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Introduction to Data Analysis With Python

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Study Mode: Online / Offline
Enrolled: 897 students
Course view: 3055
Duration: Self-Paced Learning
Lectures: 9
Course Materials: Downloadable Videos
Video: 1 Hour 21 Minutes
Course type: All Levels
Certificate of Completion: FREE
Introduction to Data Analysis With Python

Course Overview:

This course provides an extensive introduction to the fundamental skills and techniques necessary for data analysis using Python, one of the most popular languages for data science. The course modules are designed to walk students through the entire process of data analysis from data extraction to visualization. By using a practical, hands-on approach with the use of Kaggle datasets, students can expect to develop a solid foundation in Python for data analysis.


Module Breakdown:


  1. Kaggle Datasets: In this module, we will introduce Kaggle datasets, a rich resource for practising data analysis and machine learning. We’ll show you how to access and download these datasets for use in your own projects.
  2. Tabular Data: Here we delve into the world of tabular data. We will explore how Python and its libraries can help manage and manipulate these types of data structures, making your data analysis more efficient and insightful.
  3. Exploring Pandas DataFrame:This module covers the basics of using pandas DataFrame, one of the most frequently used tools in data analysis. You’ll learn how to create, navigate, and extract information from your DataFrame.
  4. Analysing and Manipulating Pandas DataFrame:  Expanding on the previous module, this section will guide you through the methods of analyzing and manipulating data within pandas DataFrame. These techniques will be critical for data wrangling and generating insights from your data.
  5. What Is Data Cleaning: Here we’ll discuss the concept of data cleaning and its importance in the data analysis pipeline. You’ll learn why properly cleaning your data can significantly improve the quality of your analysis.
  6. Basic Data Cleaning: In this extensive module, we’ll go hands-on with basic data cleaning techniques. From handling missing data to removing duplicates, this section will empower you with the essential skills to prepare your data for analysis.
  7. Data Visualization: An introductory overview of data visualization using Python. Learn why visualizing your data is important and what types of visualizations are commonly used in data analysis.
  8. Visualizing Qualitative Data: This section dives into the specific techniques for visualizing qualitative or categorical data. By creating impactful visualizations, you’ll be able to tell compelling stories with your data.
  9. Visualizing Quantitative Data: In the final module, we’ll focus on quantitative data visualization. We’ll explore various chart types and visualization techniques that can help illustrate the patterns and relationships in numerical data.

Upon completing this course, students should have a fundamental understanding of how to use Python for data analysis. They should be able to manage and manipulate data, perform basic data cleaning, and create informative visualizations. This course will serve as a stepping stone for more advanced data science courses and projects.

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Introduction to Data Analysis With Python
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