The most popular programming language for data science is Python. A data scientist is one of the top requirements in the job market. Data science, in simple words, is the extraction of meaningful and related data from raw data. Data science is responsible for bringing us new products, customizations, delivering breakthrough insights, and making our lives easier. It involves drawing out patterns and connections in the data collected. Data is considered as a resource. It provides a lot of information to the companies to enhance their services or provide new services to their customers. The customization is the most revolutionary contribution of data science. You get recommendations based on your likes and dislikes on streaming platforms, friends’ suggestions on social media, product suggestions on online shopping websites, and so much more.  

Data science is used in healthcare to keep a track of the patients and provide better diagnostics and treatment. The prediction of market trends of shares is another contribution of data science. So, you can conclude that data science is everywhere, in every aspect of our lives. Data science is the future. This field is not going to run out of opportunities for some time and is an excellent career option. Data sciences have 5 stages in its lifecycle: 

Acquire data: Data, acquisition, data entry, data collection, data extraction 
Maintainance of data: Data processing, data architecture, data cleansing, data staging, data warehousing 
Process data: Data mining, data modeling, data summarization 
Communicate: data visualization, business reporting, decision making 
Analyze: Exploratory, predictive analysis, regression techniques, qualitative analysis. 
What makes Python the preferred choice for data scientists? 
Before we discuss the benefits of Python certification, we must understand the role of Python in Data science that makes it a preferred choice among other programming languages. Here, we discuss some of the key points why you should learn data science course
The simplicity of learning: Python is a beginner-friendly language. It has an English-like syntax that enhances code readability. Along with this, it is a dynamically typed language which means you need not assign a data type for every variable. Python automatically assigns a data type to the variables during execution. This allows the developer to focus on the logical building of the code rather than on the complex syntax. 
Data science libraries: With the rise of data science, every platform evolved accordingly. Python, being flexible and open-source adapted well to this technology. It has a collection of libraries and APIs to facilitate the handling of large data and performing operations on them. Many libraries have been added over the years and the list gets updated accordingly. Libraries also provide integration features that allow cross-platform development. It has libraries like Cython and Jython that can help you to incorporate the functionalities of these programming languages into your code. You can compile your code on any language and use it in your code. Also, Python is compatible with many operating systems like Windows, macOS, Raspberry Pi, and Linux. 
Handling large volumes of data: Python comes with libraries, connectors, and frameworks that allow it to connect with various applications and data sources and perform data analytics tasks. This makes Python a preferred choice for web development, natural language processing, artificial intelligence, data analytics, and machine learning. Some of the connectors in Python are: 
a.   ORACLE: cx_oracle 
b. SQL ALCHEMY: sqlalchemy 
c. IMPALA: impyla 
d. SQL LITE: sqlite3 
e. PUG SQL: pugsql 
Community support: Python boasts of very active and large community support. There are discussion groups and forums on StackOverflow, Reddit, and similar platforms that allow you to post your doubts and queries. There are IT professionals and experts in the groups who resolve your doubts and can provide you the required mentorship. This also helps you to enhance your network. These professionals work towards adding libraries and features to Python and improve it.  
Knowledge of programming language is a key skill to become a data scientist. The reasons to justify this are as follows: 
Knowledge of programming language allows you to work on a large volume of unstructured and structured data. There are a number of data sets available online where you can practice your skills. 
Knowledge of statistics along with programming helps you to manipulate and analyze data. Graphs, charts, maps, etc are various ways to visualize data. Programming languages like Python comes with libraries to allow easy data visualization. 
Knowledge of programming language allows a data scientist to create frameworks to automatically analyze experiments, manage the data pipeline in large organizations in a way that it can be accessed by the right person in the right way.  
Certification in data science with Python will develop elementary programming knowledge that you can apply in your real-time projects and will help you to kickstart your career in the same. The content of the certification program must include the following: 
Tools and technologies 
Exploratory data analysis 
Python basics 
Data visualization using Python 
Statistics for data science 
Advanced statistics and predictive modeling 
The skills and qualifications for a person to become a data scientist are as follows: 
Mathematics and statistics 
Machine learning 
Data mining 
Data munging 
Data visualization 
Apart from these primary skills, other important skills are proficiency in deep learning frameworks, cloud tools like Amazon S3, SQL database, and database querying languages, Big data platforms, etc. Certification allows you to gain elementary knowledge in these technologies and understand the core concept of data science and how Python helps in this technology. The business skills required to become a data scientist are as follows: 
Analytic problem solving 
Communication skills 
Intellectual curiosity 
Industry knowledge 
To have a better understanding, some hands-on experiments are included to give you a real-time experience of how industries work. This will build your confidence and you can assess your knowledge and skills through these projects. Some of the projects that can be included in the certification program are as follows: 
Predict credit card defaulter using logistic regression 
Predict the quality of wine decision tree 
Predict chronic kidney disease using KNN 
Predict house price using linear regression