Data Science Specialization
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COURSE DATES AND LOCATIONS
DATE
Duration
LOCATION
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INTRODUCTION
The science concerned with the discovery of information from large volumes of unstructured data is called data science. This field has high practical relevance, as the generation and application of information is an important economic activity in today’s world. For example, data science techniques can be used in information systems for maintaining an information model of the dynamic environment, based on things like real-time sensor data. These information models, in turn, can be used to offer tailored services to the users in the environment. This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data
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COURSE OBJECTIVES
By the end of the course, you‘ll be able to:
- Describe what is data science, the various activities of a data scientist’s job, and methodology to think and work like a data scientist
- Develop hands-on skills using the tools, languages, and libraries used by professional data scientists
- Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python
- Apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholder
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Describe the various paths that can lead to a career in data science.
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COURSE AUDIENCE
This course is made for :
- Data science specialist
- IT developers
- Sales and marketing specialist
- Brand creators
- Software designers
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COURSE OUTLINE
Day One
Define data science
- Fundamentals of data science
- Paths to data science
- Data science solution
- Cloud of data science
Day Two
Data science topics
- Data science skills
- Application of machine learning
- Foundation of big data
Day Three
Data science in business
- Data science applications
- How companies get started of data science
- Careers in data science
Day Four
Data science project
- Data collection
- Data cleaning
- Explore data analysis
- Modelling
- Deployment
Day Five
Machine learning
- Logistic regression
- Building leaner regression
- Clustering