Learn Python for Data Science in the Right Way

16-Dec-2020

It is widely known that Python is one of the most common programming languages used in the field of Data science by data scientists. So many aspiring data scientists set out to learn Python but the approach they follow is usually misdirected and not efficient with respect to time. They start by learning the basics of programming and solve programming exercises, make small software applications, develop games, and study software development paradigms and algorithms.

This is the approach of learning python for becoming a software developer. However the key difference between using python for software development and using python for data science is that in the case of data science data scientists rarely build software projects from scratch. Their main area of work lies in manipulating data and to this end they use some fairly well established and well defined functions written in Python. These functions are bundled up in modules and libraries which are most often written entirely in Python and sometimes written in other programming languages but even in those cases they provide bindings written in Python. This is because Python is the most popular programming language for Data science. These modules and libraries are very well established in the worldwide data science community and so it is best for aspiring data scientists new to the field to make them their starting point.

A great way to learn Python for data science is to take Careerera’s Post graduate program in Data Science course.

Start to Setup Your Programming Environment -

The best programming environment for Data science beginners is the Jupyter programming environment. It is packed with many useful features which save data scientists a lot of time and energy because they do not have to write programs to get access to those features from scratch. One can put many of the elements commonly involved in data science together in one document such as plots, formulae, comments, images, and code. One can execute python code, generate graphs and plots, input formulae in Latex or ASCII, and even use different programming languages in the same notebook.

Sharing notebooks is an absolute breeze and these sharing and integration features bundled together in a single simple interface is what has made Jupyter notebooks so popular among data scientists.

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. All these steps are covered in our PGP in data science course.

One should also know about the 'Important Skills That a Data Scientist Must Have'

Learn the Fundamentals of Python Quickly -

An aspiring data scientist may be tempted to think that they can get by simply by learning the modules and libraries of Python and stringing function calls together, but this is not the case. During the course of their career they will have to write original code very often. They will have to do this because for their particular purposes they will require customized code and programs which they themselves will have to produce.

There are several websites on the internet which offer Python courses for free. One should learn Python till they reach the topics of object oriented programming and generics and stop there and move on to learning the particular Python modules and libraries. There is no need to dive into the more advanced concepts of Python since data science rarely has a use for them. You can learn the basic and core concepts of Python by taking a data science certification course.

Become Familiar with Numpy and Pandas -

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.

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.

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.

Move on to data visualization with 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.

All these libraries will definitely be covered in any decent data science certification training course.

Learn how to use SQL with Python -

In most organizations, the electronic data which they generate and use in their day to day operations is stored in databases. SQL is the most popular and well known programming language used to store, manipulate, and retrieve data in databases. So the process data scientists follow most often is to store the important data in a database and retrieve it using SQL commands. After that, they analyze the data in Jupyter notebooks.

By coupling SQL and Pandas, one gains a number of options for querying the data, processing the data, and using the data for one’s project. A good way to use SQL and Python together is to install an SQLite database, store the data from the database in a .csv file, import it into SQLite, and analyze it using Python.

Moving further on -

Till this point in the post the basics of getting started with Python for data science have been covered. After this one can branch out their knowledge areas with further learning. One can learn how to perform statistical processes in Python by writing statistics functions. One can also learn how to set up machine learning algorithms in Python using the popular Scikit-Learn implementation library.

After this it is a matter of practice and exposure. The beginner data scientist should find a number of data sets of various kinds. Then they should evaluate and analyze these data sets to glean meaningful insights and answer significant business questions. These make for interesting projects. Some project ideas are extracting patterns from your personal amazon.com spending habits, generating a visual map of the election polling by state, and building an application which predicts the weather in your area or one that makes stock market predictions.