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Category: Analytics

A complete guide to data analyst training for beginners

Posted on by Imarticus Learning

Nowadays, it is said that data drives the world. The organization with more refined data has a higher probability of making a better prediction on future business and its processes on optimization. Data being important to an institution, it is quite obvious that the analysis of such data may only lead to those future predictions, besides, of course, correcting the present-day business.

Thus, in present-day India, huge opportunities have been created for individuals who want to work on data management and analysis. This career is meant for candidates who missed out on pursuing a software engineering course but have a tremendous interest in computer software activities. A data analytics course is the best option for them to fulfil their ambition. Prospective aspirants may be undergraduates from any domain. However, it is important to understand that before choosing the course, students must also have a reasonable understanding of mathematics and statistics with basic to medium working-level know-how in Excel and PowerPoint.   

Tools for Data Analysis 

The prerequisites of learning data analytics have been mentioned above. Now let us understand the working tools of the subject. We are aware that the job of a Data Analyst is to gather, filter, segregate, clean and finally analyse data meaningfully for business owners so that they take important decisions. There are certain skill sets that the candidate has to acquire through Data Analyst training. Advanced Excel, SQL and Programming Languages like R, Python etc., are the basic skill sets that a professional in this field must possess. The above know-how shall help them to analyse the data fruitfully or even extrapolate them. However, next comes the process of visualization of the big picture.

A Data Analyst must present their findings to the business team so that they can understand the analysis as a whole. In order to make such visualization simple and user-friendly, the Data Analyst must learn tools like Power BI or Tableau.

Apart from the mentioned points, an aspiring Data Analyst must complete a few meaningful projects post completing the course. Executing projects will not only give the candidate confidence in the subject but also add immense value to their CV.  

Data Analysis Training Courses

data analytics course

There are several training courses on Data Analysis in India. Some of these courses are offline, while others are online. Since these are essentially certificate courses, the duration and the content for each also vary greatly.

However, a good Data Analyst training course will essentially cover topics on pre-processing the data, their classification, segregation and filtration. This also includes text mining and social media or site analysis. Understanding of data structures should be covered. Advanced Excel, SQL and programming languages like R or Python should be within its syllabus. The course must teach data visualization tools.

Thus, the selection of the right data analytics course is of extreme importance. A well-designed course with better content, well aligned with the right purpose, helps the aspiring candidate to learn systematically and quickly. A good mentor on the subject is also a must for a beginner. The importance of choosing the right course to build a career is all the more important since a candidate has to learn a whole gamut of skill sets of different dimensions and also their application. Since the job role involves an efficient presentation to business owners, soft skills also form a major learning topic. Students who aspire to enter the field of data analysis must understand all these parameters and introspect carefully before moving ahead. Technical skills and logical understanding are key deciding parameters for a recruitment process in this field. 

Besides, the study material should also contain statistical analysis and interferences, exploratory data analysis and basics of modelling, which will further help the candidate to complete projects successfully. 

Conclusion

There is a huge boom of information in today’s world and a matching demand in the field of data analysis. The job opportunities for a Data Analyst are vastly increasing. With proper training and future application, a candidate may even aspire to become a Business Analyst or an Operation Analyst. With the further enhancement of knowledge on subjects like artificial intelligence and machine learning, a present-day Data Analyst may also aspire to become a Data Scientist. 

An interested candidate can learn data analytics from the postgraduate program in data science and analytics course taught at Imarticus. The duration of the course is 6 months, and it is one of the best courses in India for learning data analytics. This is a placement-assured program with 1500+ candidates having already secured a position in reputed companies. This course offers both online as well as offline modes of teaching. This course will guide you towards your goal and help you become a successful data analyst. 

How learning data analysis can impact your career?

Posted on by Imarticus Learning

Data analysis holds the key to the future of most businesses. Earlier, the resultant data of any transaction was either not captured at all or, at best, recorded manually. Such details would be usually generated and preserved in excel sheets and used by an organisation to make data-driven decisions. However, such manual practices are unimaginable now. The main reason is the massive volume of data, often referred to in this domain as big data. A career in data analytics, thus, is in huge demand. 

Nowadays, every organisation is digitally evolving. Business houses are using digital platforms to gather substantial feedback from their clients. This model may be best understood when someone chooses a product or service from an E-commerce platform. Past client reviews and earlier product ratings often guide the choice. The manufacturer or the dealer also gets feedback on the platform from the client and understands the product’s sustainability and demand in the market.    

Role of A Data Analyst 

data analytics course

The function of a Data Analyst begins with recognising and gathering raw data. The next roles include cleaning or filtering the available raw data. The data is then worked upon using tools like advanced excel, statistics, and programming languages such as Python & R, etc., and presented in the form of easily understandable visuals like bar or pie charts. Thus, the knowledge of Power BI and Tableau is equally important. All these tasks of a Data Analyst are directed towards assisting or supporting the management of a business house to make a path-breaking and futuristic decision. This would pave the way for a targeted top line in the business with desired profit at the end of the financial year. Besides these, data analysis also helps the company to locate the present, and future potential of a said business, irrespective of whether it is a product or a service. The process can also predict whether a product should be discontinued from the market or a new product should be launched soon. 

