(A Place for Transformation)


Today The Department of Computer Science & Applications has organized a PANEL DISCUSSION CUM EXPERTS TALK On IMPORTANCE OF DATA SCIENCE AND ITS SCOPE

  Date: 12.06.2021-Saturday

   TIME: 10.30 am to 12.15 pm

WELCOME ADDRESS-Dr.J. Dhilipan-HOD/MCA-Vice Principal Admin-CSH

Dr.J. Dhilipan-HOD/MCA and Vice Principal-Admin presented the welcome address. During this, he welcomed the chief guests of the function.

SRMIST provides lot of events for the benefit of students, and a new course BCA DATA SCIENCE is introduced with the curriculum designed by domain experts

He added that We organise many programs every year and provide placement opportunities

Datascience is impacting in all the domains and will take over the industry in future by creating job opportunities.

He concluded his address by stating that Data science will create new job opportunities in various domains.

He concluded his address by requesting the participants to make use of this forum

 Mrs.J. Shobana and Mrs.D. Kanchana introduced the guest of the function to the audience and read their profile


Dr. S. R. Balasundaram, M.C.A,M.E,Ph.D ., Professor/Department of Computer Applications,National Institute of Technology, Tiruchirappalli – 62

Dr. S. R. Balasundaram started the session with an introduction to data science. What is Data Science? -Data science is the iterative cycle of designing a concrete problem, building an algorithm to solve it (or determining that this is not possible), and evaluating what insights this provides for the real underlying questions

He then discussed about the history of data science. He then explained about Pre-Requisites for data science

Descriptive question – to summarize a characteristic of a set of data. Exploratory question – to analyse the data to see if there are patterns, trends, or relationships between variables. Inferential question – restatement of this proposed hypothesis   as a question and would be answered by analysing a different    set of data

He discussed about

Programming for the entire project is different

Programming for data science related activities is different

He then explained about

Discovery: It Involves acquiring data from all the identified internal & external sources

To answer the business question.

The data can be:

Logs from webservers

Data gathered from social media

Census datasets

Data streamed from online sources using APIs

His explanation continued with

Model Planning: To determine the method and technique to draw the relation between input variables. Planning for a model is performed by using different statistical formulas and visualization. SQL analysis services, R, and SAS/access are some of the tools used for this purpose.

Model Building:

Actual model building process begins.

Distributes datasets for training and testing.

Association, Classification, and Clustering are applied to the training data

He concluded his address by explaining aboutOperationalize:

Deliver the final baselined model with reports, code, and technical documents.

Model deployed into a real-time production environment after thorough testing.

Communicate Results

Major findings are communicated to all stakeholders.

To decide if the results of the project are a success or a failure based on the inputs from the model.

Data Analyst:

Data analyst is an individual, who performs mining of huge amount of data, models the data, looks for patterns, relationship, trends, and so on.

At the end of the day, comes up with visualization and reporting for analyzing the data for decision making and problem-solving process.

‍Skill required: For becoming a data analyst, you must get a good background in mathematics, business intelligence, data mining, and basic knowledge of statistics.

‍Familiar with some computer languages and tools such as MATLAB, Python, SQL, Hive, Pig, Excel, SAS, R, JS, Spark, etc.

Technical session 2

Mr.M.James Manoharan

GM – CorporateEngagement

Crampete Pte Ltd and

Brainvalley Software Pvt Ltd

He Started the session with an introduction of various Data Science job titles available

He then explained about Data Science related myths

No programming skills required

Anyone can study

Highly paid jobs

Then he explained the various skills required for Data science job

His lecture continued with the explanation of technical skills required for various job titles in data science industry

He then explained about programming languages to be learned

What is the need for learning Python Programming language?

Specific needs of programming languages in data science

Then he discussed about the need of R programming

What makes SQL and R great?

He also discussed about Data Science tools and their importance

His discussion continued with the explanation of the following

Data science tools available

Data integration and transformation tools

Database visualization tools

Model monitoring and assessment

Code asset management

Development environment

He concluded his session by explaining about the following concepts

Execution environment in data science

Fully integrated visual tools and examples for the same

Programming languages to be studied by students

Then the session was opened to the audience for interaction and clarification of their queries. The entire session was much useful for all the participants

Mr.N. KRISHNAMOORTHY, AP/MCA, Coordinator of the event proposed the vote of thanks