Course Review – Professional Certificate Programme in Advanced Data Analytics for Managers by IIMK

The programme offers to comprehensively cover all the various techniques and tools used in the realms of data analytics. The programme is taught in 10 modules in the form of online lectures. The sessions are conducted on a weekend and usually comprise a three-hour learning webinar on Saturdays. This allows the candidates to manage their current commitments along with polishing their skills with this programme that is offered by IIM Kozhikode.

The modules start from data analytics and R and span to text mining including social media analytics, covering classification, clustering and visualization along the way. The programme is designed to ensure a strong foundation in the concepts of various skills that are essential to understanding data analytics for expert-level professionals. Here is the IIM Kozhikode data analytics course review.

Highlights

Key Features

The Professional Certificate Programme in Advanced Data Analytics focuses on creating value by making learning an integrated experience. It covers an exhaustive list of essential tools and skills within the domain of data analytics. Data analytics as a field cannot be taught in theory and is highly dependent on the hands-on case study learning that the programme provides. There are three major case studies that are covered throughout the course which focus on applied learning. The case studies not only focus on the use of multiple techniques within data analytics but also cover business intelligence and visualisation as a part of the integrated learning experience.

Content Coverage: The course is built to be covered in 10 modules with a special focus on covering all major data analytics modules such as classification, regression, text mining, clustering, and statistical inferences. Along with the fundamentals, essential complementary skills such as visualisation and business intelligence are also covered.

Mode of Learning: The programme is completely online with a flexible learning approach of having weekend classes (three hours on Saturdays) and a mix of offline recorded videos and case study modules.

Target Audience: The programme presents prospects for mid-level up to expert-level professionals who have had prior work experience with analytics (not compulsory).

Learning Support: Live sessions with experts from the industry, and group sessions on resume building added with LinkedIn profile building are offered through Eruditus. The course also provides access to an alumni network and provides an alumni status.

Price Aid: The programme offers a loan at an EMI of Rs. 9,735. It also facilitates special prices for corporate enrolments. The course lacks any form of fully-funded scholarship.

College pedigree: IIM Kozhikode is one of the prominent colleges in India. It was ranked fourth overall in the NIRF 2021 rankings. The college provides a strong weightage to your resume and access to its rich alumni base.

What Makes the Course Unique?

The curriculum provides a concentrated approach to learning the fine nuances of data analytics. The 10 modules are designed to provide end-to-end coverage on multiple algorithms that leverage analytics. The modules also throw light on data engineering concepts such as data categorisation and cleaning, along with visualisation and business intelligence concepts. A prominent feature that stands out is the alumni status which adds huge weightage to one's resume and networking groups. The course focuses on real-world examples, industry-relevant studies, and practical sessions, that ensure a strong foundation in business analytics and decision-making.

Course Offering

The following points cover the chief offerings by the certification.

• Hands-on case studies and a certificate directly by IIM-K along with an alumni status.

• Exposure to tools such as Power-BI, Tableau and R.

• Immersive knowledge-driven training with, data-backed decision-related projects & Capstone Project.

• Learn to use an analytical tool such as R to manipulate and analyze complicated data sets and become proficient in constructing machine learning models using R.

• Learn social media analytics which has use cases in multiple domains.

Key Discussion Points

Value Addition: Outshining college pedigree and alumni status, hands-on applied to learn through case studies, networking opportunities with experienced peers and experts, and comprehensive course coverage.

Points to debate: Lack of a large-scale capstone project. The program also does not offer any fully funded scholarships.

Career Options

Data Analyst: A data analyst must be able to leverage multiple algorithms based on the type of domain data available, to solve complex enterprise-level problems. The role is spread out across various data management levels. Hence, a data analyst must hold a range of skills to develop an end-to-end business solution. The analyst must be capable of understanding data types and the best-fit algorithms. They are also accountable for leveraging data for use cases such as forecasting and inventory management. Thereby, making their role essential in various domains and across verticals.

Data Science Lead: A Data science lead must hold an excellent understanding of analyzing multiple data types and a host of algorithms that can be applied to complicated problems. A data scientist must also look after the entire data architecture and data warehousing mechanism, so as to readily use data across use cases with the help of flexible APIs.

Data Engineer: A Data engineer must work on various data touchpoints in order to categorize and clean the data available. This is not as straightforward as it sounds because the data is not in a standard form across domains. Cleaning data and classification of data is a time-consuming and meticulous job, especially when it is interwoven with the entire data management pipeline. A data engineer must expand the flow of data such that is it standardized and categorized for end-use.

Big Data Analyst: A Big data analyst must hold skills that are focused on managing enormous amounts of data in an efficient and scalable manner. As digital adoption picks up pace, the amount of data touch points increases multifolds. A Big data analyst must be able to effectively use tools such as Hadoop, Spark, Hive and several others. to operate, remove outliers, and catalogue data efficiently.