How to Become A Successful Data Analyst?

Data analysis may be taken up as a career by individuals who don’t have a Bachelor’s degree in Software Engineering. However, it should be remembered that aspiring candidates who want to learn data analysis must have a strong inclination towards the subject with an aptitude to learn and grasp complex topics in the subject domain. Since this job involves handling different algorithms and programming types, knowledge of languages like C++, Java, R, and Python becomes essential. Candidates don’t need to learn all the languages simultaneously. However, starting with R & Python might prove to be helpful. Over time, the candidate is free to master other useful languages to secure a better understanding of the domain. 

Apart from the hard-core technical knowledge of the profession, knowing about the services offered by the organisation or the product manufactured is desirable. The more the candidate develops this knowledge, the better the aspirant may analyse the data. The accuracy of prediction or extrapolation of data will also have far-reaching effects. 

A successful Data Analyst should have both an innovative and inquisitive mind. He should develop a habit of taking notes from different research papers on the subject and from videos.

Along with all these, candidates aspiring to become Data Analysts or existing data analysts must create a community of people with similar educational backgrounds and common professional interests. Within the circle, they can discuss good practices and try to absorb them in their daily activities. Listening to success stories from professionals from a different background other than software engineering will also boost the morale of aspiring job seekers in this domain.

Conclusion

The job market for a Data Analyst is debatably growing at the fastest rate as per industry standards and shall have a very high demand in days to come. Every organisation is now relying on data as its prime mover. Whether it is an overall prediction for a business market or a key decision regarding an upcoming or an existing product or service, data analysis is the only modern solution in today’s world. With an advanced analytical mind and a strong command over the business domain, young Data Analysts may aspire to become Data Scientists someday. 

A prospective candidate can learn data analytics from the postgraduate program in data science and analytics course taught at Imarticus. It is one of the best data analytics courses in India, taught over a span of 6 months. This course will help you to achieve your dream. Head to the website to learn more about the course.

The Role of Python, SQL and R in Data Analysis

Posted on by Imarticus Learning

A career in data science requires in-depth knowledge of various software tools and programming languages. Languages like R, Python and SQL offer massive advantages that one can use for efficiently conducting data analysis. 

This article will discuss in detail the role each language carries while we delve into the advantages of a data science programme. For instance, the programmer can utilise R and SQL for complex queries and tables while conducting fundamental statistics. On the other hand, one can easily use Python objects to create and automate tasks while working on various data sets. 

Python in Data Analysis 

become a Data Analyst

Python is a powerful tool in data analysis as it provides a colossal library count that can be used for data visualisation and manipulation. 

A. Advantages of Python in Data Analysis 

  • Easy Programming Language: Data analysis is a large venture requiring a lot of work with every task. With Python objects, syntax and code can be written straightforwardly.
  • Extensive Library Count: Python’s standard library allows for complex data analysis tasks like data manipulation, statistics, and data visualisation to be done with ease.
  • Open-source Feature: Since Python can be modified, it is free for users to access and write, making it the ideal choice for different data analysis projects.
  • Community Support: Python boasts a large group of developers available anytime to answer and help each other with queries.

B. Popular Libraries and Frameworks Used in Data Analysis

Python utilises many libraries like TensorFlow and Scikit-learn to exercise machine learning algorithms. Other libraries and frameworks include Keras, Pandas, PyTorch and Matplotlib.

Pandas uses the ‘sort_values‘ function to make way for the action. In this example, we can see the popular instance of listing items and their prices arranged in descending order in store 1. The prerequisite ‘Pandas’ from the Python library has been used for this particular action.

items[items.store_id == 1][[‘description’,’price’]]\

.sort_values(by=’price’, ascending=False)

 

Store ID

 Description

Price

2

 zucchini 7.45

1

 orange

1.45

3

 pear

1.45

1

 butter

1.40

8

 onion

1.35
1 celery

0.75

 

SQL in Data Analysis 

A. Advantages of SQL in Data Analysis 

SQL’s performance is commendable as it can be used to query and manipulate the data present in the database. SQL can also create numerous reports and dashboards for visualising data. 

  • High Performance: SQL is widely known for its efficient form that aids in offering faster results than other programming languages.
  • Secure Database: SQL’s most relevant feature is high security for storing and retrieving data which can be used against unauthorised access and malicious attacks.
  • Scalability: SQL is known for holding a substantial amount of databases that can store more data over time.

B. Popular SQL Programming Database Management Systems Used in Data Analysis

SQL utilised a range of DMS systems for analysing data effectively. These include MySQL, PostgreSQL, MSSQL, MariaDB and Oracle.

This list shows how MySQL has been implemented for acting:

mysql> SELECT DESCRIPTION, PRICE

    -> FROM ITEMS

    -> WHERE STORE_ID = 1

    -> ORDER BY PRICE DESC;

 

Description

 Price

zucchini

 7.45
orange

1.45

pear

 1.45
butter

1.40
onion

1.35

celery

0.75

 

R in Data Analysis 

R is an essential language for data analysts as it helps create robust data structures and visualisations.

