sdmuzel / data-science-ibm

Repositório com os arquivos da especialização em DS da IBM

IBM Data Science Professional Certificate

About this Professional Certificate

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:

• 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

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.

Courses:

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

IBM Digital Badge