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Category: Analytics

Ways in which you can boost your supply chain management strategies

Posted on by Imarticus Learning

The supply chain is the backbone of any business, and visibility is key to ensuring its efficiency. There are many ways to improve visibility in your supply chain, including using technology, data analytics, and collaboration.

Technology can be used to track assets and inventory levels, as well as to monitor supplier performance. Data analytics can identify patterns and trends in data, which can help you make better decisions about your supply chain. Collaboration with other businesses in your supply chain can also improve visibility and communication.

Need for Streamlined Processes in Supply Chain Management

The need for streamlined processes such as automation, standardization, and process mapping is driven by the desire to improve efficiency and effectiveness within the supply chain. Companies can minimize errors and optimize performance by automating tasks and standardizing procedures. In addition, process mapping provides a visual representation of the workflow, allowing for easy identification of bottlenecks and potential improvements.

Factors to consider when upgrading strategies for supply chain management?

When upgrading strategies for supply chain management, there are several key factors to consider, including:

  1. Building Strong Relationships: It is important to build strong relationships with suppliers, manufacturers, distributors, and customers to ensure smooth and effective communication, increase trust, and reduce the risk of disruptions. 
  2. Managing Risk: Supply chain management strategies should include risk management plans that consider potential disruptions such as natural disasters, economic downturns, and changes in government regulations. 
  3. Embracing Innovation: Supply chain management strategies should also embrace innovation and technology, such as artificial intelligence, blockchain, and the Internet of Things, to improve visibility, increase efficiency, and reduce costs. 
  4. Sustainability: A sustainable supply chain strategy should consider the environmental and social impact of sourcing, production, and distribution and aim to reduce waste and emissions and improve workers’ lives in the supply chain. 
  5. Cost Optimization: Strategies for supply chain management should aim to optimize costs, including reducing costs associated with materials, transportation, and labor while maintaining quality standards. 
  6. Agility and Flexibility: Supply chain strategies should be agile and flexible, allowing organizations to adapt quickly to changing market conditions and customer needs. 
  7. Data Analytics: Strategies should also include data analytics to provide insights into supply chain performance and help inform decision-making and continuous improvement initiatives.

Tips for students to build a career in Supply Chain Management?

Here are some tips for students interested in building a career in supply chain management:

  1. Gain a solid understanding of the fundamentals: Study the basic concepts of supply chain management, including logistics, procurement, operations, and production planning. 
  2. Develop technical skills: Acquire skills in data analysis, project management, and technology such as enterprise resource planning (ERP) and supply chain management software. 
  3. Gain practical experience: Participate in internships, co-op programs, or work part-time in a supply chain-related role to gain valuable experience and make industry connections. 
  4. Network with industry professionals: Attend industry events, join professional organizations, and connect with supply chain professionals to build your network and learn from experienced practitioners. 
  5. Stay up-to-date with industry trends: Read industry publications and follow relevant organizations on social media to stay informed about current trends and developments. 
  6. Focus on continuous learning: Pursue additional education and certifications to continue developing your knowledge and skills and demonstrate your commitment to the field. 
  7. Develop soft skills: Soft skills such as communication, problem-solving, and collaboration are also important in supply chain management and can help you build effective relationships with colleagues and partners. 
  8. Seek out mentorship: Find a mentor who can provide guidance and support as you build your career in supply chain management.

Build Strategic SCM Career with Imarticus Learning:

If you want to build a successful career in the Supply Chain and Operations industry, consider enrolling in the Chief Supply Chain and Operations Officers online training from Imarticus Learning. This 10-month leadership program at IIM Raipur is designed for senior supply chain professionals. You will gain a comprehensive set of strategic, operational, personal, and technological skills to become an effective SCM manager.

During the program, you will have the opportunity to collaborate with other professionals in your field and embark on a unique learning experience. The course includes a simulation exercise where learners work on live-global projects. This supply chain management strategies course provides hands-on experience in balancing the responsiveness and efficiency of a supply chain.

