Data Science with Python Course for Beginners | Online Course
What you'll learn
Proficient Data Handling.
Effective Data Visualization.
Learner's Gain a solid understanding of key statistical concepts and techniques.
End-to-End Project Execution.
Requirements
Good programming knowledge, introductory statistics, a computer, and Python with relevant libraries installed.

Description
The Data Science with Python course is an extensive program designed to provide a solid foundation in the essential concepts and techniques of data science. This course is perfect for beginners and those with some programming experience looking to deepen their understanding of data analysis and visualization.Throughout the course, you will engage with powerful Python libraries such as Pandas and NumPy, which are crucial for data manipulation and analysis. You will learn how to clean and preprocess datasets, conduct exploratory data analysis, and apply various statistical methods to derive meaningful insights.Additionally, you will gain hands-on experience in data visualization using Matplotlib and Seaborn, enabling you to create informative and visually engaging charts that effectively communicate your findings to various stakeholders.The curriculum includes practical projects that allow you to apply your skills to real-world datasets, enhancing your problem-solving abilities and preparing you for data-driven challenges across different sectors.By the end of the course, you will have a robust understanding of data science principles and techniques, equipping you with the tools necessary to succeed in this dynamic field. Join us to embark on your journey into the fascinating world of data science and unlock its potential!IIBM Institute of Business Management

Overview
Section 1: Data Science With Python
Lecture 1 Introduction
Lecture 2 Data Science Application Across Multiple Domain and Business Function
Lecture 3 Data Science Project LifeCycle
Lecture 4 Multiple Predictive Model Using Python
Lecture 5 Python - Simple and Multiple Predictive Model in Practical
Lecture 6 Assignment - Real Estate Predictive Analysis
Lecture 7 Python Correlation Analysis
Lecture 8 Data Science - Questionnaire Design Process
Lecture 9 Python Classification Model Building
Lecture 10 Data Science - Experimental Design Analysis
Lecture 11 Classification Technique - Discriminant Analysis
Lecture 12 Data Science - Association Rule - Apriori Algorithm
Lecture 0 Data Science - Building Recommendation System - Market Basket Analysis(MBA)
Lecture 13 Data Architecture Design, Data Warehousing and it's Schema Design
Aspiring data scientists, analysts, students, and business professionals interested in data.

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