Course Review – Executive Programme in Data Science by IIM Lucknow
The programme brings forth opportunities to obtain in-depth knowledge of data structure and analysis. The comprehensive curriculum provides specialization in the tools which are in demand in the Data Science sector using the fundamentals of Excel and R. Along with strengthening your technical know-how, the course proves a range of career services which includes services from LinkedIn profile building up to Job placement assistance. The faculty of IIM-Lucknow and experts from the data science field provide insight into the global workings of the domain. It is designed to provide maximum exposure to the data-driven business decisions one has to make in leading positions.
Highlights
URL | https://iiml.eruditus.com/executive-programme-in-data-science/index.php?utm_source=Web |
Mode of Learning | Online |
Duration | 9 Months |
Fee | Rs. 1,95,000+GST |
Key Features
The Executive Programme in Data Science focuses on building the foundational strength of each candidate. The theoretical aspects highlight the cardinal concepts which data science professionals apply in their projects. The course is also designed keeping in mind that each understudy can efficiently manage their real-world positions along with developing their skills at their own convenience through this programme. Successful completion of this program also makes an individual eligible for alumni status. The three-day immersion experience at the IIM-L campus is provided to peek into the data-driven culture of the institution. Development of soft skills is paramount for showcasing the skills obtained from this course, for which there is a range of career services included.
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Content coverage: The course is built to cover all the basics which are a prerequisite in positions at different levels of Data Science companies. The preliminary sessions include introductions to R, Cloud technologies and other fundamentals. This is followed by a transition into predictive analysis and machine learning ideas and only then are the understudies given an insight into the world of Big data and AI. Such a curriculum enhances the absorbing capacity of each candidate and gives them an idea about the applications of each theoretical concept.
Mode of Learning: The program is completely online and includes complex concepts of Artificial Intelligence, Deep Learning and strategic case studies.
Target Audience: The program presents opportunities for mid-level up to expert level professionals who have had prior work experience of a minimum of five years.
Learning support: Live sessions with experts from the industry, interview support, and group sessions of effective resume building.
Price Aid: The program offers a loan at an EMI of Rs. 8,507. It also furnishes special prices for corporate enrolment.
College pedigree: The Indian Institute of Management Lucknow is one of the leading colleges in India. It was ranked seven overall in NIRF 2021 rankings. The college provides a strong alumni network and offers great resume weightage for career transition.
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What makes the course unique
The curriculum provides a focused approach to learning the fine nuances of data science. The brochure promises a rigorous nine-month program, delivered via engaging live online sessions and a three-day on-campus immersion at the college campus. Joining this programme will help candidates to build a robust foundation in practical business decision-making using statistics and in-demand data science tools and practices using Excel and R. The programme is divided into three modules with a focus on big data and cognitive analytics. The programme gives a great hands-on insight into popular data tools such as SQL, Hadoop , HIVE and spark.
Course Offering
The following points cover the major offerings by the certification.
Chief skills which are sought-after in the domains of ML and Data Science
Soft-skills development sessions in groups to grasp from peer interactions.
90-minute workshops from career management professionals.
IIM Lucknow campus immersion opportunity for three Days.
Executive alumni status and an opportunity to interact with fellow alumni.
Key discussion points
Value Addition: Excelling college pedigree and alumni status, hands-on exposure to real-world challenges as case studies, networking opportunities with experienced peers and experts, special corporate pricing.
Points to debate: lack of clarity on the case studies and capstone project. The program also does not offer any fully funded scholarships.
Career Options
Data Analyst: A data analyst must be able to leverage a host of algorithms to arrive at a particular solution using data and analytics. The role is spread across various verticals such as setting up the data pipeline, managing data and analyzing patterns. Hence, a data analyst must hold a variety of skills to develop an end-to-end enterprise solution. The analyst must be able to apply algorithms depending on the data type and use case. They are also responsible to discover data patterns hidden within the various data touchpoints to enable data-backed decision making.
Data Science lead: A Data science lead must hold the understanding of analyzing multiple data types and the algorithms that can be applied to complex problems. A data scientist also advises on the entire data architecture and store mechanism so as to easily absorb data when needed for scalable solutions and , in order to develop an enterprise-wide platform for data usage.
Data engineer: A Data engineer must manage and analyze data storage and categorization so that it is easily available to a data scientist or an analyst while working on complex use cases. Cleaning data and categorization of data is a complex job, especially when it is intertwined with data storage and data architecture. A data engineer must develop the flow of data such that it is cleaned and categorized for end use. They also advise on the entire data pipeline so that they are able to maintain and scale data in an efficient manner.
Big data analyst: A Big data analyst must hold exceptional skills to manage huge amounts of data in an efficient and easy to use manner. As organizations go digital and mature, the amount of data touch points increases exponentially. A Big data analyst must be able to leverage tools such as Hadoop, Spark, Hive to manage, clean, and catalog data efficiently. They must design systems that are scalable and easy to use when the need arises to extract a certain portion of the big data for problem-solving.
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