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Fakir Mohan University (FMU) Distance MBA



Fakir Mohan University (FMU) Distance MBA

Fakir Mohan University is a state-owned institution of higher learning located in Balasore, Odisha, India.

The school was established in 1999 by the Odisha State government under Section 32 of the Odisha Universities Act, 1989 (Act 5 of 1989).

FMU was created from Utkal University. The institution currently offers a distance education program through the Directorate of Distance and Continuing Education. The courses available at the program include:

  • Master of Business Administration (MBA)

Duration of Course

  • The MBA program requires a minimum duration of two years (four semesters)

Fee Structure

  • FMU’s course fee is paid on a semester basis
  • The fee per semester is Rs. 10,000

Fakir Mohan University Accreditation

  • FMU’s distance program is recognized by the University Grants Commission (UGC).

How to Take Admission into Fakir Mohan University

  • Candidates can apply to FMU distance MBA either online or offline
  • The application form and brochure can be downloaded from directorate’s website at or the school’s site (
  • Applicants are required to complete the forms and subject them along with the requested documents to the Directorate of Distance & Continuing Education at the Office of the Director, Fakir Mohan University through post or by hand
  • If you prefer to submit the form by post, you should complete the envelope and superscribe it with “Application for admission into …….…. (name of the course you are applying to)”.
  • The online application can be completed at FMU’s official website at

Course Eligibility

  • The eligibility criteria for MBA program is a bachelor’s degree in any discipline


Q1: Is Fakir Mohan University accredited by the NAAC?

Ans 1: Yes. Fakir Mohan University is accredited with “B” grade by the NAAC.

Q2: Does FMU offer other distance programs besides the MBA?

Ans: Yes. Fakir Mohan University also offers the Master of Computer Applications (MCA) and Diploma in Computer Applications (PGDCA) through the distance mode.

Fakir Mohan University Contact Details

For further inquiries, applicants can contact FMU through the following channels:

  • Address: Director
  • Directorate of Distance and Continuing Education (DDCE)
  • Fakir Mohan University
  • Vyasa Vihar, Old Campus, Balasore- 756019, Odisha, India
  • Phone: 06782-241840
  • Email: [email protected]
  • Website:,

Link to Prospectus

Jaipur National University Correspondence MBA

Distance MBA is not available at Jaipur National University. Therefore, working professionals who wish to study for an MBA via the distance mode can do so at the following schools:

Sikkim Manipal University (SMU) Distance MBA

  • Introduction: Sikkim Manipal University is an institution of higher learning that gives working professionals the opportunity of studying across a variety of fields via the distance mode.
  • Institution Type: Private
  • Year Established: 1995
  • Types of Courses Offered: MBA
  • Accreditations: SMU is recognized by the UGC
  • Fee: Rs. 20,000

AMITY University Distance MBA

  • Introduction: Amity University is a private institution founded in 1995.
  • Institution Type: Private
  • Year Established: 1995
  • Types of Courses Offered: MBA
  • Accreditations: Amity University is approved by the UGC, ACU, AIU, WASC
  • Fee: Rs. 1,36,800

Utkal University

  • Introduction: Utkal University is an institution of higher learning formed for the purpose of equipping students with the skills and knowledge needed for career advancement.
  • Institution Type: Public
  • Year Established: 1943
  • Types of Courses Offered: MBA
  • Accreditations: Utkal University is approved by the UGC
  • Fee: Rs. 73,960

Suresh Gyan Vihar University (SGVU) Distance MBA

  • Introduction: Suresh Gyan Vihar University is a private university set up in 2008.
  • Institution Type: Private
  • Year Established: 2008
  • Types of Courses Offered: MBA
  • Accreditations: Suresh Gyan Vihar University is approved by the UGC
  • Fee: Rs. 74,400

Symbiosis Distance Centre for Distance Learning (SCDL) MBA

  • Introduction: Symbiosis Centre for Distance Learning is an institution of higher learning founded in 2001.
  • Institution Type: Private
  • Year Established: 2001
  • Types of Courses Offered: PG Diploma
  • Accreditations: The Symbiosis Centre for Distance Learning is recognized by the UGC and DEB
  • Fee:
    • Rs. 37,000 (general students)
    • Rs. 34,000 (active defence/paramilitary/police personnel)
    • Rs. 90,000 (SAARC students)
    • 1,66,000 (international students).

