Applied Python for Data Science
Course Starts: Monday, April 8th  Duration: 3 Weeks
This threeweek 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 realworld projects and is a skillsbased 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 selfguided 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