Course USPs:

  • Develop leadership skills and prepare for global interactions
  • Become expert in SCM and make strategic decisions
  • Gain insight from pioneering SCM leaders
  • Opportunity to connect with alumni at IIM Raipur.
  • Join the Alumni league of Professionals at IIM Raipur

For further details, contact the Live Chat Support system or plan a visit to one of our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

How impactful is python in machine learning

Posted on by Imarticus Learning

Python is one of the world’s most widely used and popular programming languages. This is because, in many areas, it excels, for instance, in creating video games, embedded programming and even manufacturing mobiles. Also, Python is essential for machine learning (ML) and artificial intelligence (AI). In fact, all the data science online training programmes have a huge portion of their courses allotted for just Python. Notably, a good machine learning certification course gives a detailed knowledge of Python. Read on…

Why is Python important for machine learning?

Data science and machine learning course

The partnership of Python and machine learning has made its place in data science and IT. Python is used for various tasks, from software application development to web development. Here are some reasons why Python is important for machine learning: 

  • Flexibility

Python is an extremely flexible language, and for this reason, it can be used with other languages as well. It allows developers to choose between scripting or OOP. Python also does not need the recompilation of source code. So, it is very easy to see results, and the operations are also easily done. Thus, there is no room left for errors.

  • Very rich ecosystem

Python is a high-level coding language with a vast ecosystem of frameworks, tools and libraries. These libraries and tools are equipped with pre-written codes that help users carry out a large number of functions. This also saves a significant amount of time while coding.

  • Consistency and simplicity

The codes written in Python are concise and readable. Artificial intelligence (AI) and machine learning (ML) often have complex algorithms, but Python’s simplicity enables one to create reliable systems. It is straightforward and easy to understand, and hence easy to learn. 

  • Independence of platform

This is a binary platform and an independent programming language. Python can run on various platforms and different software architectures. One can write the program, compile and then run it on different platforms. Python runs on various platforms like Macintosh, Windows, Linux and macOS. Integrating other languages like C++, Java and R, with Python is also very easy.

  • Strong community support

Python has very strong community support, although it is a language that is open source. It is free and has a large number of very useful tools and libraries. Developers can also discuss their problems in forums or chat with other developers to find some solutions. Python also has corporate support with companies like Google, Instagram, Facebook, Quora and Netflix.

  • Great data visualisation

The presentation of data is extremely important in machine learning and data science. Python has been really helpful in presenting specific data in a human-readable format. Python libraries have great data visualisation tools, which help set up the data, figures, parameters and plotting. These kinds of libraries help present the data in different forms, like histograms, images, line plots, contouring and pseudocolour, three-dimensional plotting, and multiple subplots and paths.

But, why Python?

Python is one of the foundational languages in machine learning. However, the projects are different from a typical software project, which signifies that deep knowledge of the subject is required. The crux of having a career in machine learning is by knowing Python because it is both flexible and stable. 

It is very important to have the right sets of libraries and an environment well-structured for developers looking forward to solving programming challenges. This is where the pre-written sets of libraries have a huge part in helping them with the sets of frameworks and libraries to choose from, for example:

  • Keras – Used for deep learning and machine learning models
  • NumPy – Used for data manipulation and data cleaning
  • Scikit-learn – Used for data modelling
  • OpenCV – Used for image processing
  • Seaborn – Used for data visualisation
  • Caffe – Used for image processing purposes

It becomes easier for developers to create a product faster with these solutions. More so, the team of developers need not waste time searching for libraries which suit their project the best. The use of an existing library is the best for implementing further changes.

It is also observed that around 150,000 online repositories have packages which are custom built for Python. For example, Python libraries like NumPy, Matplotib and SciPy can be easily installed in programmes that run on the language.

Conclusion

The implementation of Python in different kinds of machine learning projects and various other tasks has made the work easier for data scientists, machine learning engineers and developers. Python can be used to compose the available data and analyse that, which makes it the most popular programming language in data science. The IIT Roorkee data science and machine learning course offered by Imarticus is an excellent programme that helps you to start a career in machine learning with an intricate knowledge of Python. 

