Data Science (Online Courses)
Elevate your career trajectory with our premier online course, designed to sharpen your competitive edge. Explore our curated selection of top-tier digital programs to hone your skills and propel your professional journey forward. Experience transformative learning tailored to empower your career advancement in today's dynamic landscape.
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This Course Includes
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29 hours 20 minutesof self-paced video lessons -
23 Programscrafting your path to success -
Completion Certificateawarded on course completion
Advanced and Interactive Visualization Bootcamp: Session 1 Replay
Price on Request
2 hour 55 minutes
This is a recorded Replay of the Advanced and Interactive Visualization Live session that ran on July 12th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 173
Compliance Standards: AICC
Advanced and Interactive Visualization Bootcamp: Session 2 Replay
Price on Request
2 hour 35 minutes
This is a recorded Replay of the Advanced and Interactive Visualization Live session that ran on July 13th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 157
Compliance Standards: AICC
Advanced and Interactive Visualization Bootcamp: Session 3 Replay
Price on Request
2 hour 15 minutes
This is a recorded Replay of the Advanced and Interactive Visualization Live session that ran on July 14th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 134
Compliance Standards: AICC
Advanced and Interactive Visualization Bootcamp: Session 4 Replay
Price on Request
2 hour 5 minutes
This is a recorded Replay of the Advanced and Interactive Visualization Live session that ran on July 15th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 124
Compliance Standards: AICC
Clustering, Errors, & Validation
Price on Request
35 minutes
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. Examine clustering, errors, and validation in machine learning.
Perks of Course
Certificate: Yes
CPD Points: 33
Compliance Standards: AICC
Data Analysis Concepts
Price on Request
1 hour
There are many software and programming tools for data scientists. Before applying these tools effectively, you must understand underlying concepts. Explore data analysis concepts for effectively employing software and programming tools.
Perks of Course
Certificate: Yes
CPD Points: 60
Compliance Standards: AICC
Data Communication & Visualization
Price on Request
1 hour 10 minutes
The final step in the data science pipeline is to communicate the results or findings. Explore communication and visualization concepts needed by data scientists.
Perks of Course
Certificate: Yes
CPD Points: 72
Compliance Standards: AICC
Data Exploration
Price on Request
50 minutes
Once data is transformed into a useable format, the next step is to carry out preliminary data exploration on the data. Explore examples of practical tools and techniques for data exploration.
Perks of Course
Certificate: Yes
CPD Points: 52
Compliance Standards: AICC
Data Filtering
Price on Request
55 minutes
Once data is gathered for data science, it is often in an unstructured or raw format and must be filtered for content and validity. Explore examples of practical tools and techniques for data filtering.
Perks of Course
Certificate: Yes
CPD Points: 56
Compliance Standards: AICC
Data Gathering
Price on Request
1 hour 5 minutes
In data science, you need to gather data, extracting, parsing, and scraping data from various sources, both internal and external as a critical first part in the data science pipeline. Explore examples of practical tools for data gathering.
Perks of Course
Certificate: Yes
CPD Points: 67
Compliance Standards: AICC
Data Integration
Price on Request
35 minutes
Data integration is the last step in the data wrangling process where data is put into its useable and structured format for analysis. Explore examples of practical tools and techniques for data integration.
Perks of Course
Certificate: Yes
CPD Points: 37
Compliance Standards: AICC
Data Science for Managers 2023 Bootcamp: Session 2 Replay
Price on Request
2 hour 30 minutes
This is a recorded Replay of the Data Science for Managers Live session that ran on March 8th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 150
Compliance Standards: AICC
Data Science for Managers 2023 Bootcamp: Session 3 Replay
Price on Request
2 hour 20 minutes
This is a recorded Replay of the Data Science for Managers Live session that ran on March 9th at 11 AM ET.
Perks of Course
Certificate: Yes
CPD Points: 138
Compliance Standards: AICC
Data Science Overview
Price on Request
40 minutes
Data science differentiates itself from statistics and application programming by using what it needs from a variety of disciplines. Explore what it means to be a data scientist and what sets data science apart from other disciplines.
Perks of Course
Certificate: Yes
CPD Points: 39
Compliance Standards: AICC
Data Transformation
Price on Request
1 hour 30 minutes
After filtering data, the next step is to transform it into a usable format. Explore examples of practical tools and techniques for data transformation.