A. Advantages of R in Data Analysis 

  • Cost-effective Features: R’s libraries and frameworks are open-source and free to use, making them an excellent option for greater accessibility in data analysis.
  • User-friendly Visualisation Tools: R comprises various user-friendly visualisation tools that rapidly form graphs and charts.
  • Flexibility: R’s vivid tools can analyse many data types — text, audio and images.

B. Popular R Libraries and Packages Used in Data Analysis

The most common and widely used R frameworks for analysing data include dplyr, tidyr, Shiny, plotly, XGBoost and data.table.

In this example, you can see the data.table format:

> items[store_id == 1, .(description, price)][order(-price)]

 

Store ID

 Description Price

1

 zucchini 7.45
2 orange

1.45

3

 pear 1.45

4

butter

1.40
5 onion

1.35
6 celery

0.75

 

Conclusion 

All three languages mentioned above have a substantial role in data analysis as they offer numerous functions for managing and manipulating data effectively. While R is a powerful statistical language, SQL programming is a database query for storing databases. Furthermore, Python’s general-purpose language can be accessed for machine learning purposes.

A career in data science can be gratifying, especially when using your technical skill sets. It can be especially beneficial while forming simple descriptive statistics or creating complex machine learning models. Opt for Imarticus’s Post graduate program in Data Science and Analytics while you work on amplifying your resume. 

6 Chief Operating Officer Roles and Responsibilities

Posted on by Imarticus Learning

In today’s fast-paced business landscape, a COO holds utmost significance in any organisation. They stand to be more demanding than ever. A Chief Operating Officer (COO) ensures that an organisation operates effectively by running its operations, simplifying supply chains, and implementing strategic plans. According to research, Opportunities for Strategic COOs exist globally, with major financial hubs such as New York, London, Singapore, and Hong Kong offering numerous positions.

This blog explores the key responsibilities of a COO in the domains of supply chain and operations. We’ll also discuss how advanced training programs, such as the IIM Raipur Executive Certificate Programme, can help aspiring COOs excel in their careers.

What Does a Chief Operating Officer Do?

The Chief Operating Officer is one of the key positions in any organisation. As an implementor of a business strategy, the COO needs to ensure that operations are efficient while still driving organisational growth. Deep knowledge in supply chain and operations management with leadership abilities must empower teams to work toward attainment of corporate objectives.

Why does the role of a COO matter?

  • Operational Excellence: The COO will ensure that the organisation performs at an optimal level.
  • Strategic Alignment: The COO will enable the CEO by creating actionable goals that bring a shared vision to life.
  • Leadership: The COO will create innovation and collaboration culture across teams.

Key Chief Operating Officer Roles and Responsibilities

  1. Propelling Operational Strategy

A Chief Operating Officer would formulate strategies which might be useful in optimising the company’s operational performance and work toward their implementation. They would work with other executives to establish the organisation’s performance metrics and align its operations to streamline the work.

Focus Areas:

  • Process optimisation
  • Optimisation of resource use
  • Reduce risk 

  1. Manage Supply Chain

The most important supply chain function that falls under the COO role is the smooth movement of goods and services. It includes all procurement, logistics, and inventory management.

Focus Areas:

  • Vendor management
  • Inventory optimisation
  • Cost-cutting initiatives

  1. Improving Operational Effectiveness

Efficiency lies at the core of the COO roles in operations management. COOs detect bottlenecks and work on solutions to make operations more productive and minimise costs.

Focus Areas:

  • Workflow automation
  • Teamwork
  • Performance metrics

  1. Leading Technology Integration

In the modern digital landscape, the COOs need to be leaders for integrating technology into operations. This would translate into data-driven tools for supply chain and operational analytics.

Focus Areas:

  • Digital transformation
  • Supply chain analytics
  • Artificial intelligence and machine learning

  1. Cross-Functional Teams Management

A COO is a department interface. It helps ensure that teams work towards organisational goals. Communication and conflict resolution skills are important here.

Focus Areas:

  • Team alignment
  • Leadership development
  • Stakeholder engagement

  1. Compliance and risk management

The key responsibilities of a COO includes ensuring that operations are adhered to regulatory standards and maintaining risk proactively. 

Focus Areas:

  • Compliance with regulatory standards
  • Risk analysis
  • Crisis management

Importance of Training for Chief Operating Officers

The dynamics in the business world are changing, and so is the need for COOs to be responsive to trends. Chief Operating Officers must balance strategic decision-making with operational efficiency with leading diverse teams. Effective training equips COOs with the tools, skills and mindset needed to navigate this complexity and drive organisational success. Professional training is indispensable for Chief Operating Officers.

IIM Raipur Executive Certificate Programme for Strategic Chief Operations Officers

Tailormade C-suite Training

This 10-month program covers fully the scope of the tasks of the COO responsibilities in supply chain and operations management and it is the best supply chain and operations training program. The learners get a strategic view on the design and development of the supply chain.

SCM Globe Simulations

The learners in this course get a close-up exposure to supply chain modeling and decision-making experience. They gain a comprehensive understanding of supply chain design, development, and management.

CXO Masterclasses

Learn from seasoned CXOs who share their expertise on leadership, strategy, and problem-solving with you. These masterclasses are essentially practical tools to navigate through the complexities of the COO role.