What is the importance of aggregate planning in the supply chain

Posted on by Imarticus Learning

Aggregate planning for the entire supply chain could be an alternative if your manufacturing facility is inefficient and poorly planned. If a manufacturing company wants to increase operational efficiency, it must prioritise aggregate planning in supply chain management (SCM).

best supply chain management and analytics training

Aggregate planning methods can be useful when trying to predict supply chain needs several months in advance. With this method, you can see the big picture of the materials you will need to produce all the goods that people want to buy.

To receive bulk discounts and avoid shortages, it is best to place orders for the entire planning period. Knowing the ins and outs of aggregate planning and the supply chain is crucial before introducing it into your operations.

Examples of aggregate planning in supply chain management

The following are all examples of aggregate planning in SCM:

  • Good demand prediction
    You’ll need to know the demand size for your goods before you can plan when to order supplies. Future demand for your products can be accurately anticipated by utilising historical data. This is in addition to the trends and predictions made by experts in your field. You will need the resources indicated by your forecast. This will tell you how much to produce to satisfy the demand and maintain a constant rate of production.

  • Production of goods and services

    It is proportional to the accessibility of necessary resources (equipment, labour force, efficiency). You may check with your production department to find out how many products could it make within your time range. This might not be adequate to meet the needs of the public. But, making use of available production resources can help you set reasonable product output goals. Consider the possibility of worker and machine absences also.

  • Constraints on available funds

    Investing in something is fine, but you need to remember that your spending plan has limits. Your options may be constrained by your budget in certain situations. Think about the cost of interest for any loans you might need to start up your business. To sum up, save up enough money to cover your future expenses.

The significance of aggregate planning

Achieving the organisation’s long-term goals depends in large part on aggregate planning.

  • This leads to a more contented and joyful workforce.

  • Lessen fluctuations in staffing levels.

  • Determines available funds in the near term.

  • Contributes to optimal space utilisation.

  • Accomplishes organisational aims and objectives.

  • Aids in balancing supply and demand.

  • Reduces expenditures related to varied stocks, hence minimising stocking costs.

  • Maximises service to clients by balancing demand and supply, and reduces wait times.

  • Provides higher value to customers.

  • Makes the most of available resources in manufacturing.

  • Utilises the potential of various tools.

  • Keeps production rate fluctuations to a minimum.

  • Reduces a company’s overall operating expenses and boosts its bottom line so it may better pursue its objectives.

Strategies to implement aggregate planning

If you are facing a problem with aggregate planning, there are multiple strategies you can adopt. Here are some that can help:

Level strategy

The level method seeks to maintain steady output and headcount. Businesses must have accurate demand estimates to determine whether to ramp up or slow down production. This is in response to seasonal fluctuations in client demand. A level strategy provides staff consistency. One of the disadvantages of a level strategy though is an increase in backlogs and stocks.

Chase tactics

The chasing strategy seeks to dynamically balance supply and demand. The advantages of a chase strategy include having fewer goods on hand and fewer late jobs. The disadvantage is a drop in output, quality, and morale at work.

Using a complicated method

Hybrid strategies aim to strike a middle ground between level and chase tactics.

Before you begin your aggregate strategy

There are a few things to think about before beginning an aggregation plan. Have a look:

  • Knowing the goals of the overall strategy and the fallout if those goals aren’t met is a prerequisite.

  • Knowing how many orders make up your aggregate plan, and whether or not those orders will conflict with one another.

  • Determining which demand plans will not interfere with your aggregate strategy.

  • Thinking about whether or not the benefits outweigh the costs of implementing a comprehensive strategy.

The term “aggregate plan” is used to describe a strategy that is broad enough to apply to multiple industries. The change could be little, like introducing a new line of products, or significant, like expanding into an entirely new market.

Conclusion

Are you interested in understanding the basics of operations and supply chain management? Then, Imarticus Learning has just the right supply chain management course in association with IIT Roorkee. With this course, you can learn the basics and take your organisation to the next level. Contact their representative for more details.

Artificial Intelligence – How certification course can help boost your AI skills

Posted on by Imarticus Learning

Artificial Intelligence (AI) is rapidly transforming how we live and work. As businesses increasingly adopt AI technologies, the demand for skilled professionals in this field is skyrocketing. With the potential for high-paying jobs and a wealth of opportunities, AI is quickly becoming the most sought-after skill in the job market. 

