Data analytics with Python Training

• Web scraping
• Data wrangling
• Data exploration
• Python programming concepts
• Data visualizing
• Mathematical computing
• Hypothesis building
• NumPy and SciPy package
• Scikit learn package for the processing of Natural Language

• First is the self-paced e teaching, which you may access a lifetime. It covers the content that is organized and present by the experts in the industry. You will be assisting with 24x7support from the well-trained experts in data science and Python.
• Next is the blended form of learning, which is the online training course is for 90 days. The course is flexible under the guidance and support of 24 x 7 trained instructors that provide you with lifetime access to the self-paced, high-quality e-learning course materials. It provides you with live recordings in the class; thus, audiovisual training shows a powerful impact on your learning skills.
• Finally, it is the corporate training, known as the customized training with flexible pricing and 24 x 7 support from the trained instructors. The course provides enterprise dashboards for the individuals as well as for the team members that stresses on self-paced, e-learning instructed the based course.

• You should complete at least one project out of the two schemes given for the course. You shall submit all the details of the project within the scheduled time in the LMS that shall execute and evaluated by the training staff.
• You must complete at least 85% of the course.
• You should have attended at least one complete batch.
• The score in any of the simulation tests shall be a minimum of 60%.

1. Simple to use: The reason Python has taken over the programming language and the data science is its simplicity. Python is very simple to understand and quickly readable, which makes a beginner too friendly with a new language. The clarity in the syntax with a shorter learning curve makes learning more straightforward than any other words. On the contrary, it is effortless and quick to write a program in Python compare to any other languages like C++ or Java. Python saves time as it immediately starts the research without reading the documents for long hours. Today, Python is used extensively used for web development, data analysis, text processing, statistical analysis, and many more.
2. Libraries: The comprehensive and varied in libraries add to Python's inherent simplicity. The introduction of libraries makes the program execute faster and in a more straightforward way. The different operations in the library are appealing and attracting the professionals and data scientists to enhance the functions at a faster speed. Among the wide variety in libraries, some of the other may be suitable to your needs and requirements. The popular libraries used for data analytics with python process automation are as follows.
• NumPy libraries are the earliest one to use in data science that is good for scientific purposes. It can solve complex mathematical problems.
• Panda's libraries are capable of doing everything. It can import data from the excel sheets and process the same.
• SciPy is similar is NumPy that comprises all the tools used for analyzing scientific data and numerical integration.
• Apart from the above libraries, there are many other ones too, like the Bokeh, Seaborn, Ggplot, PyMc, SymPy, PyTables, etc. to name a few.
3. Multi-paradigm approach: Compare to other languages that have a limited plan on programming, Python has a multi-paradigm approach. It supports both the procedural, functional, aspect-oriented, and objects oriented programming techniques.
4. Community: Since Python belongs to an open-source community, it is open for discussions and solves problems. It is ready with a solution whenever you have stuck anywhere in between coding or any such thing. It answers online that immediately connects with the community of python members who are available 24 x 7 to assist you. They are the change makers for data scientists and analysts.
5. Excellence in enterprise application integration: Python recommends application scripting providing the strong alliance with C, Java, and C++. Moreover, Python serves as a useful tool for testing the software, text processing, and inclusion. It has a massive network for the construction of applications requires for the desktop.
6. The Jupyter notebook: whoever is working with Python may acquaint with the jupyter notebook, as it is an open-source for the coders to write amazing codes. A handy tool highlights the findings and the results in the same system, making it simple to use.

• Easy to learn – python is a primary language to learn compared to any other tool even when you do not possess any knowledge about coding. The thing that scares a nonprofessional becoming a data scientist is the knowledge about the skills in coding. Python makes it simple and easy.
• The variety and powerful packages – Python comes with many packages like the pandas, SciPy, NumPy, etc. These packages make coding simple and easy to solve complex data in the program of data analytics.
• It is compatible with Hadoop - python belongs to an open-source community and is compatible with big data Hadoop. Thus, the combination of Python and Hadoop, are together known as the pyDoop, is liable to solve complex problems in little time and less effort.
• It provides better data visualization – All types of data, whether it is big or small, is easy to visualize using the Python. The data scientists find easy viewing of data and understand it in a better way.
• It has robust community support – Data analytics with python process automation find it very easy to get the solution to all the problems in a short time. The active community provides 24 x 7 supports of new libraries and varied packages for various uses by the data scientists.
• High speed in development and processing – The vital importance is the speed that is the key to be successful in the industry. A slow language is challenging to handle astronomical data, so Python is the solution to solve complex data, as it is clean and comfortable to handle. Initially, the execution of Python was slow, but the introduction of Anaconda had solved this problem too in recent years.

Yes, you can attend a demo session before joining the course at Trishana technologies, the best training institute in Bangalore.

For solving all your queries and doubts, you can call us on +91 9513015050 or email us at info@trishanatechnologies.com) which are our dedicated helpline services.

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