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Applied Python for Data Science

Applied Python for Data Science

Course Starts: Monday, July 29th |     Duration: 3 Weeks 


This three-week Applied Python for Data Science course offered by Jobsbridge, introduces both fundamental and advanced concepts in data science through the Python programming language. This is an applied program based on real-world projects and is a skills-based specialization intended for learners who have a sound programming background in Python and want to apply statistical, machine learning, information visualisation, text analysis, and social network analysis techniques through popular Python toolkits.

What you'll learn:

  • The course provides a path to becoming a data scientist

  • Problem Solving Approach

  • Impress interviewers by showing an understanding of the data science concept

  • Make a powerful analysis

  • Python Basic to Advance Concept

  • Python Libraries for Data Analysis such as Numpy, Scipy, Pandas

  • Python Libraries for Data Visualization such as Matplotlib, Seaborn, Plotlypy

  • Case Studies of Data Science with Coding


Course Prerequisites: 

  • Basic programming understanding in any language.

  • Familiarity with elementary mathematics concepts.

  • Understanding of fundamental statistics.

  • Proficiency in basic computer skills and file management.

  • Access to a computer with internet and commitment to self-guided learning.


Returns and Refund Policy: The course fee is 100%  refundable if students cannot join the program and want to cancel their admission. Once students join the program, payment cannot be refunded. 

  • Day 1: Introduction to Python Basics (4 hours)

    • Overview of Python and its importance in data science

    • Installing Python and Jupyter Notebook

    • Python syntax basics: variables, data types, basic operations


    Day 2: Control Flow and Functions (4 hours)

    • Conditional statements (if, elif, else)

    • Loops (for, while)

    • Introduction to functions and their significance in data science


    Day 3: Data Structures in Python (4 hours)

    • Lists, tuples, and dictionaries

    • List comprehensions

    • Understanding data structures and their application in data manipulation


    Day 4: NumPy Basics (4 hours)

    • Introduction to NumPy arrays

    • Array manipulation and operations

    • NumPy functions for numerical computing

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