Data Science with Python

Python is extremely well-suited for data science. It’s simple syntax and powerful language features such as dynamic binding make it very useful for the field of Data Science. If a data scientist chooses Python, he won’t have to learn the nitty gritties of programming from scratch as Python is a very beginner friendly language and will give him a boost so that he becomes proficient in programming very fast. If he were to choose any other language, he would have to spend a lot of time wrestling with the language itself. As a result, he won’t be able to concentrate on his actual work related to data science.

A very simple and beginner-friendly way to install the Jupyter notebook is by installing the Anaconda distribution. Anaconda is the most popular data scientist bundle which is filled with preloaded data science tools, modules, and libraries.

The first step is to set up the Jupyter learning environment. It contains many useful features which help data scientists to save a lot of time and energy. This is because the features have already been preprogrammed into the Jupyter learning environment and don’t have to be implemented from scratch. Jupyter makes it possible to add plots, formulae, comments, images, and code into one document. These elements are very common in data science and thus data scientists find this feature to be invaluable. Jupyter is so flexible that it allows one to use different programming languages in the same notebook, input formulae in Latex or ASCII, generate graphs and plots, and even execute Python code.

The simplest and most beginner-friendly way to install the Jupyter learning environment is to install the Anaconda distribution. It is almost the standard data science bundle because it contains preloaded data science tools, modules, and libraries.

You can download and install the Python interpreter. It comes bundled with an IDE or an Integrated Development Environment. It is very easy and simple to run scripts on the interpreter. One can also test individual lines of code. One can load a module in it and step through every single line of code looking for bugs. So the IDE provides an ideal testing and debugging environment too.

It is difficult but it can be done. One has to be very determined as a learner though. One should also have the guidance of expert instructors who will be able to teach him the core and essential concepts in a short amount of time.

You can get a job very easily if you have a Python certification. Companies constantly seek skilled and competent Python developers and possessing a Python certification is a good proof that you are one. So if you get a Python certification your value in the job market will go up tremendously. In addition to that, you will be able to demonstrate to your potential employers that you have a self-driven learner’s mindset and that you hold a great amount of interest in your career. Companies will immediately give you preference over all the other candidates in the hiring pool.

One of the drawbacks of Python is that it is very slow very algorithms which perform a lot of big calculations and other straightforward tasks related to numbers. Another is that it is not able to handle large data sets efficiently or at a fast speed. So one may naturally be lead to question that why is Python the most popular programming language for Data science.

The answer is that in Python it is very easy to write bindings or extensions for other languages. So what data scientists typically do is that they offload the number crunching and data processing tasks to other faster languages such as C or Fortran. This lets them take the advantage of the speed and processing power of the faster languages while at the same time retaining the simplicity, convenience, syntax, modules, libraries, and interoperability of Python, which is a high-level language.

1. Numpy -

The first library which an aspiring data scientist should take the time to learn is Numpy. They should make themselves absolutely conversant with its structure and functions. It is a library whose main purpose is to provide fast, heavily optimized, and efficient multidimensional arrays. Multidimensional arrays are the most frequently used and basic data structure of machine learning algorithms. So one can understand how essential it is to have a fast implementation of them. This is why most of the parts of Numpy dealing with fast computation are written in C and C++, which are very fast languages.

2. Pandas -

The full form of Pandas is Python Data Analysis Library. As its name suggests, it is a library mainly intended to provide data scientists with the functions of data analysis and data manipulation. It is particularly known for its functions related to numerical tables and time series sequences. The basic data structure of Pandas, which has been specially designed to aid in data manipulation, is called a data frame.

3. Matplotlib -

As a data scientist one will have to spend a large portion of their time creating data visualizations or reading them. Data visualizations offer a huge advantage over simple large arrays of data and that is that the data is presented in a format which is easily digestible. Matplotlib has been designed with this goal in mind. It is the most popular Python library for generating data visualizations from arrays and other formats of data. It can be used to create several different types of plots such as histogram, scatter, bar, line, and box plots.

We provide online instructor-led live sessions for various courses.

At the end of your respective course you will be provided with a real time project by our instructor to test your practical and applying skills. Once you complete the project, it will undergo scrutiny by the instructor. Only when the instructor is satisfied with your work, you will be rewarded with your certificate.