IBM Data Science Professional Certificate

This repository contains materials and projects from the IBM Data Science Professional Certificate course series. This certificate aims to develop career-relevant skills for anyone interested in pursuing a career in data science or machine learning.

Learnings

Describe what is data science, the various activities of a data scientist’s job, and the methodology to think and work like a data scientist.
Develop hands-on skills using the tools, languages, and libraries used by professional data scientists.
Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python.
Apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholders.

Professional Certificate - 10 Course Series

This professional certificate will equip you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms.

Upon completion, you will have built a portfolio of data science projects, earned a professional certificate from Coursera, a digital badge from IBM, and up to 12 college credits recommended by ACE®.

Applied Learning Project

This professional certificate has a strong emphasis on applied learning. The courses include a series of hands-on labs in the IBM Cloud that provide practical skills applicable to real jobs. The tools used include Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio. The libraries include Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

Course Breakdown

The program consists of 9 online courses:

Tools for Data Science: Learn about the data scientist's toolkit, use languages like Python, R, and SQL, and work with tools such as Jupyter notebooks and RStudio.
Data Science Methodology: Understand the importance of methodology, apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology, and learn about various analytic models.
Python for Data Science, AI & Development: Master Python basics and work with Python libraries like Pandas, Numpy, and Beautiful Soup.
Python Project for Data Science: Apply your Python skills to a real project, demonstrating your proficiency in Python and data science.
Databases and SQL for Data Science with Python: Work with databases using SQL and Python, create relational databases on the cloud, and write complex SQL statements.
Data Analysis with Python: Develop Python code for cleaning and preparing data for analysis, perform exploratory data analysis, and build and evaluate regression models.
Data Visualization with Python: Implement data visualization techniques using Python libraries and create interactive dashboards.
Machine Learning with Python: Understand various types of machine learning algorithms and implement various classification techniques in Python.
Applied Data Science Capstone: This capstone project gives you the chance to practice the work of data scientists in real life, working with datasets, and following the data science methodology.

Projects

The course series includes several hands-on projects, such as:

Extracting and graphing financial data with the Pandas Python library.
Using SQL to query census, crime, and school demographic data sets.
Creating a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability.
Applying and comparing machine learning classification algorithms to predict whether a loan case will be paid off or not.
Training and comparing machine learning models to predict if a space launch can reuse the first stage of a rocket.

Completion