What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Introduces foundational concepts of data analysis and its practical applications.
- Covers techniques for importing libraries and setting up the analysis environment.
- Explains the complete data analysis life cycle, from data acquisition to model deployment.
- Discusses various algorithms used in data analysis, including decision tree classifiers and logistic regression.
- Teaches Exploratory Data Analysis (EDA) methods to extract insights from datasets.
- Demonstrates effective use of libraries and visualization techniques, such as bar plots, for data understanding.
- Guides handling specific dataset columns, like the name column.
- Covers modeling techniques, including creating training sets and implementing cross-validation.
- Offers access to reference files for further practice and exploration in data analysis.
- Provides a detailed understanding of regression analysis and logistic regression modeling using Python.
- Guides learners on interpreting modeling results and presenting findings effectively.
- Explains methods for calculating probabilities and understanding model outputs.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
Description
This course teaches participants essential data analysis concepts and skills to work with real-world datasets and extract valuable insights.
Participants will gain hands-on experience with:
- Data Analysis Lifecycle: Learn the complete process from data acquisition to model deployment, including exploratory data analysis (EDA) for understanding datasets.
- Algorithms & Modeling: Work with key algorithms such as decision tree classifiers and logistic regression, and practice building models with training sets and cross-validation.
- Libraries & Visualization: Understand the role of essential libraries, learn how to load them effectively, and explore visualization techniques, including bar plots and column-specific handling.
- Reference Materials: Access additional datasets and resources to reinforce learning and practice independently.
By the end of the course, participants will have developed practical skills to analyze datasets efficiently, apply modeling techniques, and interpret results, preparing them to tackle real-world data challenges with confidence.
Requirements
- Basic knowledge of Python programming
- Basic understanding of statistics
Target Audience
- Researchers or analysts interested in data modeling
- Professionals looking to estimate probabilities and predict outcomes