Campus Immersion

The programme includes campus immersion experience at IIM Raipur. It allows participants to network with their peer group and distinguished faculty members.

IIM Raipur Executive Alumni Status

The graduating candidates are also given the chance of taking the executive alumni status at IIM Raipur that offers privileged access to exclusive resources and other events and networking activities similar to this

Building Your Social Net Worth

The course will give a person-to-person connection to industrial leaderships and peers from a variety of backgrounds where the attendees can build their networking.

FAQs

What is the core responsibility of a Chief Operating Officer?

A COO is at the helm of operational strategy, supply chain management, efficiency, incorporation of technology, management of teams, and risk avoidance.

How do COOs improve supply chain management?

COOs make procurement, logistics, and inventory processes streamlined and cost-efficient.

What are the skills that a COO needs?

Key competencies include strategic thinking, leadership, operational management, technological prowess, and risk assessment.

Why should I take up a training program for COOs?

The training course teaches one all the knowledge and experience needed to work well in this challenging job. It provides a healthy enhancement level in one’s career, as well as elevates leadership skills

How does the IIM Raipur Executive Certificate prepare one to become a COO?

The IIM Raipur Executive Certificate Programme integrates strategic knowledge with hand-on learning and networking experience to empower professionals for COO.

Conclusion

Chief Operating Officer is a much-needed role in the corporate world today. COOs are now playing a very critical role in the organisational setup from managing supply chains to driving operational excellence. For this role and to shine with excellence, the right stepping stone is taking specialised training courses such as the IIM Raipur Executive Certificate Programme for Strategic Chief Operations Officers. The program’s curriculum, including world-class faculty and unmatched networking opportunities, will help develop tomorrow’s leaders. Be challenged by playing the role of a COO and be that strategic leader your organisation needs. The future of operations management awaits!

Top data science and ML challenges in 2023

Posted on by Imarticus Learning

Data science and machine learning (ML) have become key determinants of business success. While data science deals with collecting, analysing and drawing meaning from data, machine learning focuses on building models that use data to make informed predictions. Data science involves various fields and techniques, including machine learning. Data scientists use ML models to improve data analysis and forecasts. 

Data Science Course

Data science and machine learning courses have become increasingly popular, with the demand for skilled professionals rising. In addition to having the relevant knowledge and skills, data scientists and ML experts must be quick to identify challenges and tackle them. 

This article will look at the top data science and ML challenges and how professionals can deal with them.

What are the major challenges faced in this field?

Let’s discuss some significant challenges data science and ML professionals face.

Data preparation

Collecting, organising, cleaning and analysing data is extremely tedious. Different platforms require the data to be stored in specific formats using various codes. One has to keep in mind that there should be no change in the original dataset while the analysis is being carried out. This is a major data science challenge.

Lack of appropriate data

The unavailability of proper datasets can often turn out to be problematic. Too small a dataset can result in sampling bias. To predict future performances based on past information, efficient datasets are necessary, and the inability to extract such data can often become a challenge. 

Incomplete dataset

Complete and balanced data is necessary to build machine learning models,  However, if an incomplete dataset is used, it might lead to inaccurate predictions and erroneous conclusions.

Missing values

If a dataset has a lot of missing values, then it becomes difficult to work with the data since many programming languages fail to give accurate results in this case. A non-stationary dataset might pose a challenge since it becomes complex to work with.

Data protection

The threat of cyber-attacks calls for secure data storage to prevent the leakage of sensitive information. Due to some organisations’ stringent data protection measures, accessing it becomes difficult for data scientists. Even after accessing, working on this data while conforming to these additional restrictions often becomes challenging for them. 

Data inaccuracy

If a model has been built with incorrectly labelled data, then it will certainly give incorrect results once new information has been incorporated. Therefore, ensuring the accuracy of results using proper data labels and variable types often proves quite daunting.

Data inconsistency

Consistent data is a must to build an appropriate machine learning model. Any inconsistency in the data can lead to false conclusions. Thus, the data should be free from bias and there should be no inaccurate data sources when building ML models. 

How can these challenges be tackled?

Several measures can be taken to tackle the challenges that have been discussed above:

  • Setting a definite target 

Setting the primary purpose behind the data collection and analysis is essential as it will help to make the process more precise and focussed. Once the research question has been defined, it becomes easier to carry out data operations and derive insights.

  • Cleaning the data to minimise errors

While cleaning the data, it is essential to reduce errors as much as possible, omit missing values or substitute them with other appropriate values and eliminate duplicate observations. It is also vital to detect unnecessary trends and anomalies in the dataset. 

  • Checking the linearity of data

It is crucial to check for non-linear relationships in the collected data and make them linear if needed. Checking data linearity will provide information on whether the data is sufficient or if some more variables need to be included.

  • Efficiently managing data

Efficient data management and integration tools must be utilised to ensure the availability of appropriate data required for the study. Data must be collected from reliable sources and appropriately sorted.

  • Implementing data governance

Data management and model governance processes must be set up to improve model performance, precision and accuracy. If required, regular model re-training is a must by setting up relevant tools and processes. 

