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    Data Science Professional

    Categories: data science
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    About Course

    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 will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills.

    It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist.

    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.

    Upon 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, you’ll also receive a digital badge from IBM.

    This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

    Applied Learning Project

    This Professional Certificate has a strong emphasis on applied learning. The courses include a series of hands-on labs in the IBM Cloud that 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

    • Extract and graph financial data with the Pandas Python library.
    • Use SQL to query census, crime, and school demographic data sets.
    • Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries.
    • Create a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability.
    • Apply and compare machine learning classification algorithms to predict whether a loan case will be paid off or not.
    • Train and compare machine learning models to predict if a space launch can reuse the first stage of a rocket.
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    Course Content

    What is Data Science?
    What you'll learn Define data science and its importance in today’s data-driven world. Describe the various paths that can lead to a career in data science. Summarize advice given by seasoned data science professionals to data scientists who are just starting out. Explain why data science is considered the most in-demand job in the 21st century.

    Tools for Data Science
    What you'll learn Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools Utilize languages commonly used by data scientists like Python, R, and SQL Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features Create and manage source code for data science using Git repositories and GitHub.

    Data Science Methodology
    What you'll learn Describe what a methodology is and why data scientists need a methodology. Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study. Determine an appropriate analytic model including predictive, descriptive, and classification models to analyze a case study. Decide on appropriate sources of data for your data science project.

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