A learning culture in an organisation inspires constant learning and believes that systems influence each other. Continuous learning not only upgrades an individual as a worker and as a person but it also opens opportunities for the organisation to change constantly for the better.
Here’s a researched and curated list of the relevant courses ideal to learn and grow and help your business grow, too.
Stanford’s Advanced Cybersecurity Program
University: Stanford Center for Professional Development
Professors: Prof. Dan Boneh, Prof. John Mitchell, Neil Daswani
This Cyber Security course will navigate you through the foundations and skills necessary to build a successful career in cybersecurity. In addition to security objectives basics like confidentiality, integrity, non-repudiation and authentication you also get to know about security vulnerabilities. By using secure systems, and design methodology, you will also learn about primary security breaches that occurred over the years and the building of countermeasures for the same.
- Learn about foundations for information security.
- Learn about exploiting and protecting web applications.
- Learn how to use cryptography correctly.
- In this course, you’re going to learn everything about networking.
- Explore the immediate and future dangers related to cyber security and the mechanisms to defend against them.
- Learn and experience intermediate and advanced techniques that systems and applications programmers can use to write new code and find and mitigate vulnerabilities in existing code.
Course Duration: 6 Months
Fee: The cost is $2,500 for the entire course, including tuition fee, learning materials and mentorship.
The course is best for:
- Information Technology Professionals
- Software Developers
- Network Security Engineers
- Software Engineers
- App Developers
- System Architects
- Systems Analysts
Post-Graduate Program in Cloud Computing
University: Great Lakes Executive Learning
Professor: Nirmallya Mukherjee, Raghu Raman AV, Mohit Batra, Deepak Shukla
PG Program In Cloud Computing focuses on empowering professionals to fast-track their careers in Cloud Computing. In this 8-months intensive experiential learning program, learners get access to 100+ hours of online video content, 12 labs, and live weekend mentor sessions to reinforce their learnings with an industry perspective. The program is carefully designed in collaboration with industry leaders to help you become a cross-platform and balanced cloud architect. Through a combination of expert lectures, demos, hands-on labs, and projects, you will build out solutions that the industry expects of you as a cloud professional.
- Be able to oversee a company’s cloud adoption plans, cloud application design, and cloud architecture.
- Be able to design and implement enterprise infrastructure and platforms required for cloud computing.
- Be able to analyse system requirements and ensure that systems will be securely integrated with current applications.
- Develop the ability to architect a cloud environment and make sound component choices.
- Become comfortable working with virtual machines (VM) and the nuances of the most popular tools used.
- Build the ability to use NIST Cloud Reference Architectures to solve a variety of problems faced as a cloud professional.
- Understand trade-offs, cost implications and choose the right cloud services for you.
- Understand Containers and learn how to work efficiently with Dockers
- Use your knowledge of cloud services to suggest and implement cloud-based solutions to your clients and technology teams.
- Build your professional toolkit to become an effective DevOps professional.
Course Duration: 8 Months
Fee: The cost is $1,683 for the entire course.
Upon successful completion of the program, you will receive a PG certificate from Great Lakes Executive Learning.
Eligibility: Candidates should have at least more than three years of experience in IT & technology roles which would include exposure to development, testing/quality assurance, maintenance, database administration and technology infrastructure management.
One should have an understanding of IT Services Management. It is essential to know the basic concepts related to operating systems, like Windows, Linux. Also, prior exposure to the public or private cloud environments, or infrastructure and network management is a plus.
Machine Learning: Fundamentals and Algorithms
University: Carnegie Mellon University’s School of Computer Science
Professors: Pat Virtue, Matt Gormley
Machine Learning: Fundamentals and Algorithms, an online program, provides you with the technical knowledge and analytical methods that will prepare you for the next generation of innovation.
- Synthesise components of machine learning to create functional tools for prediction of unseen data.
- Implement and analyse learning algorithms for classification, regression and clustering.
- Use concepts from probability, statistics, linear algebra, calculus and optimisation to describe and refine the inner workings of machine learning algorithms.
Course Duration: 10 weeks
Fee: The cost is $2,125 for the entire course.
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s School of Computer Science Executive Education.
The course is best for:
- Engineers in IT products and services, healthcare, or banking and financial services who want hands-on instruction in the tools and techniques of machine learning.
- Data analytics professionals in the banking and financial services industry, or IT products and services, with responsibility for publishing reports, innovating, and working with analytics in a data-dense environment. This program will be especially relevant for analysts seeking to implement machine learning into projects or to upgrade from spreadsheet-based analysis to more powerful programmatic models of data analysis.
- Technical managers/directors of data functions leading a team of coders in banking and financial services, IT, healthcare, retail, logistics, or industrial goods who want to create enterprise value and gain hands-on skills in machine learning technology with the goal of solving business pain points.
Data Science in Healthcare
University: Dartmouth QBS
Professors: Christian Darabos, Eugene Demidenko, Todd MacKenzie, James O’Malley, Ramesh Yapalparvi
Data Science in Healthcare is designed for technical professionals who have at least a moderate level of comfort with some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics.
- You will learn to build novel data sets and rigorously analyse them using a multi-pronged approach.
- You will work with public health examples, lab tests, and medical research, which can offer insights for your specific healthcare application.
- You will use cluster analysis and hierarchical clustering to analyse global cases of COVID-19; correlation heatmaps to analyse osteometric measurements; plotting to visualise diabetes rates; and discriminant data analysis to predict heart attacks in various populations.
- You will understand the essentials of R coding and Python, and how to use it for network data, data sets, and data libraries.
- You will use the Tableau software to create data visualisation dashboards to share your healthcare data with stakeholders.
Course Duration: 8 weeks
Fee: The cost is $1,658 for the entire course.
Upon successful completion of the program, participants will receive a verified digital certificate of participation from Dartmouth QBS.
The course is best for:
- Analysts: Ideal for professionals working in analytics roles in healthcare or industries adjacent to healthcare, such as insurance, pharmaceuticals, or biotech.
- Mid-level managers: Ideal for professionals on the executive track who have quantitative responsibilities and relevant experience in the healthcare field.
- Entry-level professionals: Ideal for professionals just beginning their careers who are looking to develop a data foundation with applications in the healthcare industry.
Applied Machine Learning
University: Columbia Engineering Executive Education
Professor: John Paisley
The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language. Participants will spend the first part of this course learning Python for Data Analytics. This will provide them with the programming knowledge required to do the assignments and application projects that are part of the Applied Machine Learning course.
- Define a model for your data and make the model learn.
- Build regression models to predict an unknown output from a given set of inputs.
- Create classification models to categorise datasets such as email messages as spam or non-spam.
- Develop unsupervised models like topic models or recommender systems to extract hidden patterns from large amounts of data
- Determine hidden parameters in data to improve the accuracy of your model’s predictions.
- Create probabilistic data models to predict a range of possible outcomes that account for real-world risks and uncertainties.
Course Duration: 5 months
Fee: The cost is $1,998 for the entire course.
Upon successful completion of the course, participants will receive a verified digital certificate from Emeritus in collaboration with Columbia Engineering Executive Education.
Eligibility: The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation ), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).
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