Course Summary
Learn how to perform data analysis using Python with this comprehensive course. Gain hands-on experience through real-world projects and assignments.Key Learning Points
- Learn how to use Python for data analysis
- Get hands-on experience with real-world projects
- Learn from industry experts with extensive experience in data analysis
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹4,56,434
- Spain: €29,229
- USA: $62,453
- India: ₹4,56,434
- Spain: €29,229
- USA: $73,296
- India: ₹5,38,525
- Spain: €34,523
- USA: $62,453
- India: ₹4,56,434
- Spain: €29,229
- USA: $73,296
- India: ₹5,38,525
- Spain: €34,523
- USA: $117,345
- India: ₹8,61,544
- Spain: €55,274
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of Python programming
- Learn how to analyze and manipulate data using Python
- Gain hands-on experience with real-world projects
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Access to a computer with internet connection
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Project-based
Similar Courses
- Data Science Essentials
- Applied Data Science with Python
Related Education Paths
- IBM Data Science Professional Certificate
- Data Analysis and Visualization with Excel
- Applied Data Science with Python
Related Books
Description
Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
Outline
- Importing Datasets
- The Problem
- Understanding the Data
- Python Packages for Data Science
- Importing and Exporting Data in Python
- Getting Started Analyzing Data in Python
- Accessing Databases with Python
- Lesson Summary
- Practice Quiz: Understanding the Data
- Practice Quiz: Python Packages for Data Science
- Practice Quiz: Importing and Exporting Data in Python
- Practice Quiz: Getting Started Analyzing Data in Python
- Graded Quiz: Importing Datasets
- Data Wrangling
- Pre-processing Data in Python
- Dealing with Missing Values in Python
- Data Formatting in Python
- Data Normalization in Python
- Binning in Python
- Turning categorical variables into quantitative variables in Python
- Lesson Summary
- Practice Quiz: Dealing with Missing Values in Python
- Practice Quiz: Data Formatting in Python
- Practice Quiz: Data Normalization in Python
- Practice Quiz: Turning categorical variables into quantitative variables in Python
- Graded Quiz: Data Wrangling
- Exploratory Data Analysis
- Exploratory Data Analysis
- Descriptive Statistics
- GroupBy in Python
- Correlation
- Correlation - Statistics
- Association between two categorical variables: Chi-Square
- Lesson Summary
- Practice Quiz: Descriptive Statistics
- Practice Quiz: GroupBy in Python
- Correlation
- Practice Quiz: Correlation - Statistics
- Graded Quiz: Exploratory Data Analysis
- Model Development
- Model Development
- Linear Regression and Multiple Linear Regression
- Model Evaluation using Visualization
- Polynomial Regression and Pipelines
- Measures for In-Sample Evaluation
- Prediction and Decision Making
- Lesson Summary
- Practice Quiz: Linear Regression and Multiple Linear Regression
- Practice Quiz: Model Evaluation using Visualization
- Practice Quiz: Polynomial Regression and Pipelines
- Practice Quiz: Measures for In-Sample Evaluation
- Graded Quiz: Model Development
- Model Evaluation
- Model Evaluation and Refinement
- Overfitting, Underfitting and Model Selection
- Ridge Regression
- Grid Search
- Ridge Regression Introduction
- Lesson Summary
- Practice Quiz: Model Evaluation
- Practice Quiz: Overfitting, Underfitting and Model Selection
- Practice Quiz: Ridge Regression
- Graded Quiz: Model Refinement
- Final Assignment
- Project Case Scenario
- IBM Digital Badge
- Course Creators
- Final Exam
Summary of User Reviews
Discover the fundamentals of data analysis with Python through this comprehensive Coursera course. Learn how to use Python libraries like Pandas, NumPy, and Matplotlib to process and visualize data. The course has received positive reviews from many users, who found it to be an informative and engaging learning experience.Pros from User Reviews
- Course is well-structured and easy to follow
- Instructors are knowledgeable and engaging
- Hands-on exercises and quizzes help reinforce learning
- Excellent resource for beginners looking to learn data analysis with Python
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Not enough emphasis on practical applications of data analysis
- Some users felt that the explanations were too simplistic and lacked depth
- Course content may not be challenging enough for experienced programmers
Joseph Santarcangelo
4.7 Raiting
Metabolomics in Life Sciences
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Learn about metabolomics principles and their applications in life sciences. This course is an introduction to metabolomics principles and their applications in various fields of life sciences....
Perform better geotechnical numerical analysis
- 4.7
-
![]()
![]()
![]()
![]()
![]()
The first course in the COGAN (Competency in Geotechnical Analysis) e-learning series This is followed by a self-test quiz with feedback provided on your answers....
Bayesian Statistics: Techniques and Models
- 4.8
-
![]()
![]()
![]()
![]()
![]()
This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. Real-world data often require more sophisticated models to reach realistic conclusions....
[NEW] PL-900 Microsoft Power Platforms- 5 practice tests
- 4.3
-
![]()
![]()
![]()
![]()
![]()
AUG Updated | 195+ques to build confidence | Ace PL 900 in first attempt | Detailed explanations |Read reviews b4 buying...
