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why is python considered essential for data science?
Why is Python considered essential for data science?
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7 answers
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Zlatan’s Answer
Hi!
Data science, as the name implies, requires one to be an expert in all things data. This means knowing how to
1. extract data from a given data source
2. manipulate the data into a format that's usable for analysis
3. perform various statistical or machine-learning analyses on that data set
4. summarize the insights into clean tables or visualizations
As it turns out, there are many tools for each of these things, but Python is one of the few tools that does all of these things well. Thus, it's a core skill to make data scientists productive at their jobs without them having to use so many tools that they become inefficient or disorganized. Also, Python is really well integrated into other tools, and it has a huge community of users and experts, so the capabilities of the language keep getting better and better.
Data science, as the name implies, requires one to be an expert in all things data. This means knowing how to
1. extract data from a given data source
2. manipulate the data into a format that's usable for analysis
3. perform various statistical or machine-learning analyses on that data set
4. summarize the insights into clean tables or visualizations
As it turns out, there are many tools for each of these things, but Python is one of the few tools that does all of these things well. Thus, it's a core skill to make data scientists productive at their jobs without them having to use so many tools that they become inefficient or disorganized. Also, Python is really well integrated into other tools, and it has a huge community of users and experts, so the capabilities of the language keep getting better and better.
Updated
Alan’s Answer
Long story short, Python is considered essential because it has a strong ecosystem of data analysis/statistics/mathematics libararies. Its relatively easy to start learning while being quite powerful for automation. Learning Python is great first step towards becoming a data scientist!
Updated
Reid’s Answer
Python was one of the first programming languages I learned in my analyst/data science role. I started with Excel and Visual Basic to create macros and automation within Excel, then T-SQL to interact with databases and big data, then Python. For me, Python was a great tool for performing repetitive tasks/transformations very quickly. This becomes very important when working with big datasets or creating more advanced analytics that involve forecasting/predictions and machine learning to be able to iterate through many operations.
Additional benefits from using Python come from the vast use cases ranging from simple analysis, user interfaces, games, machine learning and artificial intelligence. Since Python is widely used, there is an incredible amount of content and material online to learn from which is very useful when learning but also when finding different ways it can be used to create real solutions (at work). Python can also interface and integrate with many other platforms with all the packages available. Connecting to SQL and Oracle servers to access data, APIs, files on a local or virtual machine. I have not run into many constraints when using Python making it a very powerful tool to understand and utilize. Data analysis/science involves many platforms and databases which Python can handle in a single place to produce a concise result/output for something that would have been either very manual or near impossible without it.
Additional benefits from using Python come from the vast use cases ranging from simple analysis, user interfaces, games, machine learning and artificial intelligence. Since Python is widely used, there is an incredible amount of content and material online to learn from which is very useful when learning but also when finding different ways it can be used to create real solutions (at work). Python can also interface and integrate with many other platforms with all the packages available. Connecting to SQL and Oracle servers to access data, APIs, files on a local or virtual machine. I have not run into many constraints when using Python making it a very powerful tool to understand and utilize. Data analysis/science involves many platforms and databases which Python can handle in a single place to produce a concise result/output for something that would have been either very manual or near impossible without it.
Jennifer Akers
Principal Data Scientist, Data Practice Lead, Professor of Practice
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Jennifer’s Answer
Hi Teja,
Python is considered essential for data science for different reasons, but became the top choice largely due to its integration with other cloud platforms like Azure, AWS, and the Google Cloud Platform (a lot of data is stored in cloud platforms), as well as other tools and technologies.
Python used to be in competition with R for the data science language of choice. Since Python was considered more of a "real" programming language at the time and had the integration and support across clouds and tools to back it up, most practitioners turned to Python for that reason. When you do data science with real clients and projects, you typically integrate with other tools/platforms, needing that support steered you towards Python over other languages. Python was also already in use by software developers who were doing data science, hence the support for it over other languages.
R has come a long way since then, but Python has already "won" the position of data science language of choice. Python is a great pick for other reasons too: extensive libraries that have become the standard for data science work like Pandas, Matplotlib, Scikit-learn and more and great machine learning frameworks like Tensorflow (developed and used by Google), PyTorch, Keras, etc. You can do some really cool things with Python!
From a career perspective, Python is a great language to start learning and once you understand Python, it's easier to pick up other programming languages as well. This will help you grow your skills and give you greater career flexibility as you progress.
I hope that helps!
Python is considered essential for data science for different reasons, but became the top choice largely due to its integration with other cloud platforms like Azure, AWS, and the Google Cloud Platform (a lot of data is stored in cloud platforms), as well as other tools and technologies.