NMIMs Global Access SCE Distance MBA

  • Introduction: NMIMs is a deemed university that seeks to offer superior education via the distance mode.
  • Institution Type: Deemed university
  • Year Established: 1981
  • Types of Courses Offered: MBA
  • Accreditations: NMIMs is approved by the UGC, AIU, NAAC, DEB, NBA
  • Fee: Rs. 68,000

Integral University

  • Introduction: Integral University is a private school established in 2004.
  • Institution Type: Private
  • Year Established: 2004
  • Types of Courses Offered: MBA
  • Accreditations: Integral University is approved by the UGC, AICTE, PCI, NCTE, NAAC, MCI, and DEB.
  • Fee: Rs. 11,800

Mahatma Gandhi Antawashtriya Hindi Vishwavidyalaya (MGAHV)

  • Introduction: MGAHV is a central institution established in 1997.
  • Institution Type: Central University
  • Year Established: 1997
  • Types of Courses Offered: MBA
  • Accreditations: MGAHV is approved by the UGC
  • Fee: Rs. 15,000

NMIMS University

  • Introduction: NMIMS University is deemed university that offers a dynamic curriculum that will help students build the skills needed in today’s business environment.
  • Institution Type: Deemed
  • Year Established: 1981
  • Types of Courses Offered: MBA
  • Accreditations: NMIMS is approved by the UGC
  • Fee: Rs. 68,000

Shivaji University

  • Introduction: Shivaji University is a public institution that offers a challenging and dynamic curriculum designed to help aspiring managers deal decisively with the problems they may encounter.
  • Institution Type: Public
  • Year Established: 1961
  • Types of Courses Offered: MBA
  • Accreditations: Shivaji University is accredited by the UGC and AIU
  • Fee: Rs. 19,975

For more information about popular distance MBA options in India, please visit us at

You might also like to read about Welingkar Distance MBA program



How to Prepare for Class 8 Maths Exams from NCERT Maths Book?



How to Prepare for Class 8 Maths Exams from NCERT Maths Book?

Mathematics is not an easy subject if you are not familiar with the concepts completely. Therefore, for a student, it is immensely important to do maths every day. Regular studies are the first step to scoring well in the exam. However, you can always take additional help from the NCERT maths books.

NCERT Books can be a great help as they offer exercises to solve, easy explanations, ncert maths book class 8 solutions pdf free download, and more. Referring to these books after your child has completed the said chapter in the textbook can help gain a deeper insight. Now, before you start preparing for the exam, it is extremely important to understand the number of chapters present in the syllabus and do them one by one.

