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Day: March 9, 2022

Diploma in finance management: Correlation and Diversification

Posted on by Imarticus Learning
Reading Time: 2 minutes

Are you interested in banking courses and want to specialize in a subject that focuses on the financial management system at the organizational level? Then a Diploma in Financial Management might be the perfect way to your dream job. 

Pupils who excel in commerce and planning the organizational budget structure can choose this Financial Risk Management course. Imarticus Learning has come up with a Post Graduate Diploma in Management with a specialization choice in FinTech and Artificial Intelligence & Data Science.

The course is well-structured with subjects like managing the reporting system and financial accounting of a company, calculation, and evaluation for cost reduction chances. This article will help you get a comprehensive understanding of Financial Management and the use of Correlation and Diversification in finance.

What is Finance Management?

Financial management consists of strategic organizing, controlling, planning, and directing financial undertakings in any business entity or organization. It is also used in fiscal management and involves management principles to the financial assets of any business. Some of the principal objectives of financial management are: 

  • Preserving enough supply of funds 
  • Guaranteeing shareholders get satisfactory returns on their investments
  • Optimize the utilization of company funds
  • Creating safe investment opportunities for shareholders to invest

Advantages of a Diploma in Finance Management

There are many advantages of doing a certified Diploma course in Finance Management. Some of the major advantages are: 

  • It will open new avenues for you, like financial planning services, investment banking, insurance companies, brokerage firms, etc.

  • It will create a solid foundation for other educational opportunities and job-specific training programs.

  • It will give you practical knowledge that will be a headstart when you start as a sole trader or begin your own small business.

  • It will give you a better understanding of finance and help you grasp complex financial concepts.

Correlation

Correlation is a statistic that gauges the extent to which two variables move concerning each other. In the financial world, correlation is used to measure how stocks move with respect to a benchmark index. However, it can only show the association between two securities, but not the reason behind the association.

Correlations are used in the finance and investment industries to create advanced portfolio management where they are computed as correlation coefficients. These coefficients must have a value between -1.0 and +1.0.

Analysts, traders, and investment managers compute the correlation between various financial securities to find an estimate of the risk reduction benefits of diversification. They mainly rely on modern software and financial spreadsheets to calculate the correlation value.

Diversification

The very rudimentary step of investing is not to put all your money in one type of investment. You should plan and spread your money among different investment vehicles to limit the risk of facing loss. This strategy of risk management is known as diversification.

A diversified portfolio will consist of mixed asset types to reduce risk or exposure to any single asset type. The rationale behind this strategy is that a diversified portfolio will diminish the risk of any individual holding and yield higher long-term returns. On average, the positive performance of some areas of the portfolio will outweigh the negative performance of others. 

Conclusion

Financial management helps business organizations in their financial planning and assists them in the acquisition, allocation, and utility of company funds. It helps them in making critical financial decisions and generating higher revenues. Take the Financial Services and Capital Markets course of Imarticus Learning for diverse career opportunities in the field of finance. 

2021 in review: Principles of successful supply chain planning

Posted on by Imarticus Learning
Reading Time: 2 minutes

2021 in Review: Principles of Successful Supply Chain Planning

The supply chain is one of the most important aspects of any business. Keeping up with the market’s ever-changing demands is quite challenging, but you can stay ahead of the competition with the correct planning principles. 

This blog post will examine the essential principles of successful supply chain planning and discuss how they can help your business succeed in 2022 and beyond!

What are the principles of supply chain planning?

The key to supply chain planning is having a solid knowledge base about your industry and its needs. It includes understanding how products move through different stages from production to consumption, what consumers want or need to purchase goods, and where they will sell those products. 

With this information, you can create strategies to help your business achieve its goals while meeting customer demands regarding quality and cost-effectiveness.

The principles of supply chain planning include:

  • Forecasting – This helps estimate the demand for a product over time based on historical data or current trends within an industry. Forecasts indicate future demand levels and help businesses plan their operations accordingly.
  • Resource Analysis- This determines the resources necessary for production processes and other supply chain-related activities. These may include raw materials, labor costs, or even labor hours per employee per day.
  • Production Planning – This creates a production schedule that meets customer demand while efficiently utilizing resources. It also includes setting up processes and protocols for quality control.
  • Distribution and Transportation Planning – This determines how you will transport products to meet consumer demand, taking into factors such as delivery time, distance, and the availability of resources. It also includes creating a distribution network that will optimize efficiency.

 

  • Inventory Management – This ensures that the correct inventory level gets maintained without tying up excessive capital in stock. It can also help reduce the cost of merchandise while maximizing customer satisfaction.

