- Statistical Analysis
- Data Pipelines
- Data-Driven Decision-Making
- Regression Analysis
- Data Wrangling
- Data Import/Export
- NumPy
- Python Programming
- Data Visualization
- Predictive Modeling
- Exploratory Data Analysis
- Data Cleansing
Data Analysis with Python
Completed by OLUMIDE EMMANUEL AWOMEWE
December 26, 2022
16 hours (approximately)
OLUMIDE EMMANUEL AWOMEWE's account is verified. Coursera certifies their successful completion of Data Analysis with Python
What you will 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 will gain