With so many new technologies and concepts emerging, how can you stand out and stay ahead of the curve? The answer lies in certification. In this blog, we will explore how a certification course in AI can give you the skills, knowledge, and confidence to excel in this dynamic and growing field. 

From building a solid foundation in AI principles to learning the latest tools and techniques, a certification course will help you stay ahead of the curve and take your career to the next level. So, whether you’re a student, a professional, or just someone who wants to dive into AI, read on to find out why a certification course can help you achieve your goals.

What is Artificial Intelligence?

Artificial intelligence, or AI, is the branch of computer science that deals with building machines. Imagine a world where machines can understand natural language, see and recognize objects, play chess better than humans, and even drive cars independently. This is the world of AI.

Siri and Alexa are virtual personal assistants powered by AI, capable of understanding and responding to voice commands. They can set reminders, play music, answer questions, and tell jokes. Another example is self-driving cars, which use AI to navigate roads, avoid obstacles, and make split-second decisions to ensure the safety of passengers and other drivers.

AI is also revolutionizing the medical field with systems that can analyze vast amounts of medical data and help doctors diagnose diseases more accurately. In the financial sector, AI algorithms can analyze stock market trends and make predictions that allow investors to make informed decisions.

In short, AI has the potential to transform almost every industry and make our lives easier and more convenient. With its ability to learn and adapt, AI has the power to solve some of the world’s most pressing problems and shape the future in ways we can’t even imagine. So, if you’re looking for an exciting and dynamic field to get involved in, consider exploring the world of Artificial Intelligence!

How to learn Artificial Intelligence?

Artificial Intelligence course

Learning Artificial Intelligence (AI) has never been easier and more accessible. Anyone can start their AI journey with many online resources, tutorials, and courses. 

How to get started:

  • Build a solid foundation in mathematics, including linear algebra, calculus, and statistics. These subjects provide the mathematical underpinnings of AI and will be essential for understanding advanced concepts. 
  • Learn a programming language commonly used in AI, such as Python or R. Some online tutorials and courses can help you get up to speed with these programming languages. 
  • Familiarize yourself with the basic concepts of AI, such as machine learning, computer vision, and natural language processing. You can do this by taking online courses, reading books, or following tutorials. 
  • Practice your skills by building small AI projects. This will give you hands-on experience and help you understand how AI algorithms work in practice. 
  • Stay up-to-date with AI advancements by following industry leaders, attending conferences, and participating in online communities.

AI is a rapidly growing field with unlimited potential. So, to make a difference, learn something new, or have fun, start learning AI today. 

Discover IIT artificial intelligence and machine learning course with Imarticus Learning.

Are you prepared to advance your career in a new tech-focused field? Enroll at the E & ICT Academy for their comprehensive IIT AI ML course! You can get ready for the roles of Data Scientist, Data Analyst, Machine Learning Engineer, and AI Engineer with this intensive 9-month program. 

Through real-world projects from various industries, you will develop a solid foundation in data science concepts and work with industry experts to learn how to apply machine learning, deep learning, and AI techniques practically. 

Course Benefits For Learners:

  • Students work on 25 real-world projects to develop real-world business experience and prepare for a rewarding data science career.
  • With a certificate recognized by the E & ICT Academy, IIT Guwahati, and a credential supported by Imarticus Learning, students can attract employers’ attention and demonstrate their skills.
  • Students who complete this IIT artificial intelligence course land lucrative jobs in the machine learning and artificial intelligence sectors.

Visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad, or get in touch via the chat support system.

Learn Python Online In 2022: Zero To Mastery

Posted on by Imarticus Learning

Python is a high-level programming language that Guido van Rossum created in 1989. It’s also great for beginners because it has a simple syntax that makes it easy to write code. Python is an open-source language, and you can download it for free. You can use Python on your computer or in the cloud! 

Python is the most popular language for data scientists and AI. A common myth is that it’s hard to learn Python, but this guide will show you how to learn Python online in 2022!