Conclusion 

There are many challenges one might encounter in this field. However, it does not deter aspirants from pursuing data science and machine learning courses to join this thriving industry. In addition to imparting theoretical knowledge, these courses encourage hands-on experience working with various tools necessary to tackle these challenges successfully.

 If you are interested in data science and machine learning, then check out the Imarticus IIT Roorkee data science course. The 5-month certificate programme in data science and machine learning is designed by eminent IIT faculty members. It will teach you the fundamentals of data science and machine learning while training you to apply this knowledge to real-world problems. 

How to Become A Supply Chain Professional: A Guide

Posted on by Imarticus Learning

A supply chain is a system of facilities and transportation channels. It coordinates the purchase of raw materials, their transformation into intermediate and completed goods, and their distribution to the end customers. 

Notably, supply chain management (SCM) is the process of overseeing the flow of products and services – from sourcing raw materials to final products.

As businesses have become more globalised and competitive, the demand for supply chain management professionals has also shot up. 

To become supply chain professionals, aspirants must have specific skills and relevant educational qualifications. This article will address everything you need to know about becoming a supply chain professional. 

What is supply chain management?

At a fundamental level, supply chain management controls the flow of information, materials, and finance for a product or service. It takes care of everything – from purchasing raw materials to delivering the final product to its destination.

The concepts of supply chain and logistics sometimes need clarification. Logistics is a subpart of the supply chain. 

best supply chain management and analytics training

SCM systems nowadays are digitally created and encompass the handling of materials and software. This happens for all parties involved in creating products and services.

Getting the material, supply chain planning, product lifecycle management, logistics, and order management are all integral steps of a supply chain.

Whether strategic or tactical, material procurement falls under the responsibility of supply chain managers. They keep the inventory under control by keeping track of material availability and when it will be used. Additionally, they plan shipping routes and merge cargo whenever feasible.

Responsibilities of a supply chain manager

A supply chain manager has a wide range of responsibilities. Some of them are listed below:

  • Design the supply chain strategy for the company
  • To spot problems, analyse data from shipping and delivery
  • Keep an eye on logistics to make sure everything goes well
  • Proper upkeep of the records and inventory
  • Find reasonable solutions for supply chain problems
  • Fix any issues that arise (such as delays during delivery or accidents)
  • Oversee and encourage a group of supply chain employees
  • Enhance the effectiveness of the supply chain overall and search for any potential process innovations
  • Build connections with manufacturers, both new and old
  • To achieve sustainability goals, consider the supply chain’s environmental impact

Steps to follow to become a supply chain professional 

There are three ways in which you can become a supply chain professional. They are as follows:

Path 1:

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a BBA degree 
  • Step 3 – Pursue an MBA in logistics and supply chain 

Path 2:

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a B.Tech or B.E. 
  • Step 3 – Pursue an MBA in logistics 

Path 3: 

  • Step 1 – Complete your 10+2 level education from any recognised board 
  • Step 2 – Pursue a bachelor’s degree in any relevant field 
  • Step 3 – Get certification in supply chain management

Salary of a supply chain professional 

The average salary of a supply chain manager is Rs 13.1 LPA. It ranges between Rs 4.4 LPA and Rs 25 LPA depending on your experience, position, company and workplace. 

Skills necessary for a supply chain professional 

To become a successful supply chain professional, you must work on your skill set and education. Here are some skills and qualities a supply chain professional must have: 

  • Strong communication abilities

These professionals collaborate with several divisions in their organisation and suppliers to get a product manufactured and delivered to customers. Therefore, supply chain specialists must be effective communicators to promote collaboration and cooperation.

  • A strong moral sense

A supply chain professional, like any other business leader, must have a solid moral compass and be able to demonstrate it to peers and business partners. They must be fair to all participants in the supply chain.

  • Dedication to excellence

Customers will not do business with any company that produces inferior quality goods. A supply chain expert must hold his business to the highest standards to ensure success.

  • Time management

Every employee must be able to do tasks quickly and supply chain professionals are no exception. Anyone who works in the supply chain has a full day of commitments. Therefore, they must be skilled at balancing them all.

  • Tech expertise

The supply chain has been dramatically shaped by technology. Professionals in the supply chain must stay up-to-date on new advancements and be prepared to discuss how they could benefit the organisation.

Conclusion 

A professional overseeing the entire supply chain of a company, from raw material sourcing to the final production, is known as a supply chain manager. An essential part of business success, supply chain professionals ensure the continuity of delivery pipelines to meet market demand and enhance sustainability. 

To become a supply chain professional, consider enrolling in the Chief Supply Chain Management course offered by Imarticus in collaboration with IIM Raipur. The 10-month executive certificate programme incorporates live online classes and campus immersion to train professionals for senior supply chain management roles.

How big data Analytics can help in improving Network Security

Posted on by Imarticus Learning

Every day, the information generated and stored by companies worldwide is increasing in volume and complexity. With this arises a need for tools to protect this vulnerable data from data breaches and exploitation by cybercriminals.

Can big data analytics strengthen network security and save the day? Read on to find out.

What is big data analytics?

Big data refers to highly variable, complex data generated at a high volume and high velocity. The analysis and interpretation of big data are known as big data analytics.

