Exploratory Data Analysis
Learn to uncover insights and patterns in data using Exploratory Data Analysis (EDA) techniques with tools like Python, and its libraries.
Satyajit Pattnaik
Instructor
What you will learn?
- Effectively clean and preprocess datasets by handling missing values, applying feature scaling, and utilizing feature encoding techniques.
- Create informative visualizations using Python libraries such as Matplotlib and Seaborn to uncover patterns and trends in data.
- Analyze individual variables and their relationships with others to derive meaningful insights and support data-driven decision-making.
- Apply EDA techniques in a real-world project, demonstrating the ability to extract insights from raw data and present findings effectively.
- Utilize the skills acquired in the course to make confident, data-driven decisions based on thorough analysis and interpretation of data.
This Course Includes
- Recorded Lessons: 33
- Recorded Hours: 5
- Duration: 5 days (Avg)
- TD Assessment Available
- Access on Mobile
Course Description
Course Overview:
The Exploratory Data Analysis (EDA) course is designed to equip learners with essential techniques and tools for uncovering insights from raw data. EDA is a critical step in the data analysis process, focusing on:
Key Highlights:
· Summarizing and Visualizing Data: Learners will explore methods to summarize and visualize data, identifying patterns, detecting anomalies, and validating assumptions.
· Data Cleaning and Preprocessing: The course covers techniques for handling missing values, feature scaling, and feature encoding to prepare the data for meaningful analysis.
· Bivariate and Univariate Analysis: Learners will learn how to conduct bivariate and univariate analysis to understand the relationships between variables and identify key features.
· Portfolio Project: Learners will have the opportunity to apply their Python knowledge and EDA skills to work on a real-world portfolio project, showcasing their ability to uncover insights from raw data.
Course Objectives:
· Understand the importance of EDA in the data analysis process and its role in making data-driven decisions.
· Develop proficiency in using Python and its libraries (e.g., NumPy, Pandas, Matplotlib, Seaborn) for data exploration and visualization.
· Learn techniques for data cleaning, handling missing values, feature scaling, and feature encoding.
· Gain experience in conducting bivariate and univariate analysis to identify patterns, relationships, and anomalies in the data.
· Demonstrate the ability to apply EDA skills to a portfolio project, showcasing the learner's data analysis capabilities.
By the end of this course, learners will have the essential skills to bridge the gap between data collection and meaningful analysis, empowering them to make informed, data-driven decisions.
Course Content
33 Lessons | 4hr 44min
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