How to learn Python online?

become a Data Analyst

Python is an interpreted, object-oriented, high-level, dynamically semantic programming language. It is particularly appealing for Rapid Application Development and for usage as a scripting or glue language to tie existing components together due to its high-level built-in data structures, dynamic typing, and dynamic binding.

You can learn Python online, but you might want to consider taking a class. There are many great ways to learn Python online.

Here are just a few:

  • Python tutorials
  • Python documentation
  • The Python community (forums and meetups)
  • Books and videos on YouTube.

Here are a few reasons to learn Python in 2022

If you’re just getting started with coding and are wondering whether or not you should learn Python, here are three reasons why it’s a great choice:

  • Python is easy to understand. The syntax is relatively simple compared to other programming languages. There are also plenty of tutorials available online that explain how to use its various functions.
  • Python is suitable for general-purpose programming. Whether you want to write scripts for your personal computer or build web applications on top of the Django framework, Python has all the tools necessary for creating basic software programs in various domains like science and finance.
  • Python can improve your coding skills across multiple languages: Most programming languages have used the concepts behind object-oriented design since their creation in the 1990s. So once you’ve mastered using them with Python’s syntax structure, it should be reasonably easy to switch to another language.

Bonus reason to learn Python as a data scientist.

Another great reason to learn Python is that you can use it for data analysis and machine learning. If you’re looking for a language that can help you with your career in data science, then Python is the best choice for many reasons:

  • It’s easy to learn and use
  • It’s easy to read
  • It’s easy to maintain
  • It’s easy to share (useful for collaboration)
  • Python is easy to scale

Discover Python Training courses with Imarticus Learning

We build predictive models that improve business outcomes by learning how data science gets applied in the real world. This ensured placement program is appropriate for professionals and recent graduates who wish to pursue a career in data science and analytics. 

Course Benefits For Learners:

  • Learn how to use Tableau to master the abilities of Python for data science, SQL, data analytics, machine learning, and data visualization.
  • 25 in-class, real-world projects will help you master data science abilities. Participate in hackathons, capstone projects, and mock interviews to develop practical skills.
  • Our curriculum includes capstone projects, real-business initiatives, pertinent case studies, and mentoring from relevant industry professionals to assist you in becoming a skilled data scientist.

How does sales and operations planning work in a Supply Chain

Posted on by Imarticus Learning

How Does Sales and Operations Planning Work in A Supply Chain

Meta Description: Benefits of sales and Operations planning in the supply chain. Know more about a career in supply chain and operations

What is sales and operations planning?

One of the most challenging aspects of a supply chain is sales and operations planning. However, it is also a crucial variable on which the success of a business depends. The main goal of the planning is to create a unified business plan. Those planning to have a career In Supply Chain and Operations need to learn it works.

The sales and operations planning gets inputs from teams like:

·        Manufacturing

·        Distribution

·        Marketing and

·        Finance

Businesses combine these inputs to simplify the process for stakeholders. It also helps them to create a support system for the same.

Need for sales and operations planning

The goal of sales and operations planning is to mix the components and activities of demand and supply. It should revolve around a consensus-based plan. Building consensus is easier with planning in place. If we don’t bring the team together under planning, it is impossible to make a successful design.

Thus, sales and operations planning provides a holistic view of every component. Then collective adjustments and inputs are made. This collaboration creates a resilient and shock-proof supply chain. Which, in turn, results in the success of a business.

Steps to create sales and operations planning

Generally, senior management works on sales and operations planning. Following are the steps in the process:

1. Forecasting

Data and trends are the major components of sales and operations planning. At first, data on the sales history is collected. During this stage, it is important to consider internal and external variables. These components affect sales like customers, competition and industry demands.

Then the data aids to forecast future sales. It helps to analyse and identify trends. It then builds the process in a supply chain based on the forecast.

2. Demand planning

Demand is a major factor that runs sales and operations planning in a supply chain. In this process, cross-collaboration is important. After the forecasting analysis, businesses adjust the inventory and customer service policy. The adjustments depend upon factors like demand and the source of the product.

According to the analysis of revenue and units, the businesses measure demands.

