Python for data science Course

Its creators define the Python data language as an interpreter and high-level programing language with dynamic semantics. It is one of those programing languages which is quite useful for Rapid Application Development. It is also known as a general-purpose programing language which can be used in the development of both desktop and web applications.

This course enables you with a programming base, specifically designed for the professionals who want to learn python to use in data science, machine learning, artificial intelligence and other technologies.

What will you learn during the Python for data science Certification Course?

For making a lucrative career, a thorough knowledge of different scientific and statistical models of Python is imperative. This is where Python-related seminars and workshops play a crucial role. Skillsets such as SIMD vectors, concurrent algorithms, and multicore programing are being taught exclusively in these workshops. Here is the list of aspects which underlines the importance of attending a Python workshop.

  • Learn Python tools and libraries

By attending a Python-related workshop, you can take your predictive modeling and data analytics skills to the next level. You would learn how to use Python tools and libraries.

  • Python’s Data Science Stack

Iteration and conditional constructs are a standard part of Python. Now you can learn about them at specialized workshops.

  • Makes you a ready reckoner of test cases

A ready reckoner for test cases makes you eligible for various types of interviews in leading MNCs.

Requirements for Python for data science Course

There are no such elaborate eligibility criteria for attending this workshop. However, you should have a sound knowledge about the basics of Python.

Who can benefit from this course?

Anybody who is interested to master the concepts of the widely used and powerful programming language, Python. Through this Python for Data Science Course, you will gain hands-on experience in working with various Python packages like SciPy, NumPy, Matplotlib, Lambda function and more. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.

Course Curriculum

  • A Brief History of Python.
  • Python Versions.
  • Installing Python.
  • Environment Variables.
  • Executing Python from the Command Line.
  • Editing Python Files.
  • Python Documentation.
  • Getting Help.
  • Dynamic Types.
  • Python Reserved Words.
  • Naming Conventions.
  • Basic Syntax.
  • Comments.
  • String Values.
  • String Methods.
  • The format Method.
  • String Operators.
  • Numeric Data Types.
  • Conversion Functions.
  • Simple Input and Output.
  • The % Method.
  • The print Function
  • Indenting Requirements.
  • ​The if Statement.
  • ​Relational Operators.
  • ​Logical Operators.
  • ​Bit Wise Operators.
  • ​The while Loop.
  • ​break and continue.
  • ​The for Loop.
  • Lists
  • ​Tuples.
  • ​Sets.
  • ​Dictionaries.
  • ​Sorting Dictionaries.
  • ​Copying Collections.
  • Defining Your Own Functions.
  • ​Parameters.
  • ​Function Documentation.
  • ​Keyword and Optional Parameters.
  • ​Passing Collections to a Function.
  • Variable Number of Arguments.
  • ​Scope.
  • ​Functions – “First Class Citizens”.
  • ​Passing Functions to a Function.
  • ​Mapping Functions in a Dictionary.
  • ​Lambda.
  • ​Inner Functions.
  • ​Closures.
  • Modules
  • Standard Modules – sys.
  • Standard Modules – math.
  • Standard Modules – time.
  • The dir Function.
  • Errors
  • ​Run Time Errors.
  • ​The Exception Model.
  • ​Exception Hierarchy.
  • ​Handling Multiple Exceptions.
  • ​raise.
  • ​assert.
  • ​Writing Your Own Exception Classes.
  • Data Streams.
  • ​Creating Your Own Data Streams.
  • ​Access Modes.
  • Writing Data to a File.
  • ​Reading Data From a File.
  • ​Additional File Methods.
  • ​Using Pipes as Data Streams.
  • ​Handling IO Exceptions.
  • ​Working with Directories.
  • ​Metadata.
  • The pickle Module
  • Classes in Python.
  • ​Principles of Object Orientation.
  • ​Creating Classes.
  • ​Instance Methods.
  • ​File Organization.
  • ​Special Methods.
  • ​Class Variables.
  • ​Inheritance.
  • ​Polymorphism.
  • ​Type Identification.
  • ​Custom Exception Classes.

Key Features of Python for data science Course Training

  • Learn Python Tools and Libraries
  • Python’s Data Science Stack
  • Makes you a ready reckoner
  • Learn from Scratch to Advanced Level

Benefits of Python for data science Course

The esteem of Python as well as career opportunities is gradually rising. Google has now adopted Python as its secondary coding language. Interestingly, the career opportunities in Python are quite enticing. However, it is a big plus for the organization hiring you if you have knowledge about Python Programing. Here is a list of institutional and individual benefits of being certified in Python.

  • Python is flexible, portable, and can run on any platform quite smoothly. It is scalable and can be integrated with other third-party software easily.
  • Helps the organization to define variables automatically.
  • With Python, the program need not be compiled.
  • Comprises of a rich set of libraries and tools.
  • With a Python Certification, you can merit your skills which makes you marketable.
  • Python helps you to grasp the basics of Deep Learning.
  • Python is flexible, and this is the reason it allows data scientists to develop machine learning models.
  • Python empowers you to solve problems end to end.
  • With the surge in demand for data scientists, Python helps you to land your dream job.
  • Python helps you to become a data scientist. Interestingly, data scientists are the highest paid IT Professionals, and as per a recent report, the growth of a data scientist is almost double the average.


In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

On successful completion of the course, you will receive a course completion certificate issued by

On completing this course, you will be competent in:- Using Jupyter Notebooks in Anaconda- Creating user-defined functions

  • Manipulating & analyzing data
  • Visualizing data using Python libraries like Matplotlib and Seaborn

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of the written request for a refund. Kindly go through our Refund Policy for more details.

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