The curriculum covers a wide range of data science topics including: open source tools and libraries (Pandas, NumPy, Matplotlib, Seaborn, Folium, Scikit-learn, SciPy, etc.), methodologies, Python, Jupyter notebooks, databases, SQL, data visualization, data analysis, predictive modeling, machine learning, and data mining.
- Introduction to Data Science
- Data Science Tools
- Data Science Methodology
- Python for Data Science, AI & Development
- Python Project for Data Science
- Databases and SQL for Data Science with Python
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone
This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization.
The final assignment involves assuming the role of a Data Scientist working for a startup intending to compete with SpaceX, and following the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting results to stakeholders.
The following tasks have been performed:
- Data Collection and Cleaning from SpaceX API
- Web Scraping Falcon 9 and Falcon Heavy Launch Records from Wikipedia
- Data Exploration, Wrangling, and Analysis
- Database Population and Exploration
- Data Visualization and Preparation for Feature Engineering
- Analysis of Launch Site Locations with Folium
- Creation of an Interactive Dashboard for Launch Record Analysis
- Training, Hyperparameter Tuning, Model Comparison, and Prediction of SpaceX Falcon 9 First Stage Landing Success
An overview of the methodologies used, the results obtained, and the conclusions drawn can be found in the report.
- Extracted and visualized financial data using Pandas.
- Utilized SQL for querying census, crime, and school demographic data.
- Performed data wrangling, EDA, and regression modeling for housing price prediction.
- Developed a dynamic Python dashboard for monitoring flight reliability.
- Employed various machine learning algorithms for loan repayment prediction.
- Trained models to predict reusable rocket stages in space launches.
- Applied various Data Science and Machine Learning skills, techniques, and tools to complete projects and publish reports.
- Practiced with various tools used by professional Data Scientists.
- Mastered the key steps involved in tackling a data science problem (Data Science methodology).
- Wrote SQL to query databases and explored relational database concepts.
- Utilized several data visualization tools, techniques, and libraries in Python to present data visually.
- Cleaned data sets, analyzed data, built and evaluated data models and pipelines using Python.
- Understood and applied various supervised and unsupervised Machine Learning models and algorithms to address real-world challenges using Python.
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