Syllabus of Class 8 Maths Exams

Topic:  Rational Numbers


Properties of Rational Numbers

Representing Rational Numbers on the Number Line

Rational Number between Two Rational Numbers

Topic: Linear Equations in One Variable

Introduction to linear equations

Solving Equations

Some Applications

Solving Equations where there are Variable on either side

Some More Applications

Reducing Equations to Simpler Form

Equations Reducible to the Linear Form

Topic: Understanding Quadrilaterals



A few Measures of the Exterior Angles of a Polygon

Kinds of Quadrilaterals

Some Special Parallelograms

Topic: Practical Geometry


Constructing a Quadrilateral

Some Special Cases

Topic: Data Handling

Looking for Information

Organising Data

Grouping Data

Circle Graph or Pie Chart

Chance and Probability

Topic: Squares and Square Roots


Properties of Square Numbers

Some More Interesting Patterns

Finding the Square of a Number

Square Roots

Square Roots of Decimals

Estimating Square Root

Topic: Cubes and Cube Roots



Cubes Roots

Topic: Comparing Quantities

Recalling Ratios and Percentages

Looking for the Increase and Decrease Percent

Finding Discounts

Prices That Are  Related to Buying and Selling i.e.Profit and Loss

Sales Tax/Value Added Tax/Goods and Services Tax

Compound Interest

Deducing a Formula for Compound Interest

Rate Compounded Annually or Half Yearly (Semi-Annually)

Applications of Compound Interest Formula

Topic: Algebraic Expressions and Identities

What are Expressions?

Terms, Factors and Coefficients

Monomials, Binomials and Polynomials

Like and Unlike Terms

Subtraction and Addition of Algebraic Expressions

Introduction to Multiplication of Algebraic Expressions

Multiplying a Monomial by a Monomial

Multiplying a Monomial by a Polynomial

Multiplying a Polynomial by a Polynomial

What is an Identity?

Standard Identities

Applying Identities

Topic: Visualising Solid Shapes


 View of 3D-Shapes

 Mapping Space Around Us

Faces, Edges and Vertices

Topic: Mensuration


Let us Recall

Area of Trapezium

Area of General Quadrilateral

Area of Polygons

Solid Shapes

Surface Area of Cube, Cuboid and Cylinder

The volume of Cube, Cuboid and Cylinder

Volume and Capacity            

Topic: Exponents and Powers


Powers with Negative Exponents

Laws of Exponents

Using Exponents to Express Small Numbers and convert them into Standard Form

Topic: Direct and Inverse Proportions


Direct Proportion

Inverse Proportion

Topic: Playing with Numbers


Numbers in General Form

Game with Numbers

Letters for Digits

Test of Divisibility

Topic: Factorisation


What is Factorisation?

Division of Algebraic Expressions

Division of Algebraic Expressions Continued (Polynomial / Polynomial)

Can you Find the Error?

Topic: Introduction to Graphs


 Linear Graphs

Some Applications

When it comes to mathematics, skipping any chapter is never advised. However, you can make sure your child studies regularly to avoid any issues. Some tips will make studies for exams easier, read ahead to know more.

How To Study From Maths NCERT Books?

Using NCERT books is very helpful because;

  • The language used in the book is very simple, meaning, your child can read it himself.
  • Reputed experts and teachers with vast experience have written the books.
  • NCERT books are a genuine source of information.
  • NCERT books are great for understanding the concepts easily.

Start Chapter-Wise

One of the best ways to make use of NCERT books is to study them chapter-wise. It means, your child studies the chapter in the textbook once and then do the exercises and then move on to the NCERT books. This will ensure your child is completely prepared for the exams. It is important to give equal time to both books to ensure your child is completely prepared. Solving all the problems present in the book means more practice and more knowledge.

Make A Study Schedule

A study schedule is very important when it comes to studying for the exam. Now, you can make separate study schedules for your child- one for the textbook and the other for the NCERT book. Or you can have one study schedule including both. Don’t overburden your child, but make sure you cover all the chapters from both books. When it comes to Mathematics, practice makes perfect.

Focus on Challenging Chapters

Your child needs to concentrate more on difficult topics, but without taking too much time. Maths is a subject where your child can score full marks. Therefore, make sure all chapters are covered.

Sample Question Papers

Solving sample papers is another great way to make sure your child is thorough. It can also help you understand their strengths and weaknesses, and since you will have ample time before the exams, you can work on any weaknesses.

Always Clear The Doubts

If your child has any doubts regarding a concept, make sure it gets cleared immediately. You can either talk to the teacher or hire an online tutor. It is also possible to understand the concept clearly with the help of the NCERT books.

