Maven Analytics – Data Science in Python: Data Prep & EDA 2025-4
Maven Analytics – Data Science in Python: Data Prep & EDA 2025-4 Downloadly IRSpace
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Python Data Science: Data Prep & EDA with Python is designed as a practical, project-based guide to help you master the fundamentals of data science using the Python programming language. In this educational journey, we begin by establishing a strong understanding of the fundamental concepts of data science and machine learning. To that end, we will explain the key distinctions between supervised and unsupervised learning approaches, and we will also carefully examine the data science workflow framework that will serve as our roadmap throughout this course.
After this introductory phase, our main focus will be on the heart of the data science process, namely the stages of Data Preparation and Exploratory Data Analysis (EDA). In these crucial sections, you will get to know the perspective of a data scientist and learn how to envision and plan a data science project from scratch to its final stages. Proficiency in working with the powerful Pandas library in Python will allow you to collect data from various sources and effectively deal with common data cleansing challenges. You will also learn advanced EDA techniques, including intelligent data filtering, meaningful grouping, and data visualization using powerful visualization tools to uncover patterns and insights hidden in the data.
What will you learn in this course?
- Mastering the core building blocks of Python, which are a prerequisite for entering the world of data science and using machine learning algorithms.
- Ability to define and constrain data science projects by setting clear objectives, selecting appropriate techniques, and identifying the data sources needed to perform accurate analyses.
- Proficiency working with the Pandas library to import and export data from a variety of sources including plain text files, Excel spreadsheets, and SQL database tables.
- Ability to cleanse and prepare data by converting data types, managing common data quality issues, and creating new columns to enrich the analysis process.
- Mastery of exploratory data analysis (EDA) techniques including sorting, filtering, grouping, and visualizing data to uncover patterns, trends, and valuable insights.
- Skill in preparing data for use in machine learning models by merging different tables, adjusting the level of detail of the data, and applying feature engineering techniques to extract useful information.
Who is this course for?
- Data scientists looking to learn fundamental techniques and best practices in data preparation and exploratory data analysis.
- Users of the Python programming language who want to acquire the basic skills needed to enter the field of artificial intelligence and machine learning models.
- Data analysts or business intelligence (BI) professionals looking to change careers and enter the exciting field of data science.
- Any interested person who wants to learn one of the most popular open source programming languages in the world, Python, in the field of data science.
Data Science in Python: Data Prep & EDA Course Details
- Publisher: Udemy
- Instructor: Maven Analytics , Alice Zhao
- Training level: Beginner to advanced
- Training duration: 8 hours and 38 minutes
- Number of courses: 192
Course topics
Prerequisites for the Data Science in Python: Data Prep & EDA course
- Jupyter Notebooks (free download, we’ll walk through the installation)
- Familiarity with basic Python and Pandas is recommended, but not required
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: English
Quality: 1080p
Changes:
The 2024/11 version has no changes in the number of lessons and duration of the course compared to 2023/7, but the size of some of the videos has changed. English subtitles have also been added to the course.
Download link
Maven Analytics
Udemy (2024/11) – 720p
File(s) password: www.downloadly.ir
File size
1.3 / 2.09 GB
