10 Python Reasons

Why should you learn Python?

It’s easy: Anyone can learn Python, from entry-level coders to senior technologists. Python uses very simple syntax comprising elements from the English language, making it easier to write, read and learn. By removing unnecessary syntax and code, Python is an approachable language. For example, something that might take 4-5 lines of code to produce in one language, only takes 1 line in Python.

It’s versatile: Python has a range of different programming functionalities, from data science, to artificial intelligence, and gaming to web development. Python is used across various industries, from creative to technical, and is highly adaptable.

It’s career boosting: Learning Python gives you an advantage over other technologists, as it’s a skill you can apply to different industries. The career advancement potential for python developers is huge, with Python specialists among the highest-paid in the industry, particularly in the fields of web development and data science. Some of the world’s most exciting companies, from Google to GitHub, are calling out for the skills of Python developers and Data Science professionals.

10 Python skills for Data Scientists

1. Python: Obviously. Fortunately, Python isn’t the most challenging language to learn. But you’re going to need to spend a good amount of time grasping not just the fundamentals of the language, but advanced topics as well. You should ensure you’ve honed your python skills to incorporate core Python, including: data structures, data types and variables, exception handling, file handling, generators, iterators, and object-oriented programming.
2.  Web frameworks: You should have an understanding of how to use web frameworks, in order to develop and deploy web applications successfully, and to be able to fully utilize your Python skills. Django and Flask are the two most popular frameworks and essential to Python developers. Django, a high-level web framework that encourages rapid, clean, and pragmatic design, offers a large number of libraries that make it possible to create high-quality web applications without having to reinvent the wheel. Flask is a micro web framework that doesn’t depend on any particular tools or libraries, with no database abstraction layer, form validation, or any common functions provided by third-party libraries. Flask is considered a template engine that includes its own modules and libraries that make it possible to write web applications without writing low-level code.
3.  Object Relational Mapper (ORM): An object-oriented programming language is necessary for converting data between two systems that are incompatible – this technique is called Object Relational Mapper. From any programming language, a virtual object database is created by Object Relational Mappers. Programmers also use many customizable ORM tools. By using ORMs, Python developers can stick to Python code, instead of having to use SQL to create, read, update, and delete data schemas. 
4. AI and Machine Learning: Data Scientists must have a decent understanding of AI and Machine Learning, particularly in Machine Learning Algorithms – a subset of AI that aims to create systems that can automatically learn from data patterns. AI and Machine Learning is essential if you’re using Python within the realm of data science, where you might work with neural networks, data visualization, data analysis, and data collection.
5. Front end technologies: Alongside a deep knowledge of Python, you’re going to need to an understanding of how the front-end of your web application will work. For this, you should hone your knowledge of the three primary front-end markup languages for Python: HTML5, CSS3, and JavaScript. Python is perfectly capable of generating all three markup languages via parsers, compilers, and trans-pilers.
6. Deep Learning: Deep learning, an important factor in Data Science, is a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data. It also includes the concept of Neural network architecture. There are many aspects of Deep Learning, including: Audio Processing, Video Processing and NLP ( Natural Language Processing).
7. Apply Data Science techniques: Let’s face it, as a Data Scientist you’ll already have mastered the fundamentals of Data Science. But let’s remind ourselves of the essentials you’ll need to apply to your Python skills:
1. Good Knowledge of SQL
2. Knowledge to use Python packages such as Scikit learn and MatPlotib
3. Cleaning up of Data
4. Data Wrangling
5. Analysis of Data
6. Mathematical Knowledge
7. Knowledge of Probability
8. Knowledge of Statistics
8. Data visualization: Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs and maps, data visualization tools provide an accessible way to see and understand trends, outliers and patterns in data. In the world of big data, data visualization tools and technologies are essential for analyzing massive amounts of information and making data-driven decisions. Data visualization in python is perhaps one of the most utilized features for data science with python today. Python libraries come with lots of different features that enable users to make highly customized, elegant, and interactive plots (see two examples below).
9. Pandas: No, I’m not talking about the cute furry mammals. The Pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. Pandas is an open source Python library that allows the handling of tabular data (i.e. explore, clean and process). The term originated from the econometrics term panel data and thus PAN(el)-DA(ta)-S. Pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive. At a high-level, Pandas works very much like a spreadsheet (i.e. think Microsoft Excel or Google Sheets) as you work with rows and columns. Pandas serves as one of the pillar libraries of any data science workflow as it allows you to perform processing, wrangling and munging of data. This is particularly important as many consider the data pre processing stage to occupy as much as 80% of a data scientist’s time
10. Numpy: Numpy is Python library that provides mathematical function to handle large dimension array. It provides various method/function for Array, Metrics, and linear algebra. NumPy stands for Numerical Python. It provides lots of useful features for operations on n-arrays and matrices in Python. The library provides vectors of mathematical operations on the NumPy array type, which enhance performance and speeds up the execution. It’s very easy to work with large multidimensional arrays and matrices using NumPy.