, Data Analysis is a comprehensive process that involves taking unstructured information, inspecting it, cleansing, and then transforming key insights in a structured form. With advanced analytics, we use these data to further find specific patterns and draw conclusions that assist a large organization to make precise decisions for growing in a positive direction.

Data analysis nowadays is used across several businesses with a different approach, diverse techniques, and methods to help them make a precise decision for improvising efficiently. At Imarticus Learning, we help new age professionals to learn advanced analytics with dedicated courses to upskill them to match the corporate world requirements. 

A simple data project follows this structure in the form as:

SQL for Extracting and transforming data,

Tableau for Data Visualisation & insights building as Hypothesis building,

R for Statistical Data Analysis with Bivariate & univariate analysis of variables, and

Python for Model development / Hypothesis testing.

Data analyst professionals deal with a very high amount of data daily. The first step is to learn SQL to analyze, extract, aggregate, and transform data for a more purposeful understanding. So, as a professional working in the data analysis field, SQL is the foremost priority to learn and manage data properly.

These datasets can have 1 million+ rows, here Tableau will work on visualizing data to bring insights or hypotheses. With Tableau, one can effectively track a million rows of information in data form to create useful insights.

R is another programming language used specifically for data analysis with the environment suited for statistical computing. One can also visualize data, blend data, build a statistical model, and perform complex transformations. R language is also preferred for developing statistical software so data analysts must have an understanding of its effectiveness.

Python is a general-purpose high-level programming language that most coders prefer to use. Python is used to develop algorithms from these large sets of data variables with scripts that make effective management to find relations and goals from the data itself. One must learn Python programming for building a sophisticated career in data scientists. 

Although a data structure follows a specific path from SQL to Tableau to R to Python, still the goal and objective of the project define the purposeful use of that language. SQL helps us to query data properly; with Tableau, we learn to visualize data, R is better for exploration, while Python works better to get high production.

A well-organized course can help you to understand the right purpose for each of these languages precisely. Though an individual may not have expertise in each of these languages still, if you are opting for a career in Data analysis, you must understand the scope of SQL, Tableau, R, and Python to grow in the right direction.

At Imarticus Learning we offer several programs for professionals to learn advanced analytics and offer their expertise to the corporate world with definite preparations as well as courses to match their expectations. 

5 Top Reasons to Learn Python

Reading Time: 2 minutes

One should have a good grasp of technology, as its uses and advantages have seeped in almost all spheres of professional setups. If you are working in the field of IT, programmer to be specific, a quick way to upgrade your resume would be to learn Python. Python is considered to be the most commonly used programming languages. Hence for a programmer who is on the brink of embarking his career should learn Python.
So if you are considering learning to code, and be updated and efficient with your skills in the world of programming. Then further read on to understand five undisputable reasons you should learn Python.

Quick and Fast

Python is definitely an easy language to learn, to be true the language was designed keeping this feature in mind. For a beginner, the biggest advantage is that the codes are approximately 3-5 times shorter in Python than in any other programming language. Python is also very easy to read, almost like reading the English language, hence it becomes effective yet uncomplicated in its application.
The dual advantage is that a beginner will not only pick up faster but, will also be able to code complex programmes in a shorter amount of time. And an experienced programmer will increase productivity.

Big Corporates use Python

Python is one of the most favourite languages used at Google, and they are ever hiring experts. Yahoo, IBM, Nokia, Disney, NASA all rely on Python. They are always in search of Python web developers, and a point to note is that they are big pay masters. Hence learning Python equals to big Pay cheques.

Python for Machine Learning and Artificial Intelligence

The biggest USP of Python is that it is easy to use, flexible and fast, hence it is the preferred language choice. And especially so in computer science research. Through Python, one can perform complex calculation with a simple ‘import’ statement, followed by a function call, thanks to Python’s numerical computation engines. With time Python has become the most liked language for Machine Learning.

Python is Open Source and comes with an exciting Ecosystem

Python has been there for almost 20 years or so, running across platforms as an open source. With Python, you will get codes for, Linux, windows and MacOS. There is also a number of resources that get developed for Python that keeps getting updated. It also has a standard library with in-built functionality.

Nothing is Impossible with Python

And if the above reasons are not convincing, perhaps the best reason to learn Python, is that irrespective of what your career goals are you can do anything. Since it is easy and quick to learn, with it, you can adapt to any other language or more importantly environment. Be it web development, big data, mathematical computing, finance, trading, game development or even cyber security, you can use Python to get involved.
Python is not some kind of a niche language, and neither is it a small time scripting language, but major applications like YouTube or Dropbox are written in Python. The opportunities are great, so learn the language and get started.

References:

Python Coding Tips For Beginners

Top Resources To Learn Python Online In 2022

Top Resources To Learn Python

It is Useful To Learn Python Language For Big Data