Python used to be in competition with R for the data science language of choice. Since Python was considered more of a "real" programming language at the time and had the integration and support across clouds and tools to back it up, most practitioners turned to Python for that reason. When you do data science with real clients and projects, you typically integrate with other tools/platforms, needing that support steered you towards Python over other languages. Python was also already in use by software developers who were doing data science, hence the support for it over other languages.
R has come a long way since then, but Python has already "won" the position of data science language of choice. Python is a great pick for other reasons too: extensive libraries that have become the standard for data science work like Pandas, Matplotlib, Scikit-learn and more and great machine learning frameworks like Tensorflow (developed and used by Google), PyTorch, Keras, etc. You can do some really cool things with Python!
From a career perspective, Python is a great language to start learning and once you understand Python, it's easier to pick up other programming languages as well. This will help you grow your skills and give you greater career flexibility as you progress.
I hope that helps!
Updated
Patrick’s Answer
Teja, it's important to understand that Python has become a vital tool in the field of data science due to its adaptability, comprehensive range of libraries, and user-friendly nature. Its clear-cut syntax and readability make it a favorite among both novices and seasoned programmers, allowing data scientists to concentrate on finding solutions rather than grappling with intricate coding.
Python's powerful libraries, like NumPy for number crunching, Pandas for data handling, and Scikit-learn for machine learning, offer potent instruments for data analysis, visualization, and modeling. The language's adaptability allows for smooth integration with a variety of data sources and formats, while its scalability caters to projects of all magnitudes, from petite scripts to extensive applications. Python's dynamic community constantly creates and preserves state-of-the-art tools, ensuring that data scientists are always equipped with the most recent techniques and methodologies.
Moreover, Python's compatibility with other languages and technologies makes it a perfect fit for comprehensive data science processes, from data gathering and preprocessing to model deployment and production. This amalgamation of attributes has cemented Python's status as a crucial instrument in the data science arsenal, empowering professionals to effectively draw out insights, construct predictive models, and facilitate data-driven decision-making across various sectors.
Python's powerful libraries, like NumPy for number crunching, Pandas for data handling, and Scikit-learn for machine learning, offer potent instruments for data analysis, visualization, and modeling. The language's adaptability allows for smooth integration with a variety of data sources and formats, while its scalability caters to projects of all magnitudes, from petite scripts to extensive applications. Python's dynamic community constantly creates and preserves state-of-the-art tools, ensuring that data scientists are always equipped with the most recent techniques and methodologies.
Moreover, Python's compatibility with other languages and technologies makes it a perfect fit for comprehensive data science processes, from data gathering and preprocessing to model deployment and production. This amalgamation of attributes has cemented Python's status as a crucial instrument in the data science arsenal, empowering professionals to effectively draw out insights, construct predictive models, and facilitate data-driven decision-making across various sectors.
Updated
Chinyere’s Answer
Hello Teja,
Python is considered essential for data science for several reasons:
1. Ease of Learning and Use:
- Python has a simple and readable syntax, which makes it accessible for beginners and allows data scientists to focus on solving data problems rather than learning complex programming constructs.
2. Extensive Libraries and Frameworks:
- Python has a rich ecosystem of libraries and frameworks specifically designed for data science, such as:
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib and Seaborn: For data visualization.
- Scikit-learn: For machine learning.
- TensorFlow and PyTorch: For deep learning.
3. Community Support:
- Python has a large and active community, providing extensive documentation, tutorials, and forums. This community support is invaluable for troubleshooting and learning.
4. Integration and Versatility:
- Python integrates well with other languages and technologies. It can be used for web development (e.g., with Django or Flask), automation, and more, making it versatile beyond just data science.
5. Data Handling Capabilities:
- Python excels at handling various data formats, including CSV, Excel, SQL databases, and JSON. This makes it easier to import, clean, and manipulate data from different sources.
6. Support for Big Data:
- Python can handle large datasets efficiently and integrates with big data tools like Apache Spark, allowing for scalable data analysis.
7. Statistical and Mathematical Operations:
- Libraries like SciPy provide functionality for advanced statistical and mathematical operations, making Python suitable for scientific research and complex data analysis.
8. Interactivity and Development Tools:
- Python supports interactive environments like Jupyter Notebooks, which are widely used for data exploration, visualization, and sharing results in an understandable format.
9. Industry Adoption:
- Many industries and organizations have adopted Python as the primary language for data science, leading to a high demand for Python skills in the job market.
These factors combine to make Python an essential tool for data scientists, enabling them to efficiently process, analyze, and visualize data while leveraging a supportive community and powerful libraries.
Best wishes!
Python is considered essential for data science for several reasons:
1. Ease of Learning and Use:
- Python has a simple and readable syntax, which makes it accessible for beginners and allows data scientists to focus on solving data problems rather than learning complex programming constructs.
