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Python for High School Python for High School
Students impacted
2000+
Mode
Classroom / Online
Duration
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

Eligibility

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.


Certification

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


Our Expert Faculties

Syllabus

  • Overview of Python
  • Installation and Environment Setup
  • Basic Syntax
  • Tuples and its Operations
  • Dictionaries
  • Date and Time Functions
  • Data Types (Basic and Mutable/Immutable)
  • Basic Operators
  • Decision Making Loops
  • Numbers and Strings
  • List and its Operations
  • 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
  • Combining Datasets
  • Aggregation and Grouping
  • Pivot Tables
  • Vectorized String Operations
  • Working with Time Series
  • High Performance Pandas
  • Regular Expressions
  • 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
  • Introduction to Data Preprocessing
  • Dealing with Missing Data
  • Cleaning Data
  • Exploring Data Types
  • Normalizing and Standardizing data
  • Data Modeling and Scaling
  • 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
  • Templates
  • Cookies and Sessions in Flask
  • Message Flashing
  • Deployment using Flask
  • Flask Basics
  • URL Building and HTTP Methods

Why Students Trust Us

Fees

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