Description: The final step in the data science pipeline is to communicate the results or findings. In this course, you'll explore communication and visualization concepts needed by data scientists.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 01:20
Description: Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. In this course, you'll explore the conceptual elements of various machine learning techniques.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 01:19
Description: There are many software and programming tools available to data scientists. Before applying those tools effectively, you must understand the underlying concepts. In this course, you'll explore the underlying data analysis concepts needed to employ the software and programming tools effectively
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 01:40
Description: Data integration is the last step in the data wrangling process where data is put into its useable and structured format for analysis. In this course, you'll explore examples of practical tools and techniques for data integration.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 00:43
Description: Once data is transformed into a useable format, the next step is to carry out preliminary data exploration on the data. In this course, you'll explore examples of practical tools and techniques for data exploration.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 00:59
Description: Once data is filtered the next step is to transform it into a usable format. In this course, you'll explore examples of practical tools and techniques for data transformation.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 00:48
Description: Once data is gathered for data science it is often in an unstructured or raw format. Data must be filtered for content and validity. In this course, you'll explore examples of practical tools and techniques for data filtering.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 01:02
Description: To carry out data science, you need to gather data. Extracting, parsing, and scraping data from various sources, both internal and external, is a critical first part in the data science pipeline. In this course, you'll explore examples of practical tools for data gathering.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 01:14
Description: Data science differentiates itself from academic statistics and application programming by using what it needs from a variety of disciplines. In this course, you'll explore what it is to be a data scientist and study what sets data science apart from other disciplines. It prepares learners to navigate the foundational elements of data science.
Target Audience: Individuals with some programming and math experience working toward implementing data science in their everyday work
Duration: 00:44