With the boom in digitalisation and cloud computing, hundreds of terabytes of data are generated and utilised daily. Extracting useful information from data of this size is tedious with traditional SQL (Structured Query Language) tools. Here’s where big data analytics comes in. 

Frameworks like Hadoop and databases such as MongoDB, Cassandra, Vertica, and Apache HBase make complex data management quicker and simpler. Searching for specific events within large datasets can be completed by such frameworks in minutes.

Applications of big data analytics

Big Data Analytics Courses

Big data analytics helps store and maintain data, detect patterns and trends in real-time, predict future events or problems, and nullify abnormal or malicious activities in vast networks.

Some sectors where big data analytics finds wide applications are:

  • Finance and banking
  • Business and e-commerce
  • Public welfare and governance
  • Healthcare 
  • Communication
  • Energy
  • Entertainment and media

Network security and its challenges

Protocols, policies, and practices designed to keep data safe from cyber attacks and security breaches are part of network security. It aims to detect, prevent and nullify misuse, denial of access, unauthorised modification, or data extraction.

Network security uses physical and digital methods to secure confidential and sensitive data. Biometrics, firewalls, VPNs, and sandboxing are various types of network security. 

As databases expand, monitoring big data with traditional SQL tools becomes unreliable. Downsides of traditional analytics include frequent false positives, cyber attacks, privacy breaches, phishing, malware, and other network security threats.

Other challenges faced by network security are:

  • Breaches at admin or high clearance levels
  • Lack of vigilant monitoring in open network structures
  • Inability to process high amounts of data in real-time
  • Deliberate or accidental breaches by humans or bots
  • Stealing confidential data through hacking or identity theft

How big data analytics can be the solution?

Storage and maintenance of big data

High-volume non-relational data can be collected and stored on NoSQL databases such as Apache HBase, Cassandra, and MongoDB. These databases use platforms like Hadoop to organise variable and unstructured data.

Identification of anomalous patterns in real-time

Employees are responsible for a majority of network security breaches. These leaks may be accidental, but deliberate breaches may be minor and go undetected for long periods.

Big data analytics can monitor real-time changes in regular data flow patterns. Data from sensors monitoring user credentials can detect unauthorised log-ins and reveal botnets or APTs (Advanced Persistent Threats).

Predictive assessment of network integrity

Tools used in big data analytics can perform predictive analysis of the errors that can occur in the system. NoSQL frameworks can reveal the pitfalls in network security, which can then be patched before hackers discover them.

Prompt retrieval of information from networks

A case study by Zion Bancorporation was able to draw a comparison between the processing speed of data by traditional and big data analytics. Traditional SIEM (Security Information and Event Management) tools took 20 minutes to 1 hour to process data collected over a month. It took a Hadoop-based tool only about a minute to do the same. 

This case study shows the ability of NoSQL tools to process data at high speeds that could detect and barricade gaps in network security.

Analysis of datasets to identify past breaches

When breaches in the system occur, companies often fumble to find what exactly caused it. The sheer volume of big data often makes finding a fault in the network a hassle. With big data analytics, a detailed assessment of stored data can be performed within minutes, assuring prompt responses to cyber attacks.

Conclusion

Advancements in big data analytics have the potential to become an indispensable tool in bolstering network security. There is a rising demand for big data analysts to protect and secure sensitive data.

A career in big data analytics and network security is one click away with the advanced certification programme in cybersecurity from IIT Roorkee. The practical approach of this course aims to equip you to deal with cyber attacks and protect vulnerable networks. It is also a comprehensive guide to ethical hacking, social engineering, encryption, and data forensics.

Enrol now to begin an exciting career in cybersecurity today!

What Is the Role of Supply Chain Managers in Sales and Operations Planning?

Posted on by Imarticus Learning

Sales and operations planning (S&OP) is an inclusive planning procedure aligning a company’s demand, supply and financial planning cohesively. Sales and operations planning is a designed and executed decision-making procedure for the company’s master planning. 

An enterprise’s Supply Chain Managers help produce feasible material in adherence to a steady financial plan and available supply and resources. Since S&OP incorporates a strategic and tactical planning procedure, it provides insights into the shifting demand for resources in different geographies. 

Generally, an S&OP takes a comprehensive planning procedure between 18 and 36 months. In this article, let’s discuss the roles of Supply Chain Managers in sales and operations planning, their benefits and challenges. 

Supply Chain Manager: With Special Emphasis on Supply Chain Management

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Supply chain management primarily deals with the flow of goods and services from processing raw materials to final products. It helps streamline a business’s supply activities and integrate maximum customer value. 

Supply Chain Managers control the purchase of raw materials for production and ensure that the products come at the right time. They also coordinate on storing the raw materials so that no material gets misplaced or damaged in the procedure. 

The goal of a Supply Chain Manager is to enhance and develop the company’s supply chain strategies. He/she strives to improve productivity and efficiency and reduces costs by securing materials of optimal quality and quantity at the lowest price. 

Role of a Supply Chain Manager in Sales and Operations Planning 

A Supply Chain Manager maintains the alignment of daily operations with corporate strategies and closely examines the demand and supply in the department to keep equilibrium. Let’s see what the roles of Supply Chain Managers are in S&OP. 

