Decoding Digital Age
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IBPS IT Officer
Course Syllabus
A comprehensive breakdown of all topics covered in this program.
1. Database Concepts
ER-model
Learn
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Relational Model (Relational Algebra, Tuple Calculus)
Learn
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Integrity Constraints
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Normal Forms (1NF, 2NF, 3NF, BCNF)
Learn
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File Organization & Indexing (B and B+ trees)
Learn
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Transactions & Concurrency Control
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2. SQL Queries
Data Manipulation (Select, Update, Delete)
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Data Definition (View, Truncate, Alter)
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Joins (Inner, Outer, Left, Right)
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Aggregate Functions (COUNT, SUM, AVG)
Learn
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Set Operations (Union, Intersection, Except)
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Clauses (IN, EXISTS) & Nested Queries
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3. Programming Concepts (Java / C++)
Program Control (Iteration, Recursion) & Functions
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Scope & Binding of Variables, Parameter Passing
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OOPS Concepts
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Inheritance, Class and Object
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Constructors & Functions
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Exception Handling
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4. Data Analytics Languages (Python / R)
Regex, Slicing, Data Reshaping
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Data Structures (Dataframes, Dictionaries, Sets, Lists)
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File Management (Importing/Exporting Data)
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Classes and Functions
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Data Mining Basics
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Charts and Graphs (Visualization)
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5. Algorithms for problem solving
Tree and Graph Traversals
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Connected Components, Spanning Trees
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Shortest Path Algorithms
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Hashing, Sorting, Searching
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Design Techniques (Greedy, Dynamic Programming, Divide-and-conquer)
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6. Networking Concepts
ISO/OSI Stack & TCP/IP Model
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LAN Technologies (Ethernet, Token Ring)
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Protocols (TCP/UDP, IP)
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Network Devices (Switches, Gateways, Routers)
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Application Layer Protocols (DNS, SMTP, POP, FTP, HTTP)
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Firewalls
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7. Information & Cyber Security Concepts
Cyber Attacks & Software Development Security
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Network Security & Authentication
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CIA Triad (Confidentiality, Integrity, Availability)
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Network Audit & Systems Audit
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8. Data Warehousing
ETL (Extraction, Transformation, Loading)
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Data Cleaning
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Metadata, Data Cube, Data Mart
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Data Models
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9. Shell Programming
Shell Scripting Basics
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Shell Variables & Script Arguments
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Control Flow (If Statement, Loop, Return)
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Basic UNIX Commands
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10. AI / ML
Supervised vs. Unsupervised Learning
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Data Preprocessing & Feature Engineering
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Models (Linear/Logistic Regression, Decision Trees, Clustering)
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Model Evaluation (Cross-Validation, Overfitting/Underfitting)
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Libraries (TensorFlow, PyTorch, Scikit-learn)
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NLP, Neural Networks, Reinforcement Learning
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Cloud AI Services & Deployment Basics
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