Data science is all about exploring massive data sets to extract valuable information from them and convert it into actionable solutions. To be a successful data professional, one must be able to analyze data using Python.

Why Python, you ask?

Well, it is not only one of the most popular programming languages among coders and developers, but today, it has gained an enormous following in the data science community. The greatest feature of python has to be its simplicity. The precise and efficient syntax of this language makes it particularly beneficial to accomplish specific tasks faster than other languages. Furthermore, Python is backed by an active data science community, so you will never have trouble in finding fixes to bugs.

Thanks to the internet, learning data science has now become easier than ever! Today, numerous online platforms are offering specialized data science courses to help you get started in the world of data science.

Let’s now look at the important terms, concepts, and libraries needed to conquer Data Science using Python. Here’s our take on learning Data Science using Python:

1. Python For Data Analysis

Python is an intuitive and explicitly designed, powerful general-purpose programming language. Its simplistic design of this open-source language is not only easy to understand but it also significantly minimizes the time commitment for writing codes. A recent study maintains that nearly 80% of the top ten CS programs in the US choose Python in their introductory courses due to its simple structure and syntax. And now, Python is gaining increasing traction among data science professionals as budding data professionals are using it as a tool for data analysis.

2. Python Data Structures

Data structures lay down the foundations for the relationship between data and the operations that can be performed on data. They help store and organize data so that they can be accessed anytime, anywhere. There are many kinds of data sctructures in Python that are widely used by computer engineers, software developers, and data scientists to find solutions to complex issues.

A working knowledge of these data structures goes a long way in helping you understand the working of Machine Learning/Data Science libraries. Let’s look at some important data structures you should learn before proceeding further:

Lists – Lists are a highly versatile Python data structure that is used to store stacks of differentiated items. The signature feature of lists is that the elements in them are separated by commas and are contained within square brackets. They are mutable which means that you can alter the content of lists without changing their identity.


Strings – Strings refer to a collection or sequence of words, alphabets, or other characters. They can be defined using single (‘), double (“), or even triple (“‘) quotes. For instance, ‘cookie’ and “cake.” Unlike lists, Python strings are immutable meaning their content cannot be changed.


Tuples – Tuples are data structures containing a sequence of values or alphabets separated by commas. They are enclosed by parentheses to facilitate accurate processing. Tuples are immutable, that is, the values contained within it cannot be altered. Being immutable, tuples are processed faster than lists.


Dictionary – Dictionaries refer to an unorganized set of key-value pairs. Each dictionary has unique keys enclosed within curly brackets. While the keys are used to identify items, the value corresponds to the value of the elements contained within a dictionary.


3. Python Libraries

Python offers an impressive array of resourceful libraries for data analysis and machine learning. Libraries essentially refer to stacks of pre-existing objects and functions that can be seamlessly integrated within your code, thus saving a significant amount of time and energy.

Here’s a list of Python libraries that are excellent tools for data analysis:

1. NumPy

NumPy is the abbreviation for Numerical Python. It comprises of basic linear algebra functions, advanced random number capabilities, Fourier transforms, and other tools that can be amalgamated with low-level languages like C and C++. With its most robust feature being the n-dimensional array, NumPy facilitates efficient numerical computation.

2. Matplotlib

Matplotlib is a highly efficient and flexible plotting and visualization library that allows for plotting of a host of graphs such as line plots, heat plots, histograms, etc. although it is a very resourceful tool, using it can get difficult at times.

3. SciPy

Based on NumPy, SciPy stands for Scientific Python. It can be used to accomplish tasks associated with advanced science and engineering concepts such as Linear Algebra, Fourier transform, and Sparse Matrices.

4. SymPy

SymPy is a Python library designed explicitly for symbolic computation. It is packed with many useful features ranging from basic symbolic arithmetic to calculus, algebra, and quantum physics.

5. Pandas

Based on NumPy, Pandas is a high-performance library for exploratory analysis. It is widely used for data munging, structured data operations, and manipulations.

6. Scikit Learn

Designed on NumPy, Matplotlib, and SciPy, Scikit Learn is a premium general-purpose machine learning library. It contains an array of useful tools for statistical modeling and ML such as classification, regression, and clustering.

7. Statsmodels

As the name suggests, Statsmodels is a library designed for statistical modeling. It facilitates data exploration, estimation of various statistical models, and also allows users to conduct statistical tests. It contains a varied range of descriptive statistics, plotting functions, statistical analyses, and result statistics to be used for specific kinds of data.

8. Seaborn

Matplotlib based Seaborn is an efficient tool for statistical data visualization. It allows users to create informative and appealing statistical graphics in Python. Exploring data and analyzing it forms the core of Seaborn’s visualization feature.

9. Bokeh

Bokeh is a useful Python library ideal for designing interactive plots, dashboards and data applications. It allows users to create neat and It empowers the user to generate elegant, precise, and versatile graphics. Apart from this, Bokeh promotes high-performance interactivity over streaming or massive datasets.

10. Blaze

Blaze was designed to amplify the features of Numpy and Pandas. It is extensively used to gather data from multiple sources including Spark, SQLAlchemy, PyTables, and so on. When combined with Bokeh, Blaze can become an excellent tool for creating visualizations and dashboards from enormous chunks of raw data.

11. Scrapy

Scrapy is a highly useful library for web crawling – it allows users to create a website home URL and then sift through thousands of relevant web pages to accumulate insightful information. It is a convenient tool for generating specific data patterns.

4. Exploratory Analysis Using Pandas

Pandas is one of the most efficient tools for data munging. Since data munging is an integral part of data science, Pandas has now become an instrumental tool in the data science community. Series and Data frames form the core structure of Pandas.


Series refers to a one-dimensional labeled or indexed array. In Series, users can access individual elements with the help of labels. A dataframe, on the other hand, is more like an Excel worksheet – there are columns with names, and also there are rows with row numbers.

When it comes to mastering Data Science, Python is the whole package. With resourceful data structures and libraries, it has everything one needs for dynamic data munging, data analysis data visualization, and predictive modeling. Furthermore, the fact the Python can be conveniently integrated with databases like Hadoop and Spark makes it even more appealing for data science professionals.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.