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Technical Courses

Programming Languages

Areas-Covered

Introduction to C, Algorithms and Relevance of C programming, Basic Structure & Data types, Statements, Operators & Looping structures, Functions, Derived data types, Arrays, Structures and Union, Memory allocation & Preprocessor directive, File handling

Duration

6-8 days

Delivery Methodology

  • Each training session has a defined set of learning outcomes achieved through a combination of theoretical inputs, programming tasks and reinforcement exercises.
  • The majority of the training programme shall be lab-based in order to give practical exposure to students.
  • The training team from SMART will be an ideal mix of theoreticians, class delivery experts and specialist faculty from industry. This ensures that the course structure is sound, training inputs are industry relevant and the students are delivered a power-packed course in an easy-to-understand format.

Hackathon

The training could conclude with a day-long Hackathon of 10-20 hours conducted on campus by a professional industry-experienced team

Optional Add-on

  • Step 1 Preliminary online coding test
  • Step 2 Selecting problem from a given set, designing architecture of the solution
  • Step 3 Validation of design and correctives
  • Step 4 Implementing the solutions, mentoring and fine-tuning

Areas-Covered

Introduction to C++, Basics of Object Oriented Programming in C++, Classes, Objects, Abstraction, Encapsulation, Data Hiding, Polymorphism, Inheritance, Templates, File & Exception Handling etc.

Duration

6-8 days

Delivery Methodology

  • Each training session has a defined set of learning outcomes achieved through a combination of theoretical inputs, programming tasks and reinforcement exercises.
  • The majority of the training programme shall be lab-based in order to give practical exposure to students.
  • The training team from SMART will be an ideal mix of theoreticians, class delivery experts and specialist faculty from industry. This ensures that the course structure is sound, training inputs are industry relevant and the students are delivered a power-packed course in an easy-to-understand format.

Hackathon

The training could conclude with a day-long Hackathon of 10-20 hours conducted on campus by a professional industry-experienced team

Optional Add-on

  • Step 1 Preliminary online coding test
  • Step 2 Selecting problem from a given set, designing architecture of the solution
  • Step 3 Validation of design and correctives
  • Step 4 Implementing the solutions, mentoring and fine-tuning

Areas-Covered

Introduction to Java, Basics of Object Oriented Programming in Java, GUI Development, JDBC Programming, Interaction with Databases etc.

Duration

6-10 days

Delivery Methodology

  • Each training session has a defined set of learning outcomes achieved through a combination of theoretical inputs, programming tasks and reinforcement exercises.
  • The majority of the training programme shall be lab-based in order to give practical exposure to students.
  • The training team from SMART will be an ideal mix of theoreticians, class delivery experts and specialist faculty from industry. This ensures that the course structure is sound, training inputs are industry relevant and the students are delivered a power-packed course in an easy-to-understand format.

Hackathon

The training could conclude with a day-long Hackathon of 10-20 hours conducted on campus by a professional industry-experienced team

Optional Add-on

  • Step 1 Preliminary online coding test
  • Step 2 Selecting problem from a given set, designing architecture of the solution
  • Step 3 Validation of design and correctives
  • Step 4 Implementing the solutions, mentoring and fine-tuning

Areas-Covered

Introduction to Python Programming, Modules and Functions, Strings, Sequences & Slicing, Conditional Statements, Loop Statements, Functions, Object Oriented Programming, File Handling etc.

Duration

6-10 days

Delivery Methodology

  • Each training session has a defined set of learning outcomes achieved through a combination of theoretical inputs, programming tasks and reinforcement exercises.
  • The majority of the training programme shall be lab-based in order to give practical exposure to students.
  • The training team from SMART will be an ideal mix of theoreticians, class delivery experts and specialist faculty from industry. This ensures that the course structure is sound, training inputs are industry relevant and the students are delivered a power-packed course in an easy-to-understand format.

Hackathon

The training could conclude with a day-long Hackathon of 10-20 hours conducted on campus by a professional industry-experienced team

Optional Add-on

  • Step 1 Preliminary online coding test
  • Step 2 Selecting problem from a given set, designing architecture of the solution
  • Step 3 Validation of design and correctives
  • Step 4 Implementing the solutions, mentoring and fine-tuning

Data Structures

Programs and Programming, Algorithms and Data structures Algorithms (Emphasis on Problem Solving Techniques).

  • Introduction: Programs and Programming, Algorithms and Data structures Algorithms (Emphasis on Problem Solving Techniques)
  • Data structure: Arrays, Linked Lists , Stack, Applications of Stack, Infix to Postfix, Queue, Applications of Queue, Recursion
  • Sorting: Selection Sort, Insertion Sort, Bubble Sort, Merge Sort, Quick Sort, Comparative Analysis of Different Sorting and Searching Techniques
  • Searching: Sequential Search, Binary Search, Trees

DBMS & Networking

A refresher course mainly to help students prepare for campus placements. It covers basics of

  • Operating systems, kernels, scheduling algorithms, pages etc.
  • DDL, DQL, DML, ER model, normalization, queries, joines, stored procs, cursors etc.
  • OSI, layers, TCP/IP, UDP, links, various networking protocols, classes, topology, error deduction, correction, framing, sockets etc.

Data Analytics

Introduction to Statistical modelling and computational approach

  • Summarisation, Feature extraction and Statistical limits on data mining
  • Distributed File System, Map reduce, Algorithms using Map Reduce
  • Nearest neighbour search, Shingling of Documents, Similarity preserving summaries, Locality sensitive hashing for documents, Distance Measures
  • Stream Data Model, Sampling Data in the Stream and Filtering, Counting Distance Elements in a Stream, Estimating Moments, Counting Ones in Window, Decaying Windows
  • Page Rank, Efficient Computation, Topic Sensitive Page Rank, Market Basket Model, A-priori algorithm
  • Handling Larger Datasets in Main Memory, Limited Pass Algorithm, Counting Frequent Item sets

Machine Learning

Introduction to Machine Learning

  • Learning as Optimization, Linear Regression, Probabilistic Modeling, Probabilistic Linear Regression, Logistic and Softmax Regression
  • Online Learning via Stochastic Optimization, Perceptron, Learning Maximum-Margin Hyperplanes: Support Vector Machines, Nonlinear Learning with Kernels
  • Data Clustering, K-means and Kernel K-means, Linear Dimensionality Reduction
  • Introduction to Generative Models, Clustering: GMM and Intro to EM, Expectation Maximization and Generative Models for Dim. Reduction, Dim. Reduction: Probabilistic PCA and Factor Analysis
  • Practical Issues: Model/Feature Selection, Evaluating and Debugging ML Algorithms, Introduction to Learning Theory, Ensemble Methods: Bagging and Boosting

Internet of Things (IoT)

  • Introduction to IoT Defining IoT, Characteristics of IoT, Physical design of IoT, Logical design of IoT, Functional blocks of IoT, Communication models & APIs
  • IoT & M2M Machine to Machine, Difference between IoT and M2M, Software define Network
  • Network & Communication aspects Wireless medium access issues, MAC protocol survey, Survey routing protocols, Sensor deployment & Node discovery, Data aggregation & dissemination
  • Challenges in IoT Design challenges, Development challenges, Security challenges, Other challenges
  • Developing IoTs, Introduction to Python, Introduction to different IoT tools, Developing applications through IoT tools

Internet Marketing

  • A practical guide to help learners know more on SEO, usage of Google ad words, adsense and other webmaster tools, besides how to leverage social media for internet marketing