3. Supply planning

Another major aspect of sales and operations planning is sales planning. During this process, teams assess the capacity of the supply chain and products. The finance, operations and materials teams meet for the assessment. They determine the issues related to

·        Manpower

·        Machinery

·        Suppliers and

·        Distributors

During this process, the teams create a supply chain plan to check the limitations of capacity. They also work to come up with ways how to deal with the problems.

4. Pre-sales and operations planning

The first meeting of planning in the supply chain is crucial. Various teams take part in this process namely:

·        Sales

·        Finance

·        Marketing

·        Materials

·        Operations

·        Product management

·        Human resources

The teams cooperate and compare the forecasting analysis of the demand and supply components. They also discuss the financial impact of both plans.

5. Executive sales and operations planning

The executives of teams meet to analyse the demand and supply plans. They also go through forecasts and recommendations. In the meeting, the executives approve the final sales and operations plan.

6. Finalization and implementation

After approval of the plan, the teams work on implementing it. Then, they assess the factors for implementation. It is important to assess the sales and operations planning regularly. It helps to find faults within the processes.

Benefits of sales and operations planning in supply chain

An integrated and collaborative sales and operations planning ensure:

·        Increase in responsiveness

·        Reduction in operating cost

·        Increase in productivity

·        Increase in revenue

·        Increased transparency between departments

·        Improvement in inventory management

·        Creation of a resilient supply chain

·        Demands of customers are met on time

·        Improvement in budget and sales forecasting

·        Improvement in decision-making about demand and supply

·        Comprehensible understanding and management of a product at every stage

·        Improvement in sales and customer experience

Challenges of sales and operations planning

·        Understanding the complexity of the processes

·        Choosing right sources

·        Maintain transparency between departments

·        Coordination between all stakeholders

·        Scaling progress of a current process

·        Lack of coordination while taking important decisions

·        Usage of all data and analytics

Career In Supply Chain and Operations

For a career in supply chain and operations, you will need the best leadership program for supply chain professionals. Learners can choose from a wide variety of jobs like

·        Client Management

·        Consultative sales

·        Program Management

·        Consulting

·        Supply chain operations

·        Manager Technology

·        Executive

·        Manager Program Management

Why choose Imarticus Learning?

Imarticus Learning offers a certification program for Chief supply chain and operations officers. The program is offered in collaboration with IIM Raipur.

The 10-month-long program is offered in online mode. The 6-module program has benefits for the learners such as:

·        C-suite training

·        SCM Globe simulations

·        Masterclasses by industry experts

·        Campus immersion

·        IIM Raipur alumni status

To know more about how you can have a Career in Supply Chain And Operations, click here.

10 Must-Know Concepts For Your Data Science Online Training

Posted on by Imarticus Learning

Enrolling in a data science online training course from a prestigious institution such as the Indian Institute of Technology (IIT) guarantees that we will be learning from experts in the field. IIT Roorkee’s Data Science and Machine Learning Course is one of the best data science online courses available. The course covers the essential concepts that are essential to any data science online training, such as machine learning algorithms, linear regression, data visualisation, and communication. Completing the course can open up the doors to a prosperous and fulfilling data scientist career.

Data Science Course

However, before we begin our journey with The IIT Roorkee Data Science Online Course, we need to be familiar with foundational concepts of data science that can make our learning journey smoother. Mastering these concepts beforehand will also enable us to understand the IIT Roorkee Data Science Online Course better and help us make the most of it.

Here’s a list of 10 must-know concepts for our data science online training:

1. Datasets

The most basic yet most important concept we must know is the dataset. A dataset is a collection of data points, usually consisting of different variables such as numerical values and textual information. Datasets are an essential part of data science and machine learning projects because they provide the information needed to train models. Understanding how to select, manipulate, and analyse datasets is crucial for data science online training.

2. Data Cleaning & Pre-Processing

Dirty and unstructured data can impede our data science projects. Data cleaning and pre-processing are the processes of making sure our datasets are in a usable state. It involves dealing with issues such as missing values, outliers, or incorrect data types.

