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Python for High School Python for High School
Students impacted
Classroom / Online
30 Hours
Batch Schedule
Weekdays / Weekends

Program Highlights

  • Exclusive Curriculum, Curated by IIT Alumni
  • Designed Specially for the 14-18 Years Age Group
  • Experiential Learning Approach
  • Industry Experienced Faculty
  • Periodical Practical Assignments
  • Live Projects & Case Studies
  • Personal Interaction with Mentors
  • Internship Assistance
  • Dedicated Doubt Solving Sessions
  • No-Cost EMI Options


Minimum eligibility criteria for the program

Age group of 14-18 years.

While there are not many educational criteria for this course, curiosity & keen interest in data science or programming and coding is an essential prerequisite.


Receive your Zell Certified Data Scientist (Professional) Certification upon successful completion of all the program modules and end-of-program assignments. 

Our Expert Faculties


  • Overview of Python
  • Installation and Environment Setup
  • Basic Syntax
  • Date and Time Functions
  • Data Types (Basic and Mutable/Immutable)
  • Basic Operators
  • Decision Making Loops
  • Numbers and Strings
  • List and its Operations
  • Tuples and its Operations
  • Dictionaries
  • Files and I/O Handling and Manipulation Exceptions
  • Classes/Objects
  • Introduction to Numpy
  • Ndarray Object
  • Arrays using Numpy
  • Indexing and Slicing in Numpy
  • Mathematical Functions
  • Introduction to Pandas
  • Pandas Objects
  • Data Indexing and Selection
  • Operating on Data
  • Handling Missing Data
  • Hierarchical Indexing
  • Pivot Tables
  • Vectorized String Operations
  • Working with Time Series
  • High Performance Pandas
  • Regular Expressions
  • Combining Datasets
  • Aggregation and Grouping
  • Introduction to MatPlotLib
  • Types of Graphs
  • Setting the Axis, Ticks, Grids
  • Defining the Line Appearance
  • Using Labels, Annotations and Legends
  • Introduction to Seaborn
  • Plotting Categorical Data
  • Statistical Estimation
  • Linear Relationships
  • Cleaning Data
  • Exploring Data Types
  • Normalizing and Standardizing data
  • Data Modeling and Scaling
  • Introduction to Data Preprocessing
  • Dealing with Missing Data
  • Data Sources and its types
  • Choosing Data Sources
  • Data Exploration
  • Handling exceptions passed from MySQL
  • Creating and Dropping Database and Tables using python
  • Beautiful Soup
  • Selenium Web Driver
  • Data Extraction and Data Processing
  • Message Flashing
  • Deployment using Flask
  • Flask Basics
  • URL Building and HTTP Methods
  • Templates
  • Cookies and Sessions in Flask

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