  • Gathering Data: One of the primary roles of a Supply Chain Manager in an enterprise is to collect forecasts based on the existing predicament with various key variables. The key variables include inventory, recent sales and cash on hand.
  • Planning Demand: Supply Chain Managers help analyse, forecast and influence demands, thus shaping a pattern. As a result, they make an inventory to match the demand for raw materials.
  • Planning Supply: Supply Chain Managers have the role of assessing the production and distribution capacity. They also examine the constraints that might cause an untimely delay.
  • Reconciling Production Plans and Demands: Supply Chain Managers adhere to the demand and production plan alignments and ensure that they meet the company’s financial requirements and objectives. They also make recommendations to the management to ensure the best for their department.
  • Attending Executive Meetings: It is pivotal that a Supply Chain Manager attends executive meetings. He/she generally takes the inputs from the S&OP team, reviews the plan and subsequently makes a final version that would suffice well for the company. 

Benefits of Sales and Operation Planning 

Sales and operations planning comes with many benefits that help integrate the company’s mission and goals through various departments. It does not use a segregated approach but focuses on bringing different pieces of the company together and thus creating an overview. Let’s find out about the benefits of S&OP in the context of Supply Chain Management (SCM).  

  • Resource Optimisation: The S&OP helps in resource optimisation by helping the company know the right amount of materials needed for production. It avoids any circumstances for potential waste and uses the resources to their fullest with maximum efficiency.
  • Collaborative Improvement: S&OP helps in the alignment of the sales department alongside different production goals. It understands the effects of performances and thus improves communications accordingly to enhance efficiency. S&OP does not let production limitations be an issue for seamless collaborations.
  • Profit Maximisation: Once the sales and operations are substantially aligned, the company thus maximises its profits. They help greatly in terms of cost efficiency, lowering storage needs and eliminating complex issues in the supply chain. This would help the management set realistic goals and create a realistic forecast of future budgets. 

Conclusion 

Besides improving forecast accuracy, S&OP is efficient for curtailing inventory costs that boost the working capital. It is also responsible for increasing market revenues and shares by proving its effectiveness to new initiatives and products. 

If you are interested in earning a Certification in Supply Chain Management or SCM, then check out Imarticus Learning’s Professional Certification in Global Supply Chain Management and Analytics. You can make a successful career as a Supply and Operations Planner and learn sales and operations planning and other important aspects of inventory management incorporated in SCM. 

The program is certified by IIT Roorkee. To know more, check out its website right away! 

Everything you should know about k-means clustering

Posted on by Imarticus Learning

Data is the foundation of the Data Science function. With businesses generating a lot of unstructured data, machine learning frameworks help with handling unlabelled data. The entire domain of Unsupervised Learning under machine learning works on unlabelled data.

As we want to understand how data is organized under machine learning, clustering plays an integral role in this function. Clustering can also handle labelled data apart from handling unlabelled data. As the name suggests, clustering is used for grouping similar data together. One such technique of clustering is known as K-Means Clustering. It is one of the most common forms of clustering.

What is K-Means Clustering?

K-Means Clustering is a type of unsupervised machine learning algorithm used to group similar data points. The goal of the algorithm is to partition a dataset into K clusters, where each cluster contains similar data points. The number of clusters (K) is specified by the user and is one of the inputs to the algorithm.

The algorithm works by first randomly initializing K centroids, which are the centre points of the clusters. The data points are then assigned to the closest centroid based on their Euclidean distance. After all data points have been assigned to a centroid, the centroid positions are recalculated based on the mean position of the data points in the cluster. This process is repeated until the centroid positions no longer change or a maximum number of iterations is reached.

The K-Means Clustering algorithm is sensitive to the initial placement of the centroids, so it’s common to run the algorithm multiple times with different initial centroid positions to ensure that the final clusters are stable.

Use case of K-Means Clustering

K-Means Clustering is a widely used algorithm in many fields, including image and speech recognition, market segmentation, and anomaly detection. It’s also used as a preprocessing step in other machine-learning algorithms.

The key goal of K-Means Clustering is not just to make clusters but to create useful and meaningful clusters. It is critical that the data points in a cluster and closer together and far away from other clusters.

How does K-Means Clustering work?

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The key goal of K-Means Clustering is to find out clusters in the provided dataset. It can either be done by trial and error where the value of K keeps changing till we can create the best clusters. Another method is the elbow technique to find out the value of K. The value from the centroid and the number of centroids are critical for the proper functioning of this method. Accordingly, it uses those points to the corresponding centroid where the distance is the least.

Benefits of K-Means Clustering

There are several benefits of K-Means Clustering. The key advantages of K-Means Clustering are as follows:

Scalability

One of the key advantages of K-Means Clustering is its scalability, it can handle large datasets, and also it’s computationally efficient.

Pattern Identification

It can also be used to identify patterns and structures in the data that are not immediately obvious.

Adaptable

The K-Means Clustering can be adapted to new examples easily. As a result, it is one of the most common methods of handling unlabelled data.

