CCSE Tutoring Lab

The College of Computing and Software Engineering (CCSE) Tutoring Lab welcomes all undergraduate and graduate students enrolled in CGDD, CS, CSE, CYBR, IT, and SWE courses! We have progressed to an appointment-based tutoring system using the EAB Navigate. To schedule an appointment, please view below. If you need help when no tutor is available, please see the Program Coordinator in the Main Lab Center for assistance.

  • The Tutoring Lab is for students who are currently enrolled in the course.

    • For undergraduate students : To schedule an appointment please use the link under “Tutoring” section. Your appointment can be “In-person”, “Online”, or come in Tutoring Lab as walk-in for all courses available for tutoring.

      • Please make an appointment in advance. You can schedule up to two appointments per day for each course you currently enroll in. Each session is 30 minutes.
      • If you cannot attend the scheduled session, be sure to cancel your appointment at least 2 hours before the session. Your account will still be open.
      • If you no show and miss three appointments, the system automatically locks
        your account and you will be blocked from making additional tutoring
        appointments for the remainder of the semester 
         
    • For graduate students: You can come to the Tutoring Lab as a walk-in or connect with Program coordinator to schedule “In-person” or “Online” appointment. Graduate students can have help with courses with Languages as Java, C++, C#. For all other courses please contact your instructor.
  • The courses we tutor include the following listed below. Please note that all subjects/languages do not have tutors available at all times. When booking an appointment, click on the "calendar day" to see the next available appointment time.

    • CSE 1321: Programming and Problem Solving I
    • CSE 1321L: Programming and Problem Solving I Laboratory (Java, Python, C#, C++)
    • CSE 1322: Programming and Problem Solving II
    • CSE 1322L: Programming and Problem Solving II Laboratory (Java, C#)
    • CSE 3153 : Database Systems
    • CS 3305: Data Structures
    • CS 3410 : Intro to Database Systems
    • CS 3502: Operating Systems
    • CS 3503: Computer Organization and Architecture
    • CS 3503L: Computer Organization and Architecture Lab (Assembly, Digital Trainer)
    • CS 4306 : Algorithm Analysis
    • CS 5000: Foundations of Programming
    • CS 5020: Computer Architectures and Operating Systems
    • CS 5040: Data Structures and Algorithms
    • IT 1114 Programming Principles (Python)
    • IT 3883 Advanced Application Development
  • Computer Languages we tutor include:

    • Java for CSE 1321, CSE 1321L, CSE 1322, CSE 1322L, CS 5000
    • Python for IT 1114 and CSE 1321L
    • C# for IT 3883
    • C#, Unity for CGDD 2002
    • C++ for CS 3305, CS 3305L, CS 5040, CS 4306
    • Digital Trainers for CS 3503, CS 5020
    • Linux for CS 3502, CS 5020

Schedule An Appointment

Online tutoring sessions are available by appointment only.

    1. Navigate to https://kennesaw.navigate.eab.com
      • You must use your KSU NetID and Password to login. This is the same information you use to access Owl Express.
      • Contact the Student Help Desk through UITS if you have any issues.

    2. After logging into Navigate, click the Appointments tab in the left side menu.

    3. Once in the Appointments menu, click the Schedule an Appointment button at the top right of your screen.

    4. Then use the drop-down boxes to select what type of appointment you need. 
      • For example, asking for C#(Unity) on the data Monday, August 15, 2022

    5. Click Find Available Time.

    6. Review your appointment details.

    7. Select how you would like to meet.

    8. Please add details of what you would like help with in the Would you like to share anything else text field.

    9. Press Schedule and you are done!
    1. Navigate to https://kennesaw.navigate.eab.com
      • You must use your KSU NetID and Password to login. This is the same information you use to access Owl Express.
      • Contact the Student Help Desk through UITS if you have any issues.

    2. After logging into Navigate, click the Appointments tab in the left side menu.

    3. You should see a section that says Upcoming under the My Appointments Click on the appointment link by selecting the word(s) in blue. In this example, I would click on C#.

