https://github.com/alpkanoz/ibm_data_science_professional_certificate

The repository contains projects and training materials carried out throughout the IBM data science professional course.
https://github.com/alpkanoz/ibm_data_science_professional_certificate

classification clustering data-analysis data-science data-visualization dataframe ibm ibm-watson machine-learning mathplotlib pandas predictive-modeling python scikit-learn

Last synced: 3 months ago
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The repository contains projects and training materials carried out throughout the IBM data science professional course.

• Host: GitHub
• URL: https://github.com/alpkanoz/ibm_data_science_professional_certificate
• Owner: alpkanoz
• License: mit
• Created: 2023-10-25T14:36:02.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-10T14:03:19.000Z (over 2 years ago)
• Last Synced: 2024-01-30T15:19:43.146Z (over 2 years ago)
• Topics: classification, clustering, data-analysis, data-science, data-visualization, dataframe, ibm, ibm-watson, machine-learning, mathplotlib, pandas, predictive-modeling, python, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://www.coursera.org/professional-certificates/ibm-data-science
• Size: 14.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# IBM Data Science Professional Certificate

![Professional_Certificate_-_Data_Science](https://github.com/alpkanoz/IBM_Data_Science_Professional_Certificate/assets/78761783/5133d9de-d0aa-49e2-87f7-eef4993670df)

## About Course and Certificate

Prepare for a career in the high-growth field of data science. In this program, I developed the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist. No prior knowledge of computer science or programming languages is required. 

Data science involves **gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes**. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater.

I learned in-demand skills used by professional data scientists, including **databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms,** and **data mining**. I also worked with the latest languages, tools, and libraries, including **Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib,** and more. Upon completing the full program, I had built a portfolio of data science projects. This Professional Certificate had a strong emphasis on applied learning and included a series of hands-on labs in the IBM Cloud that gave me practical skills.

**Course Link:** [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science)

### Tools

+ Jupyter

+ JupyterLab

+ GitHub

+ R Studio

+ Watson Studio

  

### Libraries

+ Pandas

+ NumPy

+ Matplotlib

+ Seaborn

+ Folium

+ ipython-sql

+ Scikit-learn

+ ScipPy, etc

  

### The Projects

+ **Extracting and graphing financial data** with the Pandas Python library.

+ Using **SQL to query** census, crime, and school demographic data sets.

+ **Wrangling data, graphing plots, and creating regression models** to predict housing prices with data science Python libraries.

+ 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 would be paid off or not.

+ **Training and comparing machine learning models to predict** if a space launch could reuse the first stage of a rocket.

## Instructors

+ Dr. Pooja

+ Romeo Kienzler

+ Joseph Santarcangelo

+ Polong Lin

+ Alex Aklson

+ Rav Ahuja

+ Saishruthi Swaminathon

+ Saeed Aghabozorgi

+ Hima Vasudevan

+ Azim Hirjani

+ Aije Egwaikhide

+ Yan Luo

+ Svetlana Levitan

## Course Overview

1. [What is Data Science?](https://www.coursera.org/learn/what-is-datascience?specialization=ibm-data-science)

     - Defining Data Science and What Data Scientists Do

     - Data Science Topics

     - Applications and Career in Data Science

     - Data Literacy for Data Science

1. [Tools for Data Science](https://www.coursera.org/learn/open-source-tools-for-data-science?specialization=ibm-data-science)

     - Overview of Data Science Tools

     - Languages of Data Science

     - Packages, APIs, Datasets and Models

     - Jupyter Notebooks and JupyterLab

1. [Data Science Methodology](https://www.coursera.org/learn/data-science-methodology?specialization=ibm-data-science)

     - From Problem to Approach and From Requirements to Collection

     - From Understanding to Preparation and From Modeling to Evaluation

     - From Deployment to Feedback and Final Evaluation

     - Final Project and Assessment

1. [Python for Data Science, AI & Development](https://www.coursera.org/learn/python-for-applied-data-science-ai?specialization=ibm-data-science)

     - Python Basics

     - Python Data Structers

     - Python Programming Fundamentals

     - Working with Data in Python

     - APIs, and Data Collection

1. [Python Project for Data Science](https://www.coursera.org/learn/python-project-for-data-science?specialization=ibm-data-science)

1. [Databases and SQL for Data Science with Python](https://www.coursera.org/learn/sql-data-science?specialization=ibm-data-science)

     - Getting Started with SQL

     - Introduction to Relational Databases and Tables

     - Intermediate SQL

     - Accessing DAtabases using Python

     - Course Assignment

     - Advanced SQL for Data Engineering

1. [Data Analysis with Python](https://www.coursera.org/learn/data-analysis-with-python?specialization=ibm-data-science)

     - Importing Data Sets

     - Data Wrangling

     - Exploratory Data Analysis

     - Model Development

     - Model Evaluation and Refinement

     - Final Assignment

1. [Data Visualization with Python](https://www.coursera.org/learn/python-for-data-visualization?specialization=ibm-data-science)

     - Introduction to Data Visualization Tools

     - Basic and Specialized Visualization Tools

     - Advanced Visualizations and Geospatial Data

     - Creating Dashboards with Plotly and Dash

     - Final Project and Exam

1. [Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python?specialization=ibm-data-science)

     - Introduction to Machine Learning

     - Regression

     - Classification

     - Linear Classification

     - Clustering

     - Final Exam and Project

1. [Applied Data Science Capstone](https://www.coursera.org/learn/applied-data-science-capstone?specialization=ibm-data-science)

     - Introduction

     - Exploratory Data Analysis (EDA)

     - Interactive Visual Analytics and Dashboard

     - Predictive Analysis (Classification)

     - Presentation