 How to apply these principles in your own business?

There are many ways to apply these principles to your own business. For example, by using data from previous years and looking at current trends, companies can make better decisions about future demand for their products or services. By understanding resource availability, businesses can plan production schedules accordingly to avoid running out of stock before consumers need it.

Additionally, by using inventory management techniques, companies can ensure that they are not overstocking or understocking products and lose out on potential sales.

Supply chain principles are essential for any business looking to succeed in the ever-changing marketplace. 

Explore Supply Chain Management Course with Imarticus Learning

This Professional Certification in Supply Chain Management and Analytics equips candidates interested in entering the operations or supply chain analytics course with cutting-edge experience. This supply chain analytics course will prepare students for jobs such as Demand Planner, Data Scientist, Supply Planner, and Supply and Operations Planner, which are in great demand.

 

Course Benefits For Learners:

  • We immerse students in in-demand technologies and projects, including six real-world projects that will give them a complete grasp of the industry to prepare them for a satisfying future in Supply Chain Management and Analytics.
  • Students who complete a supply chain management online course will prepare for competitive careers such as Demand Planner or Data Scientist, which are in great demand among companies today!
  • This supply chain management and analytics training course will help students become more data-centric and improve Supply Chain decision-making by harnessing the power of Python.

Contact us through chat support, or drive to one of our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Top 5 machine learning skills desired by employers

Posted on by Imarticus Learning
Reading Time: 3 minutes

AIML or Artificial Intelligence and Machine Learning are some of the leading subject matters in the tech industry. ML is a branch of AI and has a wide range of applications in our daily lives, ranging from traffic predictions, face and voice recognition, product recommendations, virtual personal assistant, fraud detection, automatic language translation, and many more.

Therefore, big ventures like Google and Facebook are exclusively employing AIML in their products and services. Machine learning is the process by which computer scientists and engineers attempt to impart intelligent behavior into machines, to make them think and respond like human-mind in real-time situations.

For example, Google Assistant, Cortana, and Siri are entirely powered by machine learning algorithms that recognize speech.

AIML works in a complex way to make predictions and decisions based on past data, eventually refining its accuracy. A machine learning course can definitely help someone study and get training in machine learning data and algorithms.

What are the top Machine Learning Skills?

To get a desirable job related to machine learning – data engineer, machine learning engineer or machine learning scientist – you need to have knowledge and training in both software engineering and data science.

Following are the top 5 machine learning skills desired by employers:

  1. Computer Science Fundamentals and Programming

If you are getting into a technical world, then you need to have knowledge of CS fundamentals like data structures (graphs, stacks, queues, etc), algorithms (optimizing, dynamic programming, etc), computability, and complexity (NP problems, P vs NP, etc).

Having experience in different programming languages, like Python and Java, will make it easy for you to implement these fundamentals for better results.

  1. Applied Mathematics

Within applied mathematics, probability and statistics go hand in hand. Many machine learning algorithms employ probability and its techniques, like Markov Decision Process and Bayes Net, to approach uncertainties and deal with them.

You should also be well-versed in statistics to be able to build algorithms from observed data through the application of various measures, analysis methods, and distributions.

  1. Data Modelling and Evaluation

Data modeling is the process of understanding the underlying structure of a dataset, in order to find complex patterns. Furthermore, you will have to evaluate the data to be able to choose an effective accuracy/error measure like regression, clustering, and classification.

The kind of evaluation strategy that you will apply, whether it’s training-testing split or sequential vs randomized cross-validation, depends on your knowledge of data modeling and its different measures.

  1. Machine Learning Algorithms

ML algorithms are broadly characterized into three categories – supervised, unsupervised and reinforcement machine learning algorithms. You can effectively choose a machine learning algorithm if you are aware of the learning procedures and hyperparameters that affect the learning.

Some of the common algorithms are K Means Clustering, Naïve Bayes Classifier, Support Vector Machine and Linear Regression. Having appropriate knowledge of the advantages and disadvantages of these algorithms is essential to machine learning.

  1. Natural Language Processing (NLP)

NLP is the bedrock of machine learning. It is a learning model through which a computer is made to understand and interpret the human language. Many libraries across the world provide the foundation of NLP and help computers understand human language by decoding the text or speech according to its syntax.

Natural Language Toolkit is one of the most popular libraries to build NLP applications. Without the basic skill of using NLP, it can become fairly difficult to get into machine learning.

 Conclusion

All these skills come under one roof with the Artificial Intelligence and Machine Learning course offered by Imarticus. A PG in Data Analytics and Machine Learning will definitely polish these top skills and help you understand related concepts such as Deep Learning and Artificial Neural networks.