    4. Scroll to the bottom of menu where it says Cancel and then select the Reason drop-down menu.
      • In the drop-down menu, select the reason you are canceling. For example, you could select “Emergency” or “Sick.” You may put additional details in the Comments box
      • When you have filled out all the fields, select Mark as Canceled or Cancel Appointment 

    5. Once you have confirmed the appointment is canceled, you may click on Close. The appointment should disappear from Upcoming Appointments and you may also get an email. 
SCHEDULE APPOINTMENT

Our Labs

Tutoring Lab

The Tutoring Lab is an open and collaborative-friendly environment that is available to all KSU students and is equipped with computer desktops, tables with power outlets, paper shredder, etc. 

Location: Marietta Campus, Atrium, J Building – Room 263

Hours:
Monday – Friday: 9 a.m. – 10 p.m.
Saturday: 9 a.m. – 5 p.m.

campus printing

Virtual Desktop Infrastructure

The CCSE Virtual Desktop Infrastructure (VDI) environment is virtualization technology that hosts a desktop operating system on a centralized server in our data center. CCSE offers two types of VDI environments: persistent and nonpersistent. Persistent VDI (assigned based on class) provides each user with his or her own desktop image, which can be customized and saved for future use, much like a traditional physical desktop. Nonpersistent VDI (CCSE Standard) provides a pool of uniform desktops that users can access when needed. Nonpersistent desktops revert to their original state each time the user logs out.

view instructions

CCSE Data Center Services

 

The goal of the College of Computer Science and Software Engineering Data Research Center (CCSE-DRC) is to provide a cloud service infrastructure for big data analyses and deep learning using cutting-edge software platforms. The CCSE-DRC provides different services based on the needs of our faculty and students. 

CCSE-DRC architecture consists in physical machines used as sole resource and mainly as cluster. Our cluster lays the foundation for complex software frameworks.

  • KVM’s machines with any Linux distribution are available. Standard setup include 2 Cores, 8 GB memory, and 100 GB Storage, but it can expanded as previous requirement to the CCSE-DRC. Those machines could be Desktop or servers, and can be access using CLI or GUI (SPICE- VNC). For GUI access the user should install the GUI client. 

    Version 

    • Compiled against library: libvirt 6.0.0
    • Using library: libvirt 6.0.0
    • Using API: QEMU 6.0.0
    • Running hypervisor: QEMU 4.2.1 
  • LXD is a next generation system container manager. It offers a user ex- perience similar to virtual machines but using Linux containers instead. Some of the advantages of containers over VM’s includes quicker spin- ning up apps, and better share of resources which is vital in our academic community. Similar to VM’s we offer CLI and Desktop access for our users, and deployment of multiples containers to create a personalized micro-cluster. 

    Version: 

    • Lxd version: 4.0.7 
  • These are shared resources used by our GRA’s students and faculties. Each server has 4 Tesla Maxwell Nvidia’s GPU’s per server and an ISCSI storage server attached. 

    The main deep learning frameworks are installed such as PyTorch, Ten- sorflow, Keras, and Caffe. These servers also include the most important Python libraries for machine learning and data science like Numpy, Pan- das, SciPy, Matplotlib, SymPy, Sckits-image, Sckits-learn, Pandas, Bokeh, and Jupyter among others. In addition, R-cran statistical language, Julia mathematical language and Unix tools are installed. 

    The user can install their own Python libraries using virtual environ- ment, and R packages as no administrators. In case that the user needs administrative rights to install system libraries the servers provides Stan- dard Docker and Docker-nvidia containers. Please ask to CCSE-DRC for more information. 

