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Data Science with Python Course Syllabus

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Our Data Science with Python Training in Chennai provides comprehensive instruction in Python programming, data manipulation, machine learning, and practical application development. You’ll gain proficiency in analyzing data, constructing predictive models, and visualizing insights using Python libraries such as Pandas, NumPy, and Matplotlib. Hands-on projects and industry-focused training ensure you acquire practical skills essential for real-world applications. With Data Science with Python Course with 100% Placement Support, you’ll be well-prepared for a rewarding career in data science. This training not only enhances job readiness but also offers placement assistance to help you secure opportunities in this dynamic and rapidly expanding field of data analytics.

Course Syllabus

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Introduction of Python
  • Why do we need Python?
  •  Program structure
Execution Steps
  • Interactive Shell
  •  Executable or script files
  •  User Interface or IDE
Data types in Python
  • Memory management and Garbage collections
  • Object creation and deletion
  • Object properties
Data Types and Operations
  •  Numbers
  •  Strings
  •  List
  •  Tuple
  •  Dictionary
  •  Other Core Types
Loops and expression in Python
  • Assignments, Expressions and prints
  • Statements and Syntax
  •  If tests and Syntax Rules
  •  While and For Loops
  •  Iterations and Comprehensions
User defined function in Python
  • Functions
  • Function definition and call
  •  Function Scope
  •  Arguments
  •  Function Objects
  •  Anonymous Functions
Exception handling in Python
  • Exception Handling
  • Default Exception Handler
  •  Catching Exceptions
  •  Raise an exception
  •  User defined exception
Data Science Data Science and AI
  • All the topics in data science will covered with following concept:
  • All the topics in data science will covered with following concept:
  •  Mathematics beside of each model
  •  Which scenario we want to use a particular algorithm
  •  How to apply it in tool
  •  An inferential thing of each model
Difference between each model
  • Introduction to Machine Learning & Predictive Modelling
  •  Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Statistics
  • Standard Deviation
  •  Variance
  •  Concept of hypothesis testing
  •  T-test
  •  Chi-square
  •  Anova
  •  Correlation
  •  Probability
  •  Outliers
  •  Drop highly correlated features

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