Finally, remember, make sure your child remains calm and panic-free before and during the exam as it will help him remember everything more clearly. Also, try to make learning more fun, with quizzes, interactive games, and more as this will help your child learn better.

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All You Need To Know About Molecular Spectroscopy



All You Need To Know About Molecular Spectroscopy

Spectroscopy involves the investigation and measurement of spectra produced due to matter interacting to give off electromagnetic radiation. In other words, it refers to the dispersion of light into component colours. It involves the interactions between molecules and electromagnetic radiation. Therefore that tells you that molecules oscillate from a lower energy level to a higher energy one and back repeatedly. In the process, the molecules absorb and radiate electromagnetic radiation. Here includes everything you need to learn about Molecular Spectroscopy.

Understand the meaning of a Molecule

A molecule is a group of positively charged atoms surrounded by a considerable number of negatively charged electrons. That explains molecular stability since there is a balance between the attractive and repulsive forces of the negatively charged electrons and positively charged nuclei. Moreover, a molecule is also characterized by the resulting energy that emerges from these interacting forces.

Types of Molecular Spectroscopy

The electromagnetic spectrum of a series of wavelengths is produced whenever a matter is exposed to any electromagnetic radiation. As a result, the Molecules will absorb a certain amount of wavelengths to the vibrational, higher electronic, and rotational energy levels. Therefore, the series of wavelengths a given molecule absorbs gives off a distinct molecular spectrum that lies in the specific region of the electromagnetic spectrum. Here are three types of molecular spectra you should know:

  • Pure Rotational Spectra

Pure rotational spectra occur when a molecule absorbs the least amount of energy that compels it to transit from one rotational level to the next but within the same vibrational level. It is possible to observe rotational spectra using the spectral region of Far Infrared and Microwaves. Moreover, the energies in these types of spectral regions are very small. That’s why they are referred to as all rotational spectra, the microwave spectra.

  • Vibrational Rotational Spectra

Vibrational, rotational spectra occurs when a molecule absorbs sufficient energy that causes the molecule to move from one vibrational level to the next within the same electronic level. However, in this case, both vibrational and rotational transition takes place. That is how you end up with vibrational, rotational spectra. With vibrational spectra, it is easy to observe the spectra in the Near-Infrared Spectral region. That’s called vibrational, rotational spectra, the Infrared spectra.

  • Electronic Band Spectra

Lastly, electronic band spectra happen when the radiation’s exciting energy is large enough to aid the successful transition from one electronic level to the next. Both rotational and vibrational level changes accompany this transition. Additionally, a set of closely spaced lines appear for each vibrational transition. These closely spaced lines are known as bands Because the corresponding rotational level changes.

Molecular Spectroscopy primarily involves the excitation of atoms and molecules using photons. They are excited by either resonant vibrations or electronic transitions based on the induced quantum mechanical changes. While vibrational transitions correspond to changes made within the molecular vibrational states, they typically appear in the infrared region. On the other hand, Electronic transitions specifically correspond to changes in the electronic state of the molecules. Moreover, it often appears in the UV-visible region.

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Difference between an Algorithm and a Model in Machine Learning



Difference between an Algorithm and a Model in Machine Learning

If you are at a crossroads, wondering which data science certification to take for a highly relevant career upgrade, opt for a Machine Learning course at a reputed institute. It teaches you all the essentials of big data analytics and steers your career path to a more focused data scientist job role as a machine learning engineer. 

Before registering for the course, you may like to clear some concepts about machine learning algorithms and models. So here we are, discussing what is an algorithm and a model? What is the difference between the two?

What is Machine Learning

IBM lays down the definition of machine learning: “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” It provides systems with the ability to automatically and iteratively learn and improve from experience. Machine learning is also an important element of data science, where algorithms train to make classifications or predictions, to uncover insights from massive quantities of data.

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What is an Algorithm in Machine Learning

Some definitions of machine learning encapsulate the “algorithm” component of machine learning.