3. Data Science Tools & Technology

Data science tools and technologies such as Python, R, SQL, Tableau, Hadoop, and Spark are used to manipulate data and create models. It is important to understand the purpose of each technology and its advantages and disadvantages so that we can choose the right tool for the job.

4. Machine Learning

Machine learning is a subfield of artificial intelligence that enables computers to learn from data and make decisions without explicit programming. Understanding the basics of machine learning algorithms is a key concept for data science online training and IIT Roorkee’s Data Science Online Course.

5. Regression & Classification

Regression and classification are two of the most popular machine-learning algorithms used in data science projects. Regression is used to predict the outcome of a continuous variable such as stock prices or house prices. In contrast, classification can be used to classify data into different categories, such as spam or not spam.

6. Data Visualisation

Data visualisation is the process of transforming raw data into visual representations, such as charts and graphs. It is used to make data easily accessible and understandable to humans. With IIT Roorkee’s Data Science and Machine Learning Course, we can learn the fundamentals of data visualisation and use tools such as Tableau and D3.js to create stunning visualisations.

7. Natural Language Processing

Natural language processing (NLP) is a subfield of artificial intelligence that deals with understanding and generating human language using computers. It is a rapidly advancing field in data science, and many modern applications use NLP, such as chatbots and text-based search engines.

8. Big Data

Big data refers to large, complex datasets that cannot be processed using traditional methods. IIT Roorkee’s Data Science and Machine Learning Course will teach us how to use big data tools such as Hadoop and Spark to process, analyse, and visualise big datasets. Thus, we need to familiarise ourselves with big data concepts and technologies.

9. Mathematical & Statistical Concepts

Mathematical and statistical concepts are foundational for data science. We need to be strong in topics such as linear algebra, calculus, and probability to understand the underlying mathematics behind many data science algorithms.

10. Data Science Real-World Applications

Finally, we need to understand the real-world applications of data science. We can use data science for a wide range of tasks, such as predicting customer churn, forecasting sales, or detecting fraud. Being cognizant of the potential of data science can help us with projects in the IIT Roorkee Data Science Online Course.

Data science is a rapidly evolving field. As per estimates, the number of data science jobs is expected to increase by 28% in the next five years. In India alone, there will be about eleven million job openings in the data science field. Moreover, there is a shortage of data scientists in the job market globally.

Thus, embarking on a data science journey with IIT Roorkee’s Data Science and Machine Learning Course is a great way to develop the skills needed to stay ahead of the competition. Following the 10 must-know concepts outlined above, we will be well on our way to becoming successful data scientists!

Four Ways to Apply Data Visualisation to Your Daily Life

Posted on by Imarticus Learning

Data is present everywhere. We are surrounded by different forms of data, be it Google Maps or diet planners. One of the major reasons for the widespread use of data is the ease of communication. Visuals communicate more easily and effectively than words. Medieval paintings are a testament to the fact that people have used data visualisation since time immemorial for conveying complex ideas.

Keep reading to understand the meaning and logic behind data visualisation.

What is Data Visualisation?

data analytics courses

Raw, written data is available in both organised and unorganised forms. Using data visualisation, it is translated into visible forms like graphs, pie charts, videos, GIFs, reports and images. You do not have to go through large chunks of data. You can quickly understand the trends in the data through visuals. Companies use these techniques for the process of decision-making.

Four ways to apply data visualisation in real life

Raw data is of no use to a company. The primary aim of processing data and generating visuals is to easily identify the patterns in big data. Companies all around the world are seeking individuals with knowledge of big data and its applications. Let us understand how to perform the visualisation of big data in the real world.

Bar Charts: Bar charts are one of the best visualisation options for comparing two or more quantities. You can use it to analyse your income and spending in different months of the year. Companies can use this tool for measuring the performance of their internal departments against each other.

Mapping: Maps are used for understanding the distribution of resources or products for a company. You can use a map for planning the itinerary of your international or local trips. If a company has operations overseas, a map helps in comparing the performance of different branches against that of the headquarters.

Matrix: A matrix is a visual representation that displays the existence of several factors (or groups) in a general location. If you have a big data set, a matrix helps in understanding the relationship between different elements of this data set. You can use a matrix for identifying the cause of a problem. Thus, you can allocate resources according to the needs of different areas.

