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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. \n\nData 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.\n\nI 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.\n\n**Course Link:** [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science)\n\n### Tools\n+ Jupyter\n+ JupyterLab\n+ GitHub\n+ R Studio\n+ Watson Studio\n  \n### Libraries\n+ Pandas\n+ NumPy\n+ Matplotlib\n+ Seaborn\n+ Folium\n+ ipython-sql\n+ Scikit-learn\n+ ScipPy, etc\n  \n### The Projects\n+ **Extracting and graphing financial data** with the Pandas Python library.\n+ Using **SQL to query** census, crime, and school demographic data sets.\n+ **Wrangling data, graphing plots, and creating regression models** to predict housing prices with data science Python libraries.\n+ Creating a dynamic **Python dashboard** to monitor, report, and improve US domestic flight reliability.\n+ Applying and comparing **machine learning classification algorithms** to predict whether a loan case would be paid off or not.\n+ **Training and comparing machine learning models to predict** if a space launch could reuse the first stage of a rocket.\n\n## Instructors\n\n+ Dr. Pooja\n+ Romeo Kienzler\n+ Joseph Santarcangelo\n+ Polong Lin\n+ Alex Aklson\n+ Rav Ahuja\n+ Saishruthi Swaminathon\n+ Saeed Aghabozorgi\n+ Hima Vasudevan\n+ Azim Hirjani\n+ Aije Egwaikhide\n+ Yan Luo\n+ Svetlana Levitan\n\n## Course Overview\n1. [What is Data Science?](https://www.coursera.org/learn/what-is-datascience?specialization=ibm-data-science)\n     - Defining Data Science and What Data Scientists Do\n     - Data Science Topics\n     - Applications and Career in Data Science\n     - Data Literacy for Data Science\n1. [Tools for Data Science](https://www.coursera.org/learn/open-source-tools-for-data-science?specialization=ibm-data-science)\n     - Overview of Data Science Tools\n     - Languages of Data Science\n     - Packages, APIs, Datasets and Models\n     - Jupyter Notebooks and JupyterLab\n1. [Data Science Methodology](https://www.coursera.org/learn/data-science-methodology?specialization=ibm-data-science)\n     - From Problem to Approach and From Requirements to Collection\n     - From Understanding to Preparation and From Modeling to Evaluation\n     - From Deployment to Feedback and Final Evaluation\n     - Final Project and Assessment\n1. [Python for Data Science, AI \u0026 Development](https://www.coursera.org/learn/python-for-applied-data-science-ai?specialization=ibm-data-science)\n     - Python Basics\n     - Python Data Structers\n     - Python Programming Fundamentals\n     - Working with Data in Python\n     - APIs, and Data Collection\n1. [Python Project for Data Science](https://www.coursera.org/learn/python-project-for-data-science?specialization=ibm-data-science)\n1. [Databases and SQL for Data Science with Python](https://www.coursera.org/learn/sql-data-science?specialization=ibm-data-science)\n     - Getting Started with SQL\n     - Introduction to Relational Databases and Tables\n     - Intermediate SQL\n     - Accessing DAtabases using Python\n     - Course Assignment\n     - Advanced SQL for Data Engineering\n1. [Data Analysis with Python](https://www.coursera.org/learn/data-analysis-with-python?specialization=ibm-data-science)\n     - Importing Data Sets\n     - Data Wrangling\n     - Exploratory Data Analysis\n     - Model Development\n     - Model Evaluation and Refinement\n     - Final Assignment\n1. [Data Visualization with Python](https://www.coursera.org/learn/python-for-data-visualization?specialization=ibm-data-science)\n     - Introduction to Data Visualization Tools\n     - Basic and Specialized Visualization Tools\n     - Advanced Visualizations and Geospatial Data\n     - Creating Dashboards with Plotly and Dash\n     - Final Project and Exam\n1. [Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python?specialization=ibm-data-science)\n     - Introduction to Machine Learning\n     - Regression\n     - Classification\n     - Linear Classification\n     - Clustering\n     - Final Exam and Project\n1. [Applied Data Science Capstone](https://www.coursera.org/learn/applied-data-science-capstone?specialization=ibm-data-science)\n     - Introduction\n     - Exploratory Data Analysis (EDA)\n     - Interactive Visual Analytics and Dashboard\n     - Predictive Analysis (Classification)\n     - Presentation\n      \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falpkanoz%2Fibm_data_science_professional_certificate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falpkanoz%2Fibm_data_science_professional_certificate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falpkanoz%2Fibm_data_science_professional_certificate/lists"}