A McKinsey & Co. Insight states that “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.” A University of Washington paper mentions how “Machine learning algorithms figure out how to perform important tasks by generalizing from examples.”

So what is an “algorithm” in machine learning? A machine learning algorithm is a program that provides systems the ability to learn on their own and improve from experience without being explicitly programmed. Machines adjust themselves to perform better as they are exposed to more data. 

What is a Machine Learning Model

A machine learning model is a file that has been trained to recognize certain types of patterns. A set of data is used and provided with an algorithm to reason and learn from the data, and this is called training the model. Once the model is trained with the given set of training data, it can be used to reason with unseen data and make predictions about that data. For example, if you want to build an application that recognizes a user’s emotions based on facial expressions. The model can be trained by providing it with images of faces tagged with a certain emotion and then used in an application that can then recognize any user’s emotion. 

A machine learning model is thus a condensed representation of what a machine learning system has learned.  It is similar to a mathematical function that takes a request as input data, makes a prediction on that input data, and then serves a response. 

The final set of trainable parameters of a model depends on the type of model. All machine learning models are categorized as either supervised or unsupervised. Where the model is a supervised model, it is further sub-categorized as either a regression or classification model. 

Difference between a Machine Learning Algorithm and a Machine Learning Model

An “algorithm” creates a machine learning “model.” A “model” is the output of a machine learning algorithm. The model represents what is learned by the algorithm. The Machine Learning model is “the “thing” that is saved after running an algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.”


Examples of machine learning algorithms are linear regression, decision trees, convolutional neural networks, and reinforced learning. Some examples of machine learning models are Regression Models, Clustering Models, and Dimensionality Reduction Models.

Data set

Machine learning algorithms are executed in the code and run on data. Machine learning models are the output of the algorithms and consist of the model data and a prediction algorithm.

When you train an algorithm with known data, it becomes a model. 

Thus, Model = Training (of an Algorithm + Data)

Mathematical equation

Every algorithm has a mathematical underpinning, which, when executed in a machine, forms a machine learning algorithm. A model is an equation structured by discovering the parameters in the equation of the algorithm. The model specifies which family of functions the learning algorithm can choose from when varying the parameters to reduce the training objective. (Deep learning book, Goodfellow et al., 2016).

Algorithms are methods undertaken to get a task done or solve a problem, while Models are well-defined computations that are a product of an algorithm.

An algorithm takes some value as input and produces some value as output and is thus a sequence of steps with flow and loops, etc., transforming input to output.


An algorithm is an approach you take to solve a problem. The model is what you get when you run the algorithm on the training data and what you further use to make predictions on new data. A new model may be generated using the same algorithm but with different data, and a new model can be generated from the same data using a different algorithm.

Training data vs. new data

An algorithm is implemented in a programming language. An algorithm takes an input set of data and outputs an equation which is a model. Model is the output of the machine learning algorithm.

An algorithm can be used on different sets of training data. A model is then used as the deployment vehicle, which can take any unseen data in the future and produce an output prediction. That model has both data and a procedure for how to use the training data to make a prediction on new data, almost like a prediction algorithm.

Storing the entire dataset

Models are always triggered by the algorithm but not always dependent on the data. Based on the purpose that your model serves, some models like the k-nearest neighbors store the entire dataset, which acts as the prediction algorithm. 


The algorithm behind the model matters most, as it is important to know which algorithm to apply to your model to yield the best predictions and right results. If the algorithm used is right, you will likely get a good model that works well on new data sets. However, a robust algorithm does not always yield a good model, as a machine learning model strategy depends on various factors other than the algorithm.

Ultimately, an algorithm is a few lines of code that you implement after much deliberation, while a perfectly working model is dependent on many other factors other than the algorithm or the training data.


Ultimately, a machine learning engineer works with big data to execute algorithms and build models for predictions. And a good way to kick-start a career in machine learning is to take a certification.

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