    Versions: 

    • torch 1.9.0 
    • torchsummary 1.5.1
    • torchvision 0.10.0
    • tensorboard 2.6.0
    • tensorboard-data-server 0.6.1
    • tensorboard-plugin-wit 1.8.0
    • tensorflow 2.5.0
    • jupyter 1.0.0
    • jupyter-client 6.1.12
    • keras 2.6.0
    • keras-nightly 2.5.0.dev2021032900
    • Keras-Preprocessing 1.1.2
    • matplotlib 3.4.2
    • matplotlib-inline 0.1.2
    • numba 0.53.1
    • numpy 1.19.5
    • opencv-python 4.5.3.56
    • pandas 1.3.1 
  • Hadoop is an open-source software framework used for storing and pro- cessing Big Data in a distributed manner on large clusters of commodity hardware.Apache HDFS or Hadoop Distributed File System is a block- structured file system where each file is divided into blocks of a pre- determined size. These blocks are stored across a cluster of one or several machines. Apache Hadoop HDFS Architecture follows a Head/Worker Architecture, where a cluster comprises of a single NameNode (Head node) and all the other nodes are DataNodes (Worker nodes). HDFS can be deployed on a broad spectrum of machines that support Java. Though one can run several DataNodes on a single machine, but in the practical world, these DataNodes are spread across various machines. CCSE-DCR provides a cluster with 10 nodes at the moment but can be expanded as needed. This is a shared resource and can be used to experiment with new map-reduced algorithms or to process huge data. 

    Versions: 

    In Production 

    • Hadoop 2.7
    • Spark 2.1 

    Very Soon: 

    • Spark Standalone Cluster 3.1.2 

    In Progress: 

    • Hadoop 3.0
    • Spark 3.1.2
  • Classic data analysis was performed over data resting mostly on a rela- tional database or plain text. The natural consequence of this approach is that the analysis may be conducted on the whole collected data, or on a data batch accumulated over a period of time. Subsequently, because of the massification of portable devices, traditional storage methods be- came insufficient due to the enormous amount of fluid data available. Thus, new technologies like streaming analytics emerged to solve pre- vious limitations. 

    Streaming analytics processes never ending data originated from con- nected devices(IoT), people networks (social media), and interrelated complex systems (autonomous platforms) among others. Some goals of streaming analytics are to facilitate real time statistical analysis, to per- form machine learning analysis and training, and to interact with other frameworks for permanent data storage. Real time analysis refers not only to real time data analysis, but also to the analysis of data batches 

    collected over short periods of time, ranging from seconds to minutes. In addition, these systems must be able to place the information in tem- porary storage during a specific period of time and should also be able to store more than one temporal batch. 

    CCSE-DCR provides a cluster with 2 Kafka nodes at the moment but can be expanded as needed. This is a shared resource for more information contact CCSE-DRC. 

    Version: 

    Kafka: 0.10 

  • Kubernetes, at its basic level, is a system for running and coordinating containerized applications across a cluster of machines. It is a platform designed to completely manage the life cycle of containerized applica- tions and services using methods that provide predictability, scalabil- ity, and high availability. In conjunction with Kubernetes we use Helm which is a package manager for Kubernetes that allows developers and operators to more easily package, configure, and deploy applications and services onto Kubernetes clusters. For more information contact CCSE- DRC 

    Version: 

    kubectl: 1.21.3 

  • The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. 
  • Faculty, Staff and Students can sign in with their netIDs and passwords and then go to the software section to see everything. This is only for Microsoft products but it includes operating systems and other development software including Project and Vision.

    learn more
  • Access to NETLAB+ is fast and easy, requiring only a web browser with a Java plugin. Build-in applications for virtual machine and device console are included. NETLAB+ provides scheduled access to your virtual machines and lab equipment. All lab access is controlled by reservation, using the scheduler. Using the calendar interface, students and instructors can view the pods and the timeslots available to schedule lab time at their convenience.

    learn more

Contact Us

If you desire to give and/or provide feedback on a tutor’s performance, whether in the classroom or Tutoring Lab:

Please Contact Svetlana Dimova, Mon. - Fri., 9 am - 5 pm below.

Name
Position
  • Director, Computer Lab and Academic Support & Part-Time Assistant Professor of Programming

    cbryan78@kennesaw.edu
    (470) 578-6653
    J 261C

  • Program Coordinator

    sdimova1@kennesaw.edu
    (470) 578-2234
    J 263

Job Opportunities

If you are interested in working for the CCSE Lab Centers, please check Handshake for the following positions:

  • CCSE  Grader
  • CCSE  Lab Technician
  • CCSE  Teaching Assistant
  • CCSE  Lab Assistant – Tutor
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