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Applied Data Science with Python (Course code: ALS04211116)

Applied Data-Science Online training with Python incorporates multi-disciplinary domains comprising of computer-science, programming, information theory, statistics and artificial intelligence, to name a few. It focuses more on the application of statistical approaches, machine learning, information visualization, social network analysis and text analysis in order to gain insight into applied data ananlysis. Talking about Python programming, the aforementioned feats can be achieved by using various Python toolkits such Pandas, Matplotlib, Scit-learn, Ntlk and Networkx among many others. Online Data-science training involves the acquisition of full-stack Data-science skills to complete the entire problem structure using various Data-science tools.

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Course Details

The Python online training begins with an overview and basic know-how about how to apply Data-science. Along with Data-science, an introduction to Python is also made. Closely following the overview, a Python tutorial about the various tools that may be used in data-manipulation and analytics are introduced and described. As we dwell deeper, various Data-science concepts will be taken into account with reference to the introduced Python tools.

Section 1: Python Basics

Learn the basic methodologies and environment of Python online with different data types and variables while focusing on data-science as the end-result.

Section 2: Python Lists

Understand the use of different data types under a single name using Python lists. Learn to create, subset and manipulate lists in various ways.

Section 3: Functions and Packages

Learn the intricacies using packages and calling functions in order to make the best use of Python Libraries.

Section 4: Numpy

Learn to code with superfast speed using numerical Python. Get efficient in storing and performing calculations on huge amounts of data.

Section 5: Matplotlib

Make us of different types of visualization depending on the type of end result. Build complex and custom plots using real data in order to portray your results.

Section 6: Control flow and Pandas

Understand the use of conditional constructs iin order to tweak the execution of scripts to understand the Pandas DataFrame, the fundamental element of data science in Python.

Live Instructor-led & Interactive Online Sessions

Regular Course

Duration : 40 Hours

Capsule Course

Duration : 4-8 Hours

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Training Options


Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM

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