Python-The language for beginners

A computer language is a set of instructions on how to communicate with the computer. It is mainly used by programmers for coding, web development etc. It is the only way for a programmer to develop anything and communicate with the computer. The easiest one to understand for beginners is called Python. Python is mainly used for web & app development.

The History

It was developed by Guido Van Rossum in the 1980s. He wanted a simple language that was easy to understand. The name is a reference to his favorite show Monty Python. It is described as a ‘glue language’ because it connects existing components together. Python focuses on ease of readability which means developers can easily read the language and translate it. Since it’s so easy to read and since it uses modules and packages, it can be re-used across multiple websites or apps.

The Advantages of Python

Python has multiple advantages, for example, it is a simple language which is easy to learn since the language is so easy and readable. Not only is this good for beginners, as it doesn’t scare them off and is also fun, but it is easy and cheaper to maintain, at a very low cost. Not only this, Python is free and open sourced which means anyone can customize it.

Python is also portable as it can be moved around easily and run across multiple systems. You can also combine other languages like C++ with Python. This will make your program have more features. Python also ensures to make things easy for beginners, as with other languages, you have to worry about memory management etc. It also converts your code to a language the computer understands so first timers or people new to this won’t have difficulty.

Where is Python used?

There are many applications for Python such as web development and applications. Some famous browsers and applications, for example, Firefox, Instagram and reddit are all written in Python. It can also be used for numeric and scientific calculations as it has certain libraries such as NumPy which is used generally, and for more specific tasks, there are libraries like AstroPy for astronomy. It is used in the development of games and web applications and automation and data management.

The drawbacks

But not all is perfect here, Python is slower than the other languages because it works as an interpreter and not a complier which means programmers can only find bugs in the program during runtime.

It also has a very high memory consumption which means it takes up more RAM, and also, it’s not good for mobile development. It requires a lot more testing than other programs and it has very limited database access to applications using JDBC.

Future career prospects

People who get into this might be interested in being a software engineer and IT developers as they can use Python to develop applications etc. Research analysts and data analysts might also use this, as one of its advantages is numeric calculations.

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Data Science: The science that matters now

The data is all around us. It’s everywhere. No matter what humans do, at every moment we generate lots and lots of data that can be collected and monitored. The whole existence of humans technologically can be singled into a single piece of data article. So when we say that our population is increasing day by day with more and more people joining the technological bandwagon, we need this data to be managed and arranged in a better way so that we can use it when we want. This power to use the data and process it to get what we actually want comes from this Data Science. Data Science helps us understand the correct behavior and trends of the data around us by using simple tools on them. All of the big companies use Data Science to actually deduce about what actually customers want with the help of Data Science. All the things we do are collected as data and then worked upon to get the results. This is what makes it so interesting and in this tech savvy age, it makes data science kind of necessary for learning and research activities.

What exactly does a Data Scientist do?

A data scientist has many kinds of work. But most importantly, they have the job to create the processed form of data in the way described by their clientele. The data scientist has to actually start by mining the data from different places. Here mining effectively means to collect all the instances of data that have to be used in this particular result. After getting this raw instances of data they now have to plan accordingly on the ways they can convert the data so as to make it easy for their clients to understand. All of this is done with the help of tools and languages that help these expert individuals to do their job. The end product is a data instance that is easy to see and understand and this is what helps people or companies to take a better and educated decision and plan accordingly.

What is in this course?

The industry experts would help you understand the basic concepts of data science with online lectures and study materials.

In this course, there are many statistical tools and advancements to be learned. Most of them are actually used by experts in the industry. Most of the major topics include making decision trees, learning clustering, hypothesis testing, creating perfect data models, understanding linear and logistic regression, making regression models and data visualization as well. R studio, Hadoop, Spark, PROC SQL, SQL Macros et cetera tools and languages help us work with data in a better way. To cap them all, a capstone project is also added at the end to help you judge your learning and implement what you have learnt here.

Conclusion

Data science is now on a gradual rise. People are slowly getting interested in this subject.

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Data science: A Blend of These Data Components

Data science: What is it?

Those who are a little new to the field should first know what data science is at all. So, most of you should not have much problem in agreeing that the current era is the era of data. Data is collected, stored and processed by individuals, groups, and large business firms throughout the world. Data science is the very field that deals with all the problems involving data. It is a very vast and highly multi-disciplined domain and its applications and reaches are rooted far more extensively than you can even imagine of.

As I’ve already mentioned, data science is a multi-disciplinary sphere and is contributed by many data related fields. These fields are described precisely in the following part of this article.

Different Components of Data Science

1)      Big data- The term “Big Data” is used to address the unusually large and extraordinarily complicated sets of data that can’t be even though of to be solved using the old traditional data management techniques. We know that data is being created each and every second from many sources, including company databases, cameras, microphones, IoT networks, and many other sources. The amount of data being stored per day now accounts up to several zettabytes.

2)      Data mining- Data mining is the process of processing raw data with the intention of extracting more understandable trends or patterns out of it. This makes the data easier to work on at the subsequent steps. It is used for predictive analysis i.e. finding out the possible behavior of the data on the basis of the patterns in the pre-existing data.

3)      Data analytics- Data analytics can be understood as the set of qualitative and quantitative techniques and processes involved in solving business and commercial problems and aiding in taking decisions which result in maximum benefit or gain.

4)      Data analysis- Though it may sound similar to the previously discussed component- “data analytics”, data analysis is a little different from it. In fact, data analysis is a part of data analytics. Data analysis is the set of processes involved in examining data for taking decisions to fulfill business objectives.

5)      Machine learning- Machine learning is the study of computer algorithms and statistical models which is used to train machines to perform some specific operation by giving them sample tasks and teaching them how to respond. Machine learning is a very vast field in its own. One of the biggest and most promising applications of machine learning is in artificial intelligence. It teaches machines to do such tasks which were earlier regarded to be the privilege of humans only.

So, these were the main components which frame together to make the huge arena of data science. Whether it resonates with you or not, data science has innumerable applications in the commercial world as well as your personal life.

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