B.Sc. CS in Data Science and Analytics typically covers a broad range of topics related to data analysis, statistics, programming, and machine learning. The program aims to equip students with the necessary skills and knowledge to work with large datasets, extract valuable insights, and make data-driven decisions. Here are some common highlights of a B.Sc. in Data Science and Analytics program :
Foundational Courses: These courses lay the groundwork for understanding data science concepts and techniques. They may include Introduction to Data Science, Introduction to Statistics, and Fundamentals of Programming.
Data Collection and Management: Students learn methods for collecting, cleaning, and managing data from various sources. Topics may include data preprocessing, data wrangling, and database management.
Statistical Analysis: Courses in statistical analysis teach students how to apply statistical methods to analyze data and draw meaningful conclusions. Topics covered may include probability theory, hypothesis testing, regression analysis, and time series analysis.
Machine Learning: Machine learning is a fundamental aspect of data science. Students learn about different machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and clustering techniques.
Data Visualization: Effective data visualization is crucial for communicating insights derived from data. Students learn about visualization techniques and tools to create clear and informative visualizations that aid in data exploration and presentation.
Big Data Technologies: With the increasing volume and complexity of data, students may learn about big data technologies such as Hadoop, Spark, and NoSQL databases. They gain hands-on experience working with large datasets and distributed computing frameworks.
Ethics and Privacy in Data Science: Given the sensitive nature of data, students explore ethical considerations and privacy concerns related to data collection, storage, and analysis. They learn about relevant regulations and guidelines governing data usage and privacy protection.
Capstone Projects and Internships: Many programs require students to complete capstone projects or internships where they apply their skills and knowledge to real-world problems. This hands-on experience helps students gain practical insights and build a portfolio of projects to showcase to potential employers.
Elective Courses: Depending on the program, students may have the opportunity to choose elective courses in specialized areas such as natural language processing, image recognition, social network analysis, or business analytics.
Soft Skills Development: In addition to technical skills, programs may also focus on developing students’ soft skills such as critical thinking, problem-solving, communication, and teamwork, which are essential for success in the field of data science.
Overall, a B.Sc. in Data Science and Analytics provides students with a solid foundation in data science principles and prepares them for careers in a variety of industries where data-driven decision-making is essential.
To empower students with cutting-edge technology for innovation, addressing cognitive challenges, and improving the lives of citizens globally.
- To harness the power of data science and solve real-time challenges through data –driven insights and innovations.
- To optimize operations in data science that enhances customer experiences and unlocks new opportunities for growth in the digital era.
- To faster a culture of cross – disciplinary thinking with professional ethics that navigate the complexities of data landscape and solve challenges across Industries and Public /People needs.
A candidate should have passed the Higher Secondary Examination(10+2 System) conducted by the Board of Higher Secondary Education, Government of Tamilnadu or from any other higher Secondary Board accepted by the Madurai Kamaraj University as equivalent there to with Mathematics and Physics as subjects.
Duration of the Course
The student shall undergo the prescribed course of study for a period of not less than three academic years (Six Semesters).
Medium of Instruction : English
- Data Journalist
- Data Analyst
- Statistical Analyst
- Computer Systems Analyst
- Database Administrator
- Business Intelligence Analyst
- Data Scientist
- Machine Learning Engineer
- Data Engineer
- Data Architect
- Application Architect
- Enterprise Architect
| Course Name |
| General Tamil – I |
| French Language, Culture and Civilization I |
| Hindi I |
| Communicative English I |
| Problem Solving using C Lab |
| Data Analytics using Excel Lab |
| Statistics I / E-Commerce Technologies / Client-Server Computing |
| Digital Principles & Computer Organization |
| MS Office Lab I / Web Designing Lab I |
| Soft Skill -1 Practical
|
Scope of Data Science
The field of Data Science is one of the fastest growing across globally. In recent years, there has been a surge in the amount of data available, and businesses are increasingly looking for ways to make use of this data. As a result, data scientists are in high demand. Data Science is a relatively new field, covering a wide range of topics, from machine learning and artificial intelligence to statistics and cloud computing.
- Data Science is a relatively new field, so there is still a lot of excitement and interest surrounding it.
- The potential applications of data science are vast, and global businesses are just beginning to scratch the surface of what is possible.
- Many global companies are investing heavily in Data Science as they realize the competitive advantage that it can provide.
- The Indian government also supports Data Science careers in India, investing in infrastructure and initiatives to promote adopting data-driven practices.
- The talent pool of data scientists is rapidly growing as more people see data science’s future scope in India.
- There are already many success stories of Data Science applications in India, and this will only likely continue in the future.
Data Science job opportunities in India are vast, and there are many opportunities for those with the right skill set. With the right training, data scientists can help businesses to improve their decision-making, gain insights into their customers, and automate their operations. In short, the scope of Data Science in India is unlimited.
- Microsoft Technology Associate Certification – Data Science using Python
- To make students competent for higher studies and employable, to meet industrial requirements.
- To develop students having core competence in science, mathematics and fundamentals of Data Science to address ever-changing industrial requirements globally.
- To create academically conducive environment to learn skills in the domains such as Data Analytics, Data Modelling, Data Visualization and Allied Technologies.
- To enrich students with professional ethics, leadership qualities, and entrepreneurial skills.