Perks of Course
Certificate: Yes
CPD Points: 42
Compliance Standards: AICC
DevOps for Data Scientists: Containers for Data Science
Price on Request
1 hour
In this 16-video course, explore the use of containers in deploying data science solutions by using Docker with R, Python, Jupyter, and Anaconda. Begin with an introduction to containers and their use for deployment and data science. Then examine approaches to infrastructure as code for data deployment, and concepts behind Ansible and Vagrant approaches to data science deployment. Explore the main features of provisioning tools used in data science. You will learn how to use Docker to build data models, then use it to perform model testing for deployment, to manage R deployments, and for a PostgreSQL deployment. Also, discover how to use Docker for persistent volumes. Next, learners look at using Jupyter Docker Stacks to get up and running with Jupyter and using the Anaconda Distribution to run a Jupyter Notebook. This leads into using Jupyter Notebooks with a Cookiecutter data science project. Then learn about using Docker Compose with PostgreSQL and Jupyter Notebook, and using a container deployment for Jupyter Notebooks with R. The concluding exercise involves deploying Jupyter.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
DevOps for Data Scientists: Data DevOps Concepts
Price on Request
45 minutes
To carry out DevOps for data science, you need to extend the ideas of DevOps to be compatible with the processes of data science and machine learning (ML). In this 12-video course, learners explore the concepts behind integrating data and DevOps. Begin by looking at applications of DevOps for data science and ML. Then examine topological considerations for data science and DevOps. This leads into applying the high-level organizational and cultural strategies for data science with DevOps, and taking a look at day-to-day tasks of DevOps for data science. Examine the technological risks and uncertainties when implementing DevOps for data science and scaling approaches to data science in terms of DevOps computing elements. Learn how DevOps can improve communication for data science workflows and how it can also help overcome ad hoc approaches to data science. The considerations for ETL (Extract, Transform, and Load) pipeline workload improvements through DevOps and the microservice approach to ML are also covered. The exercise involves creating a diagram of data science infrastructure.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC
DevOps for Data Scientists: Data Science DevOps
Price on Request
1 hour 10 minutes
In this 16-video course, learners discover the steps involved in applying DevOps to data science, including integration, packings, deployment, monitoring, and logging. You will begin by learning how to install a Cookiecutter project for data science, then look at its structure, and discover how to modify a Cookiecutter project to train and test a model. Examine the steps in the data model lifecycle and the benefits of version control for data science. Explore the tools and approaches to continuous integration for data models, to data and model security for Data DevOps, and the approaches to automated model testing for Data DevOps. Learn about the Data DevOps considerations for data science tools and IDEs (integrated developer environment) and the approaches to monitoring data models and logging for data models. You will examine ways to measure model performance in production and look at data integration with Cookiecutter. Then learn how to implement a data integration task with both Jenkins and Travis CI (continuous integration). The concluding exercise involves implementing a Cookiecutter project.
Perks of Course
Certificate: Yes
CPD Points: 72
Compliance Standards: AICC
DevOps for Data Scientists: Deploying Data DevOps
Price on Request
35 minutes
In this course, learners will explore deploying data models into production through serialization, packaging, deployment, and rollback. You will begin by watching how to serialize models using Python and Pandas. Then the 8-video course takes a look at the tools and approaches to model packaging and deployment. Next, you will explore the concept of the blue-green deployment strategy for data DevOps, which is the strategy for upgrading running software. This leads into examining the concepts behind the Canary deployment strategy in terms of data DevOps. Canary deployments can be regarded as a phase or test rollout on updates and new features. Then take a look at versioning and approaches to rolling back models for machine learning with DevOps. Finally, you will learn about some of the considerations for deploying models to web APIs (application programming interfaces). The concluding exercise involves creating a model by using Python and Pandas, then serializing the results of the model to a file.
Perks of Course
Certificate: Yes
CPD Points: 33
Compliance Standards: AICC
Estimates & Measures
Price on Request
30 minutes
To effectively use the software and programming tools available for data scientists, you must understand underlying concepts. Discover how to use estimates and measures in data analysis.
Perks of Course
Certificate: Yes
CPD Points: 32
Compliance Standards: AICC
Machine Learning Introduction
Price on Request
40 minutes
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. Explore the conceptual elements of various machine learning techniques.
Perks of Course
Certificate: Yes
CPD Points: 39
Compliance Standards: AICC
Using Data to Find Data: Correction & Categorization
Price on Request
50 minutes
Data professionals working with various data management systems must be able to implement data correction by using R and have a good understanding of data and data management systems. In this 12-video course, learners explore how to apply and implement various essential data correction techniques; to follow transformation rules; and to use deductive correction techniques and predictive modeling by using critical data and analytical approaches. Learn more about data wrangling, essentially the process of transforming and mapping data into another format to ensure that data are appropriate for analytical requirements. Along the way, you will learn key terms and concepts, including how to design data dimension; dimensional data design; cleansing data, and cleansing data with Python; data operations for fact finding; and common data operations for fact-finding. Next, learn about data categorization with Python; data visualization in general; and data visualization with Python. In a concluding exercise, you create a series data set by using Python; create a data frame using the series data; and, finally, calculate the standard deviation of the data frame.
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC
Using Data to Find Data: Data Discovery & Exploration
Price on Request
50 minutes
Explore essential approaches of deriving value from existing data in this 12-video course. Learn to produce meaningful information by implementing certain techniques such as data cleansing, data wrangling, and data categorization. The course goal is to teach learners how to derive appropriate data dimension, and apply data wrangling, cleansing, classification, and clustering by using Python. You will examine such useful data discovery and exploration techniques as pivoting, de-identification, analysis, and data tracing. Learn how to assess the quality of target data by determining accuracy of the data being captured or ingested; data completeness; and data reliability. Other key topics covered include data exploration tools; Knime data exploration; data transformation techniques; and data quality analysis techniques. The concluding exercise asks learners to list prominent tools for data exploration; recall some of the essential types of data transformation that can be implemented; specify essential tasks that form the building block to finding data with data; and recall essential approaches of implementing data tracing.
Perks of Course
Certificate: Yes
CPD Points: 52
Compliance Standards: AICC