Foundations of Data Analysis: From Introduction to Modeling
Explore the fundamentals of data analysis in our comprehensive course. Learn essential techniques from importing libraries to modeling. Dive into exploratory data analysis and algorithm implementation. Access reference files for further practice and exploration.
Offer ends in:
What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- The foundational concepts of data analysis and its applications.
- Techniques for importing libraries and setting up your analysis environment.
- Understanding the life cycle of data analysis from data acquisition to model deployment.
- Different algorithms used in data analysis, including decision tree classifiers and logistic regression.
- Exploratory Data Analysis (EDA) methods to gain insights into your datasets.
- How to load libraries effectively and utilize them in your analysis.
- Visualization techniques such as bar plots to better understand your data.
- Handling specific columns in your datasets, such as the name column.
- Modeling techniques including creating training sets and implementing cross-validation.
- Accessing reference files for further exploration and practice in data analysis.
- Know in detail about regression analysis
- Develop a logistic regression model using python
- Learn how to interpret the modeling results and present it to others
- Know about the different methods of finding the probabilities
Content
-
MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔
Description
Introduction: Get introduced to the fundamentals of data analysis in this course. Understand the course objectives and what you'll be learning throughout the modules.
Getting Started: Learn about the life cycle of data analysis, starting from data acquisition to model deployment. Import essential libraries and delve into various algorithms used in data analysis, including decision tree classifiers and logistic regression. Explore exploratory data analysis (EDA) techniques to gain insights into your datasets.
Load Libraries: Understand the importance of libraries in data analysis and how to load them effectively. Dive deeper into bar plot visualization techniques and learn how to handle specific columns, such as the name column, in your datasets. Get hands-on experience with modeling techniques, including training sets and cross-validation.
Reference Files: Access reference files that complement the course materials, providing additional resources and datasets for further exploration and practice.
Throughout the course, you'll gain practical skills and knowledge essential for data analysis, preparing you to tackle real-world datasets and derive meaningful insights.
Requirements
- python basics
- Statistics basics
Target Audience
- Anyone who is interested in modeling data and estimate the probabilities of given outcomes.
Offer ends in:
Training 5 or more people?
Get your team access to 5,000+ top courses, learning paths, mock tests anytime, anywhere.
Drop an email at: [email protected]
