BCA in Data Analytics at Digital Computers - Course, Fees, Admission, Seats, Syllabus

When designing a syllabus for a course like "Digital Computers in Data Analytics" as part of a Bachelor of Computer Applications (BCA) program it is important to outline the topics learning goals and assessment methods. This will provide students with a foundation in the fundamental concepts of digital computers and their application in data analytics. It's worth noting that the specific content and structure may vary depending on the institution. However I have provided a framework, below that can be adapted or expanded upon. Learn how to create programs for processing and analyzing data with the following course outline; Unit 1; Getting Started with Digital Computers Explore the history and evolution of computers. Understand the components of a computer system, including the CPU, memory and input/output devices. Discover the binary and hexadecimal number systems. Learn about data representation using bits, bytes and different data types. Unit 2; Computer Architecture and Operating Systems Gain an overview of computer architecture, including the CPU, memory hierarchy and I/O systems. Understand the role of operating systems in data analytics. Get introduced to computer networks and data communication basics. Unit 3; Data Storage and Processing Learn about data storage devices and media options. Explore file. Databases for efficient data storage. Get an introduction to algorithms and data structures used in data processing. Unit 4; Introduction to Data Analytics Understand the importance of data analytics in domains. Explore types of data analytics such as descriptive diagnostic predictive prescriptive analytics. Learn statistical concepts and techniques used for effective data analysis. Unit 5; Tools and Techniques in Data Analytics Get introduced to programming languages commonly used in data analytics like Python or R. Discover popular software tools and platforms utilized in data analytics such, as Excel, Tableau or SQL.

The content of a Bachelor of Computer Applications (BCA) program with a specialization in Data Analytics may vary across institutions. However there are key subjects that are commonly included in such a curriculum. Here are the main topics you can expect to encounter in a BCA program focused on Data Analytics; **Introduction to Data Analytics** An overview of Data Analytics The significance and relevance of Data Analytics in todays world Types of Data Analytics; Descriptive, Diagnostic, Predictive, Prescriptive These subjects provide a solid foundation, for understanding and applying Data Analytics within the realm of digital computers.*Foundational Concepts in Data Analysis* Categories of Data; Structured, Unstructured, Semi structured Ensuring Data Quality and Governance Effective Management of the Data Lifecycle *Programming for Analyzing Data* Introduction to programming languages like Python or R Basics of programming; Variables, Data Types, Control Structures Utilizing Libraries and frameworks for Data Analytics (e.g., Pandas, NumPy, Matplotlib in Python) *Understanding Database Management* Introduction to Database Concepts Employing SQL (Structured Query Language) for manipulating data Exploring NoSQL databases for managing large scale datasets *Uncovering Insights through Data Warehousing and Mining* Key concepts behind data warehousing strategies Understanding ETL (Extract, Transform, Load) processes Exploring different techniques and algorithms used in data mining *Harnessing the Power of Big Data Analytics* Introduction to the concept of Big Data Familiarity with Big Data Technologies such as Hadoop and Spark Addressing challenges and recognizing opportunities in Big Data Analytics *Statistical Approaches for Analyzing data* Descriptive Statistics; Summarizing data effectively Inferential Statistics; Making conclusions about a population based on sample data Probability Distributions and Hypothesis Testing *Understanding Machine Learning* Differentiating between Supervised and Unsupervised Learning approaches Familiarizing with commonly used algorithms like Linear Regression, Decision Trees, Clustering techniques Gaining an introduction, to Deep Learning methodologies"Data Visualization; Understanding the principles behind data visualization Familiarity with popular tools like Tableau and Power BI for data visualization Creating interactive and real time data visualizations Ethics and Privacy in Data Analytics; Knowledge of data security and privacy laws Recognizing ethical considerations when working with data analytics Addressing bias and ensuring fairness in both data and algorithms Capstone Project; Applying the concepts learned to a practical real world project Collecting, analyzing and presenting findings based on the project Emerging Trends in Data Analytics; Exploring the role of artificial intelligence, in data analytics"