Drawbacks of K-Means Clustering

Along with several advantages, there are some drawbacks of K-Means Clustering. The key drawbacks of K-Means Clustering are as follows:

Size of Clusters

One major drawback of the K-Means Clustering algorithm is that it assumes that the clusters are spherical and equally sized.

Density

K-Means Clustering also assumes that the data points within a cluster are dense, meaning that there are no large gaps between data points. This can lead to poor results if the data does not meet these assumptions.

Specific Knowledge

K-Means Clustering requires us to specify the number of clusters (K) in advance, which can be a challenge when the number of clusters is not known. Also, It’s not guaranteed that the final clusters will be optimal, or even meaningful, for the problem at hand.

It is important to pre-process the data in the case of K-Means Clustering. We need to convert the dataset into numerical values in case data is not already present in the prescribed format before calculations can be performed. We should also understand that applying feature-reduction techniques would improve the speed of the process.

IIT Roorkee Machine Learning Certification Course

The Machine Learning Certification Course from iHUB DivyaSampark at IIT Roorkee and Imarticus Learning can help us learn about K-Means Clustering and other techniques to help us build a data scientist career. The Machine Learning Certification Course from IIT Roorkee covers several modules which help provide an in-depth understanding of various topics in the field of data science and machine learning. The IIT Roorkee Machine Learning Certification Course has been designed by IIT faculty for early and mid-level professionals that provide live training by IIT faculty and campus immersion opportunity at IIT Roorkee.

What are demand forecasting basics and its features

Posted on by Imarticus Learning

Basics of demand forecasting

Demand forecasting is a term used in business planning to test the future demand for products or services. Depending on this assessment, a company further plans its operations, inventory, and supply chain management. Demand forecasters consider all present or past sales figures, experiences, and knowledge to predict what customers will want in the future.

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It is important to note that generating predictions is only the first step toward demand forecasting success. Success depends upon how the forecasts make informed decisions.

These decisions include product pricing, product direction, hiring, and company expansion. Moreover, the prime focus remains on helping the firm avoid overproduction and underproduction. In a nutshell, demand forecasting helps control all the marketing efforts and aspects of a modern business.

Various techniques or methods help in creating demand forecasts. Some ways include the following:

Collective opinion

Market experiment

Regression analysis

Consumer survey

Time series analysis

Delphi method

Each technique has its features that help in achieving different objectives. Therefore, the technique selection depends on the goals and requirements of the forecast.

Features of demand forecasting

Here are a few key features of the demand forecasting technique. Knowing these features can help choose the suitable method:

Time Horizon

The length of time that a forecast needs bears a great impact on the method of forecasting. When the time horizon increases, the probability of marginal forecasting error decreases.

Level of Detail

The forecasting method depends on the level of detail needed in the data. Also, this data must match the focus of the decision-making unit. For instance, a production or sales planning unit will do demand forecasting at an individual level. In contrast, the corporate planning department will need a high-level total forecast by product categories.

Stability

The method for demand forecasting can vary with stable times and fluctuation. While stable time requires lesser attention, it is difficult to forecast the periods in greater flux as they need lots of attention. In stable conditions, the ongoing patterns are a good option to continue with and put the past patterns into future use.

Data Pattern

Different forecasting method uses different patterns to identify and assume the data. So, it is best to choose a method that fits the need for data and its underlying relationships.

Cost

Improved and accurate forecasting procedure is a costly affair! The expenses also vary from forecasting methods and several costs. So, when a company finalizes its forecasting technique, it should be in sync with the firm’s economy.

Ease of Application

The chosen method must be easy to understand and use. Organizations must be able to interpret the result and use it for maximum benefit.

Other factors that affect Demand Forecast

Apart from these above-mentioned method-led features, several other factors affect the organization’s ability to forecast and predict demand. Let’s explore them here:

Customer factor

Both customers and potential customers affect demand. Of course, happy and loyal customers will stick to the product and generate referrals. But in case the business deals with solving service problems and not with the products, then forecasting becomes tricky!

If the competitors bring a better problem-solving approach, then the past sales data and knowledge become meaningless. Therefore, incorporating the marketing department’s plans into forecasts is a must.

One-offs events

Any single event can skew the historical data. For instance, if an organization’s name features on a TV show or any celebrity endorses it in any event, this one-off event can spike sales for that quarter or year. However, the company cannot consider the same projection in next year’s forecasting.

Future uncertainties

Various subtle changes or happenings can change the entire trajectory of demand. Even if the company has the most accurate predictions, the underlying forces are always at play! From unexpected price regulations or a new competitor to a viral video on social media and a global pandemic, certain uncertainties always loom around businesses!

A Career Option in Supply Chains

With the increasing demand for forecasting among businesses to make critical considerations in supply chain management and other marketing management, there is also a growth in their career options. Several Career Certifications For Supply Chain Professionals are available to help learners gain expertise in this scientific exercise of demand prediction. IIT Roorkee Supply Chain Management Course stands out as the best among them!

Futureproof your career with Imarticus Learning

IIT Roorkee Supply Chain Management Course has a design module that trains candidates with cutting-edge experience handling demand forecasting. This Career Certifications For Supply Chain Professionals equip them with the skills to succeed in high-demand job roles like supply and operations planner, demand planner, supply planner, data scientist, etc.