The field of data science and analytics offers a wide range of career opportunities across various industries. As organizations increasingly rely on data to drive decision-making and gain insights into their operations, the demand for professionals with skills in data science and analytics continues to grow. Here are some common career opportunities in this field:
Data Scientist: Data scientists are responsible for analyzing large datasets to identify patterns, trends, and insights that can inform business decisions. They use statistical analysis, machine learning algorithms, and programming skills to extract valuable information from data.
Data Analyst: Data analysts focus on interpreting data to help organizations make informed decisions. They collect, clean, and analyze data using tools such as SQL, Excel, and data visualization software to create reports and dashboards that convey insights to stakeholders.
Business Analyst: Business analysts bridge the gap between business objectives and data insights. They work closely with stakeholders to understand business requirements, identify opportunities for improvement, and use data analysis to drive strategic decision-making.
Machine Learning Engineer: Machine learning engineers design, develop, and deploy machine learning models and algorithms to solve complex problems. They have expertise in programming languages such as Python or R and knowledge of machine learning frameworks like TensorFlow or PyTorch.
Data Engineer: Data engineers are responsible for building and maintaining the infrastructure required to collect, process, and store data. They design data pipelines, implement data integration solutions, and ensure data quality and reliability across the organization.
Quantitative Analyst (Quant): Quants apply mathematical and statistical methods to financial and risk management problems. They develop models for pricing financial instruments, assessing market risk, and optimizing investment strategies using quantitative techniques.
Data Architect: Data architects design and implement the structure and organization of data systems within an organization. They develop data models, define data standards, and establish data governance policies to ensure data consistency, security, and accessibility.
Data Visualization Specialist: Data visualization specialists create compelling visualizations and interactive dashboards to communicate data insights effectively. They use tools like Tableau, Power BI, or D3.js to design visualizations that enable stakeholders to explore and understand complex datasets.
AI/ML Researcher: AI/ML researchers work on advancing the field of artificial intelligence and machine learning by developing new algorithms, techniques, and models. They often work in research institutions, universities, or technology companies to push the boundaries of AI and ML capabilities.
Data Science Consultant: Data science consultants provide expertise and guidance to organizations on leveraging data science and analytics to solve business challenges. They work with clients to define project goals, develop analytical solutions, and implement best practices for data-driven decision-making.
These are just a few examples of the diverse career paths available in data science and analytics. With the increasing demand for data-driven insights across industries, professionals with skills in data science and analytics are well-positioned for rewarding and high-impact careers.
Technical courses for data science and analytics encompass the key skills, tools, and techniques that professionals in this field use to extract insights from data and drive decision-making. Here are some technical cousres which we follow at SLCS
Programming Languages: Proficiency in programming languages is crucial for data science and analytics. Python and R are two of the most commonly used languages for data analysis, statistical modeling, and machine learning. Proficiency in languages such as SQL for data querying and manipulation is also important.
Statistical Analysis: Understanding statistical concepts and techniques is essential for analyzing data and drawing meaningful conclusions. Proficiency in statistical methods such as hypothesis testing, regression analysis, time series analysis, and multivariate analysis is important for data scientists and analysts.
Machine Learning: Machine learning algorithms enable data scientists to build predictive models and uncover patterns in data. Knowledge of supervised learning techniques (e.g., regression, classification), unsupervised learning techniques (e.g., clustering, dimensionality reduction), and deep learning algorithms is valuable for solving a wide range of data science problems.
Data Visualization: Data visualization is the process of representing data graphically to aid understanding and interpretation. Proficiency in data visualization tools and libraries such as Matplotlib, Seaborn, ggplot2, and Tableau is important for creating clear and insightful visualizations that communicate complex data effectively.
Big Data Technologies: As the volume, velocity, and variety of data continue to grow, proficiency in big data technologies is becoming increasingly important. Familiarity with distributed computing frameworks such as Apache Hadoop and Apache Spark, as well as knowledge of NoSQL databases such as MongoDB and Cassandra, enables data scientists to work with large-scale datasets efficiently.
Data Wrangling and Cleaning: Data wrangling involves the process of cleaning, transforming, and preparing raw data for analysis. Proficiency in tools and techniques for data wrangling, such as pandas for Python or dplyr for R, is essential for ensuring data quality and consistency.
Data Mining and Exploration: Data mining involves discovering patterns and relationships in large datasets. Proficiency in exploratory data analysis (EDA) techniques, such as descriptive statistics, data visualization, and feature engineering, enables data scientists to gain insights and identify important patterns in the data.
Natural Language Processing (NLP): NLP involves the analysis and understanding of human language data. Proficiency in NLP techniques and libraries, such as NLTK (Natural Language Toolkit) and spaCy, enables data scientists to work with text data for tasks such as sentiment analysis, named entity recognition, and text classification.
Version Control: Version control systems such as Git enable data scientists to track changes to code and collaborate effectively with team members. Proficiency in version control tools and workflows is important for managing codebase, reproducing experiments, and maintaining project integrity.
Cloud Computing: Cloud computing platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide scalable infrastructure and services for data storage, processing, and analysis. Proficiency in cloud computing enables data scientists to leverage cloud resources for building and deploying data-driven applications and solutions.
These technical courses at SLCS represent the core skills and knowledge areas that are essential for success in the field of data science and analytics. Continuous learning and staying updated with the latest developments in technology and methodologies are key for data professionals to stay competitive in this rapidly evolving field.
