Ementorhub

What you'll learn

Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning

Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights

Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines

Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making

Skills you'll gain

Scikit Learn (Machine Learning Library)

Statistical Modeling

Feature Engineering

Pandas (Python Package)

Data Manipulation

Regression Analysis

Supervised Learning

Data Wrangling

Descriptive Statistics

NumPy

Data-Driven Decision-Making

Data Cleansing

Exploratory Data Analysis

Data Pipelines

Predictive Modeling

Matplotlib

Data Analysis

Data Import/Export

There are 6 modules in this course

This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.

Data Wrangling

Exploratory Data Analysis

Model Development

Model Evaluation and Refinement

Final Assignment