Course Review – Advanced Certification in Big Data Analytics by Indian Institute of Technology Guwahati

The programme is covered in 18 modules and provides a comprehensive course coverage on Big data analytics. The course covers all major tools such as Apache, Hive, Spark, and Hadoop, that are part of the essential learning curve in the realms of big data analytics. The case studies covered during the course are well-detailed and provide helpful insights that as essential for applied hands-on learning. The projects provide comprehensive coverage of a host of domains such as retail, Supply Chain, Banking and Healthcare, to offer insights on how big data impacts multiple domains and organizational streams. The programme takes into account the major fundamental topics of big data analytics as well as advanced concepts. Hence, it brings forth an excellent platform for beginners or mid-level professionals to build a foundation on big data and help in career transition.

Highlights

Key Features

Advanced Certification in Big Data Analytics is an integrated concept that covers databases, data warehousing, categorization, and running of multiple algorithms over the available data. The programme focuses on creating value by making learning an integrated hands-on immersive experience. It encompasses an exhaustive list of essential tools and skills within the domain of big data analytics. Data analytics as a domain cannot be taught in theory and, is highly reliant on the hands-on case study learning that the programme provides. The program promises 231 hours of live learning and 182 hours of recorded learning content, along with 300 hours of guided projects. This is considerably on the higher side as compared to its peer offerings. The focus on hands-on learning makes this course stand out in the domain of Big data analytics.

Content coverage: The course is built to be covered in 18 modules with a special focus on covering all major big data analytics elements such as classification, regression, text mining, clustering, and statistical inferences. Along with the fundamentals, the programme covers 10 case studies to provide an immersive learning experience.

Mode of Learning: The program is completely online with 231 hours of live webinars, 182 hours of recorded video learning content, and 300 hours of guided projects.

Target Audience: The programme targets beginners and mid-level professionals who would like to build a career in Big data analytics and data management.

Learning support: Live sessions with experts from the industry, and career-oriented sessions are offered along with other learning support such as 1:1 mentoring sessions and profile-building sessions.

Price Aid: The programme offers a loan at an EMI of Rs. 8,000 through various partners. It also facilitates special prices for corporate enrolments.

College pedigree: IIT Guwahati ranks 7th in NIRF INDIA rankings 2023. The college has a rich history of meritocracy and a strong alumni network base.

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What makes the course unique

The programme is designed to fork out the excellence in each candidate in a strategic manner. The curriculum is mapped out to impart the maximum skills one might find essential in the Data analytics sector. Additionally, personalized mentor support is available, keeping in mind the difference in learning paces of different understudies. There is also a two-day immersion experience offered at the campus of IIT Guwahati to provide excellent education exposure. The data analyst course in Guwahati bestows access to IBM Watson in order to provide firsthand training. The peer learning platform also provides a range of career services for soft-skills improvement.

Also Read: 15 Best Hadoop Tutorials To Pursue Online Today

Course Offerings

The following points cover the chief offerings of the IIT Guwahati data analytics course.

• Hackathons are arranged as a method of amusing learning experience.

• Peer collaboration opportunities for exchanging job referrals and interview tricks.

• A guarantee of a minimum of three interviews and a chance for many more in association with more than 400+ hiring partners.

• Flexible learning options of obtaining recordings of live classes and materials in case of other commitments.

• Personalized resume-building sessions to showcase the key skills obtained from the programme.

Key Discussion Points

Value Addition: Personalised mentoring, applied learning opportunities, peer collaboration, and networking opportunities with expert faculty from the field.

Points to debate: The course lacks any form of fully-funded scholarship. However, it furnishes a range of payment options.

Career Options

Big Data Analyst: A big data analyst must understand the fine nuances of managing and making use of the huge amounts of relevant data that an organization manages. As modern organisations progress and become more digitised, the amount of data being managed increases exponentially. This data must be cleaned, and organized and should be stored in a ready-to-use manner. Once this process is completed, a big data analyst would leverage this data to discover patterns or solve complex problems to make data-driven business decisions. They must be aware of the various data types and algorithms that one can leverage in order to achieve a successful accuracy value to predict or forecast numbers.

Big Data Engineer: A Big Data Engineer must focus on important data strategy concepts such as data management, storage, and data cleansing. The amount of data that an organization manages can be collected in multiple forms (for example binary or non-binary), this data must first be standardized. After standardization the data must be categorized and cleaned. The entire pipeline of managing and performing all these essential activities on industry-relevant data comes under the radar of a big data engineer.

Data Science Lead: The primary responsibility of a Data Science Lead is to understand the data types and the algorithms that are being used or could be used in an organization to support data-backed decision-making or to discover hidden patterns which might help in reducing costs, increasing revenue, innovation or creating new streams of business. The lead must understand the domain closely so that the final outcome is aligned with what the domain is trying to achieve. They must advise on the end-to-end setting up of the data pipeline and data strategy.

Data Engineer: A Data Engineer works alongside a data analyst or a big data management professional to set up and manage large-scale enterprise data. Essential data management activities such as data cleansing and categorization come under the responsibilities of a data engineer. They must work towards standardisation of data types and making data readily available for use within multiple use cases across organizational domains.