The program consists of 9 online courses that will provide 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. Learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including:
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Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
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Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
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Projects: random album generator, predict housing prices, best classifier model, Predicting successful rocket landing, dashboa rd and interactive map
Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science. In addition to earning a Professional Certificate from Coursera, the person also receive a digital badge from IBM recognizing your proficiency in data science.
1. What is Data Science?
- Defining Data Science
- What Do Data Scientists Do?
- Big Data and Data Mining
- Deep Learning and Machine Learning
- Data Science in Business
- Careers and Recruiting in Data Science
- The Report Structure
2. Tools for Data Science
- Data Science Tools - Open Source Tools
- Data Science Tools - Commercial Tools
- Data Science Tools - Cloud Based Tools
- Packages, APIs, Data Sets and Models - Libraries for Data Science
- Packages, APIs, Data Sets and Models - Application Programming Interfaces (API)
- Packages, APIs, Data Sets and Models - Data Sets & Sharing Enterprise Data
- Packages, APIs, Data Sets and Models - Machine Learning Models
- Packages, APIs, Data Sets and Models - The Model Asset Exchange
3. Data Science Methodology
- From Problem to Approach and From Requirements to Collection
- From Understanding to Preparation
- From Deployment to Feedback
4. Python for Data Science, AI & Development
- Python Basics
- Python Data Structures
- Python Programming Fundamentals
- Working with Data in Python
- APIs, and Data Collection
5. Python Project for Data Science
- Crowdsourcing Short squeeze Dashboard
6. Databases and SQL for Data Science with Python
- Getting Started with SQL
- Introduction to Relational Databases and Tables
- Intermediate SQL
- Accessing Databases using Python
- Bonus Module: Advanced SQL for Data Engineer
7. Data Analysis with Python
- Importing Datasets
- Data Wrangling
- Exploratory Data Analysis
- Model Development
- Model Evaluation
8. Data Visualization with Python
- Introduction to Data Visualization Tools
- Basic and Specialized Visualization Tools
- Advanced Visualizations and Geospatial Data
- Creating Dashboards with Plotly and Dash
9. Machine Learning with Python
- Introduction to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
10. Applied Data Science Capstone
- Introduction
- Exploratory Data Analysis (EDA)
- Interactive Visual Analytics and Dashboard
- Predictive Analysis (Classification)
- Present Your Data-Driven Insights