Data science and machine learning skills continue to be in highest demand across industries, and the need for data practitioners is booming. Upon completing this Professional Certificate program, you will be armed with the skills and experience you need to start your career in data science and machine learning. Through hands-on assignments and high-quality instruction, you will build a portfolio using real data science tools and real-world problems and data sets. The curriculum will cover a wide range of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. There is no requirement for prior computer science or programming knowledge in order to take this program.

Anyone with some computer skills and a passion for self-learning can succeed as we begin small and build up to more complex problems and topics.

With the tremendous need for data science and data analyst professionals in the market today, this program will jumpstart your path in data science and prepare you with a portfolio of data science deliverables to give you the confidence to take the plunge and start your data science career.

The Program 

1. Introduction to Data Science.
2. Data Science Tools.
3. The Data Science Method.
4. SQL for Data Science.
5. Python Basics for Data Science.
6. Analyzing Data with Python.
7. Visualizing Data with Python.
8. Machine Learning with Python: A Practical Introduction.
9. Data Science and Machine Learning Capstone Project.

What will you learn 

• Apply various Data Science and Machine Learning skills, techniques, and tools to complete a project and publish a report.
• Practice with various tools used by Data Scientists and become experienced in using some of them like Jupyter notebooks.
• Master the key steps involved in tackling a data science problem and learn to follow a methodology to think and work like a Data Scientist.
• Write SQL to query databases and explore relational database concepts.
• Understand Python and practice Python programming using Jupyter.
• Import and clean data sets, analyze data, build and evaluate data models and pipelines using Python.
• Utilize several data visualization tools, techniques and libraries in Python to present data visually.
• Understand and apply various supervised and unsupervised Machine Learning models and algorithms to address real world challenges using Python.