IBM Data Science Professional Certificate
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About this Professional Certificate
Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience.
The program consists of 9 online courses that will provide 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. You’ll 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:
Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
Projects: random album generator, predict housing prices, best classifier model, Predicting successful rocket landing, dashboa rd and interactive map
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
- Jupyter Notebook and Jupyter Lab
- Peer-graded Assignment
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
- Peer-graded Assignment: Analyzing Historical Stock/Revenue Data and Building a Dashboard
6a. Data Engineering Foundations Specialization - Introduction to Relational Databases (RDBMS)
- Course Introduction
- Relational Database Concepts
- Using Relational Databases
- MySQL and PostgreSQL
- Course Assignment
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
- Course Assignment
- Bonus Module: Advanced SQL for Data Engineer
7. Data Analysis with Python
- Importing Datasets
- Data Wrangling
- Exploratory Data Analysis
- Model Development
- Model Evaluation
- Final Assignment
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
- Final Project & Exam
9. Machine Learning with Python
- Introduction to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
- Final Project
10. Applied Data Science Capstone
- Introduction
- Exploratory Data Analysis (EDA)
- Interactive Visual Analytics and Dashboard
- Predictive Analysis (Classification)
- Present Your Data-Driven Insights
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