Overview

The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022. Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations. This specialization will teach learners how to analyze, understand, manipulate, and present data within an effective and repeatable process framework and will enable you to bring value to the business by putting data science concepts into practice. This course is designed for business professionals that want to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. Certified Data Science Practitioner (CDSP) will prepare learners for the CertNexus CDSP certification exam. To complete your journey to the CDSP Certification Complete the Coursera Certified Data Science Practitioner Professional Certificate. Review the current version of the CDSP Exam Blueprint, available at CertNexus Purchase your CDSP Exam Voucher at the CertNexus store Register for your CDSP Exam.

Syllabus

Course 1: Address Business Issues with Data Science
Course 2: Extract, Transform, and Load Data
Course 3: Analyze Data
Course 4: Train Machine Learning Models
Course 5: Finalize a Data Science Project

Courses

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6 hours 37 minutes
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This course is designed for business professionals that want to learn how to determine if a business issue is appropriate for a data science project and apply the data science process. The typical student in this course will have experience in a business setting and a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
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20 hours 54 minutes
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This course is designed for business professionals that want to learn how to analyze data to gain insight, use statistical analysis methods to explore the underlying distribution of data, use visualizations such as histograms, scatter plots, and maps to analyze data and preprocess data to produce a dataset ready for training. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.
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15 hours 16 minutes
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This course is designed for business and data professional seeking to learn the first technical phase of the data science process known as Extract, Transform and Load or ETL. Learners will be taught how to collect data from multiple sources so it is available to be transformed and cleaned and then will dive into collected data sets to prepare and clean data so that it can later be loaded into its ultimate destination. In the conclusion of the course learners will load data into its ultimate destination so that it can be analyzed and modeled. The typical student in this course will have experience working with data and aptitude with computer programming.
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12 hours 22 minutes
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This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes. The typical student in this course will have completed previous courses in the CDSP professional certificate program, and have several years of experience with computing technology, including some aptitude in computer programming.
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1 day 4 hours 39 minutes
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This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.