It has been less than a decade since data science crashed upon mainstream industries like a oceanic wave and it has already become part and parcel to every operation. Data literacy is an important phenomenon. A number of companies are making the effort to educate and upskill their existing staff to cope with the demands of the changed times. And an even larger number of companies are looking for people who are trained in the field of data analytics and data science. Intuitive decision making is a thing of the past – each step is measured with the help of data. From determining what advertisement you will see more often on YouTube to which truck at a logistics firm needs new engine oil, everything is mostly automated with the help of data driven insights.
So, now we are talking about machines that can recognize patterns in data, much like we, human beings do. Just like we see a cloud and infer the possibility of rain a trained machine crawls through millions of bytes of data to recognize patterns and gain knowledge.
You can already imagine that such complex mechanism need equally effective tools and rigorous programming. Fortunately enough Python came as a great solution for data science professionals. Famous for its simplicity, this language can accommodate the hardest and most complicated of algorithms. For the last few years Python has been the most trusted data science correspondent. A Python certification can actually give you a much better chance of landing a data scientist job.
Globally more than 40 % of all companies use python. With its extensive libraries, more than 70,000 at the last count make it one of the most popular tools for data analysis.
When opting for Python certification, make sure your curriculum teaches you to proficient in handling the various libraries that are available for the assistance of the programmers and analysts. Some of the popular Python libraries are:
- NumPy- considered as one of the most popular library in Python. NumPy is highly interactive, intuitive, makes complex application extremely easy and has contribution from varied sources.
- PyTorch- this has been the most popular machine learning library since its inception in 2017. It has become a popular choice because of its advanced features like hybrid front end, distributed training, and it is deeply ingrained with the basic functioning of Python, so that its compatible with other libraries.
- SciPy-it is developed on NumPy and uses most of its advanced features and helps in optimisation and mathematical integration amongst other things.
- Pandas-this has emerged as the go-to tool for data analysis and can translate complex data in just a couple of commands. Handling humungous amounts of data becomes relatively easier with Panda.
- Light GBM- this is the library that is chosen by machine learning developers and is preferred over competitive libraries because of its intuitive features and quick computation features that provides efficient result.
Industries that implement Python for Data Analysis
- Banking- providing a holistic banking experience for the customer, along with mitigating fraudulent risks and curbing any other malpractice that might threaten financial security.
- Ecommerce- by analysing customer buying patterns, ecommerce portals offer customised pattern and help in marketing products and reselling products.
- Gaming- hacking of personalised data is always at threat for the gaming industry, where the shelf life of every service is limited. Data analysis using Python helps to protect and provide greater gaming experience.
Get a Python certification and enjoy working with top organisations and receive hefty annual packages. Happy working!