This Course will introduce you to Python and how to use it for statistical data analysis, Data Management Machine learning and Data Visualization. in-depth Sessions will be delivered on python ecosystem, Libraries like- NUMPY, SCIPY, PANDAS, SCIKIT-LEARN, matplotlib, SEABORN .

• Flexible Timing
• Course Material Sharing
• Live assignment
• Downloadable recordings
• s/w installation help
• Post-training email support

• Weekend Class Timing
• 100% Live Practices
• Daily assignment
• access to prerecorded sessions
• s/w installation help
• Post-training live support

• Delivery models ( ILT online / instructor-led onsite )
• Need-based Content analysis & customization
• Access to the learning management system (LMS)
• Pre and Post Training Assessments
• Post-training email support & Feedback reviews

In recent years, a very large amount of business data is generated by companies and Social media. Approximately- Daily 2.5 billion GB of data are created-(according to IBM report)

In this competitive market such data, create an opportunity for the companies to deliver excellent customer experience and services. However, enterprises have set up their big data platform to handle big data using the platform like, Hadoop, Spark and nosql. The big data platform can collect and handle data efficiently, but the later challenge is in terms of analyzing data and get insights to make it meaning full to develop a high standard business process.

Data science and machine learning Techniques to help in unlocking data insights using the statistical Data Modelling & algorithms. Despite conventional Business analytics, based on past data- performance, data science can handle and identify the unidentified data patterns, beyond traditional data analysis it enables to analyse unstructured data, such as speech, text data & even image data to get more accurate insights.

In simple words’ Machine learning is, “Building models of data “.

Machine learning is built of statistical or mathematical models to understand the data as information. Machine learning is the next level of data mining, which can complement human decisions up to the certain extent by taking actions automatically based on patterns received.

This Course will introduce you to Python and how to use it for statistical data analysis, Data Management and Data Visualization.

Before data can be analyzed, there are multiple steps involved, including Data preparation, Data import, cleaning and transforming data. This course is comprised of 2-parts:

• An overview of basic and intermediate Pandas, demonstrating how effectively panda manipulates datasets in memory. You will have hands on experience on indexing, alignment, join/merge methods, date/time types, and handling of missing data.
• Another part of this course will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, statistical data modeling using some of the advanced functions in Numpy,Scipy and Pandas.

Why Python for Data Analysis?

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Python is an easy language to learn and is a very powerful solution for complex Data Analysis tasks.

Python was developed and used for the first time in 1991 by Guido Van Rossem. Since then Python has become one of the most popular, dynamic, programming languages in the world of Computer Programming. Python is basically a scripting language, which is easy to learn and quick to code in form of scripts.

Python is also very popular among Mathematicians and Data analysts. It is an open source tool– free to install along with online community support.