Title: Journey into Data Science with Python

Location: US

Company: CodeSignal

About this course:
This intermediate path covers the core Python tools and workflows used in applied data science. You will work with NumPy and Pandas for numerical computing and data manipulation, then use datasets such as Titanic, housing, Iris, and airline data to practice analysis in realistic contexts. The path introduces essential steps in the data science process, including cleaning messy data, engineering features, creating visualizations, and preparing datasets for modeling. You will also explore supervised learning with regression models and unsupervised learning with clustering and dimensionality reduction. By the end, you will have practiced moving from raw data to insight using Python. This path is suited for learners who have some programming background and want to strengthen their ability to analyze data and apply foundational machine learning techniques.

Manipulate and analyze structured data with NumPy and Pandas

Clean datasets by handling missing values, transforming features, and preparing inputs for modeling

Create and interpret visualizations with Matplotlib and Seaborn

Build supervised learning models using linear and logistic regression

Apply clustering and dimensionality reduction techniques to uncover patterns

Analyze time series data to identify trends, seasonality, and growth patterns

Build practical data science skills in Python through hands-on work with NumPy, Pandas, visualization, preprocessing, and machine learning. Analyze real datasets while learning how to prepare data, model outcomes, and interpret patterns.

Duration: 8 weeks

Skills:
- Data Engineering
- Data Manipulation
- Data Modeling
- Data Science
- Dimensionality Reduction
- Machine Learning
- NumPy (Python Package)
- Numerical Computing
- Pandas (Python Package)
- Python (Programming Language)
- Python Tools For Visual Studio (Python Package)
- Supervised Learning
- Unsupervised Learning
- Workflow Management

Curriculum:
- Manipulate and analyze structured data with NumPy and Pandas
- Clean datasets by handling missing values, transforming features, and preparing inputs for modeling
- Create and interpret visualizations with Matplotlib and Seaborn
- Build supervised learning models using linear and logistic regression
- Apply clustering and dimensionality reduction techniques to uncover patterns

- Analyze time series data to identify trends, seasonality, and growth patterns