2. Extensive Libraries and Frameworks:
- Python has a rich ecosystem of libraries and frameworks specifically designed for data science, such as:
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib and Seaborn: For data visualization.
- Scikit-learn: For machine learning.
- TensorFlow and PyTorch: For deep learning.
3. Community Support:
- Python has a large and active community, providing extensive documentation, tutorials, and forums. This community support is invaluable for troubleshooting and learning.
4. Integration and Versatility:
- Python integrates well with other languages and technologies. It can be used for web development (e.g., with Django or Flask), automation, and more, making it versatile beyond just data science.
5. Data Handling Capabilities:
- Python excels at handling various data formats, including CSV, Excel, SQL databases, and JSON. This makes it easier to import, clean, and manipulate data from different sources.
6. Support for Big Data:
- Python can handle large datasets efficiently and integrates with big data tools like Apache Spark, allowing for scalable data analysis.
7. Statistical and Mathematical Operations:
- Libraries like SciPy provide functionality for advanced statistical and mathematical operations, making Python suitable for scientific research and complex data analysis.
8. Interactivity and Development Tools:
- Python supports interactive environments like Jupyter Notebooks, which are widely used for data exploration, visualization, and sharing results in an understandable format.
9. Industry Adoption:
- Many industries and organizations have adopted Python as the primary language for data science, leading to a high demand for Python skills in the job market.
These factors combine to make Python an essential tool for data scientists, enabling them to efficiently process, analyze, and visualize data while leveraging a supportive community and powerful libraries.
Best wishes!
Updated
Sendilnath’s Answer
Python is a key player in the realm of data science, primarily due to its simplicity, abundance of useful tools, a nurturing community, and its compatibility with big data and various technologies. What's more, it's completely free, making it universally accessible.
Simplicity in Learning and Usage: Python is designed to be user-friendly and easy to comprehend, enabling even novices to get to grips with it swiftly. You don't have to be a programming whizz to start your data science journey with Python.
A Wealth of Useful Tools: Python comes equipped with numerous built-in tools, known as libraries, that simplify data science tasks.
These include:
NumPy: Assists with calculations and handling extensive sets of numbers.
Pandas: Streamlines working with data tables, similar to spreadsheets.
Matplotlib and Seaborn: Facilitate the creation of charts and graphs for data visualization.
Scikit-learn: Offers ready-to-use tools for machine learning.
TensorFlow and PyTorch: Aid in building and training intricate models, like those used in artificial intelligence.
A Vibrant and Helpful Community: Python is used globally, resulting in a plethora of tutorials, forums, and resources to assist you should you encounter any roadblocks.
Compatibility with Other Tools: Python can be seamlessly integrated with other programming languages and tools, making it a versatile choice for diverse projects.
Jupyter Notebooks: Jupyter Notebooks allow you to write and execute Python code in segments, making it easy to experiment and showcase your work progressively. It's an excellent tool for data exploration and sharing your discoveries.
Ideal for Data Analysis: Python is equipped with tools that simplify the cleaning, sorting, and analysis of data, which are crucial aspects of a data scientist's role.
Facilitates Sharing and Collaboration: Python’s tools make it easy to share your work with others and ensure that they can replicate your results, fostering a sense of teamwork and collaboration.
Simplicity in Learning and Usage: Python is designed to be user-friendly and easy to comprehend, enabling even novices to get to grips with it swiftly. You don't have to be a programming whizz to start your data science journey with Python.
A Wealth of Useful Tools: Python comes equipped with numerous built-in tools, known as libraries, that simplify data science tasks.
These include:
NumPy: Assists with calculations and handling extensive sets of numbers.
Pandas: Streamlines working with data tables, similar to spreadsheets.
Matplotlib and Seaborn: Facilitate the creation of charts and graphs for data visualization.
Scikit-learn: Offers ready-to-use tools for machine learning.
TensorFlow and PyTorch: Aid in building and training intricate models, like those used in artificial intelligence.
A Vibrant and Helpful Community: Python is used globally, resulting in a plethora of tutorials, forums, and resources to assist you should you encounter any roadblocks.
Compatibility with Other Tools: Python can be seamlessly integrated with other programming languages and tools, making it a versatile choice for diverse projects.
Jupyter Notebooks: Jupyter Notebooks allow you to write and execute Python code in segments, making it easy to experiment and showcase your work progressively. It's an excellent tool for data exploration and sharing your discoveries.
Ideal for Data Analysis: Python is equipped with tools that simplify the cleaning, sorting, and analysis of data, which are crucial aspects of a data scientist's role.
Facilitates Sharing and Collaboration: Python’s tools make it easy to share your work with others and ensure that they can replicate your results, fostering a sense of teamwork and collaboration.