IBM Data Science

Define data science and its importance in today’s data-driven world. Explore the different paths to a career in data science and gain insights from seasoned professionals. Understand why data science is one of the most in-demand jobs in the 21st century.

Discover the essential tools and languages used by data scientists, including Python, R, and SQL. Learn to use Jupyter notebooks and RStudio effectively and manage source code with Git repositories and GitHub.

Understand the importance of a data science methodology and how it guides the work of data scientists. Apply the CRISP-DM methodology to analyze case studies and evaluate appropriate data sources for analysis.

Gain proficiency in Python for data science, AI, and development. Learn about Python basics, programming logic, and how to work with libraries such as Pandas, Numpy, and Beautiful Soup.

Participate in a real-world project to demonstrate your Python skills for data science and analysis. Build a dashboard using Python and libraries like Pandas, Beautiful Soup, and Plotly, all within Jupyter notebook.

Learn about databases and SQL for data science with Python. Analyze data within a database, create relational databases, and work with multiple tables using DDL commands and basic to intermediate SQL queries using DML commands.

Develop Python code for cleaning and preparing data for analysis. Perform exploratory data analysis, manipulate data using dataframes, and build and evaluate regression models using machine learning scikit-learn library.

Implement data visualization techniques and plots using Python libraries such as Matplotlib, Seaborn, and Folium. Create advanced visualizations and generate interactive dashboards using the Dash framework and Plotly library.

Explore various types of machine learning algorithms, compare and contrast linear classification methods, and write Python code to implement classification techniques. Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics.

Demonstrate proficiency in data science and machine learning techniques using a real-world data set. Apply your skills in data collection, data wrangling, exploratory data analysis, data visualization, model development, and model evaluation.