Box Plots: A box plot helps in understanding the range of a data set and several other characteristics of a given group. From this representation, you can understand the outliers, range and variance of a particular data group. It can then be compared with others. Using this representation, you can check if there are any unused groups in a data set.

Is data visualisation scientific?

Data analytics (or visualisation) is much more than turning written data into visuals. The process of accomplishing this goal has a lot of scientific reasons. They show how humans gather and process any type of information.

According to several psychologists, the human thinking process can be generally categorised into two systems. System one is responsible for reflex thoughts. These refer to the ideas or solutions that the brain gives without processing any information. On the contrary, system two generates thoughts and solutions based on the results obtained after processing the information received from outside.

When system one receives data in a familiar format, it becomes easier for system two to process it more efficiently. System one is used to immediately process the information received from our visual senses. Thus, it is better if data is represented visually to allow better decision-making.

Start a Data Analytics Course with Placement

Imarticus Learning offers PG in Data Analytics and Machine Learning. This course can be helpful for aspiring data scientists. It guarantees job interviews opportunities in renowned companies. These include HSBC, Standard Chartered, ICICI Bank, Infosys, Wipro, HDFC Bank, Deloitte, Oracle, and many more. The curriculum is designed by professionals to include the latest job-relevant skills using tools, packages and programming languages. They include Python, Spyder, pandas, NumPy, OpenCV, Colab and TensorFlow.

This course demands the completion of 25 projects. These will help you understand concepts better and improve your job prospects. These real-world projects are completed in class. You can add these to your resume as a part of your portfolio. Additionally, you can be a part of competitions like Hackathon. You can also participate in mock interviews to improve your skills. The professionals at the institute will help you create LinkedIn and GitHub profiles to attract employers.

Learn data analytics by visiting the website. You can interact with professionals for solving your queries through call or chat support on the website. You can also visit one of our centres.

Interested in taking the Certificate Program in Data Science and Machine Learning? Read on

Posted on by Imarticus Learning

Today, Machine learning is being leveraged in almost every sector to bring more efficiency, intuition, and applicative use to products and services. The global machine learning market is expected to grow at a compound annual growth rate of 38.8% between 2022 to 2029, from a value of $ 21.17 Billion in 2022 to $209.91 billion. On the other hand, the market size of data science platforms market size is estimated to become a $ 378.7 billion industry by 2030, growing at a Compound annual growth rate of 16.45 3%, between 2022 to 2030.

As the applicative uses of these two functions keep growing, various sectors will experience a growing demand for skilled professionals with expertise in data science and machine learning. In this blog, I delve deeper into these domains and review the skill sets, and growth opportunities available. I also talk about how pursuing a contemporary machine learning certification course can give you a competitive edge in the job market.

The growing importance of machine learning

Today, consumers are growing increasingly dependent on the benefits of machine learning for various purposes. Here is an example. Machine learning is leveraged by a popular language learning app to curate an intuitive learning experience. The app can gauge the proficiency level of the learner and adapt a gamified learning plan, based on the data captured. This application completely eliminates the need for human teaching intervention. It enables millions of learners to learn simultaneously but at their own pace. It also enables them to learn through a format best suited to them. The application of machine learning is enabling platforms to reduce costs, and grow at scale. There are many such examples of how machine learning is transforming lives daily.

Machine learning simplified

So what exactly is machine learning? It is a branch of artificial intelligence (AI) and computer science, which mirrors how consumers engage and learn, aided by data usage and algorithms. The accuracy of engagement and responses gradually improves. Those engaged in machine learning work are working at the cusp of innovation. Having an in-depth understanding of data science coupled with the principles of machine learning can give one an edge in this domain.

Data Science Course

To build these combined skills, I recommend pursuing a Certificate Program In Data Science And Machine Learning. A professionally designed programme can equip ambitious professionals with the tools and techniques needed to excel in this innovative field.

5 attributes of a superior programme

Today, the market is flooded with several free programmes and paid programmes. So, how do you choose the right one for you? According to me, your machine learning certification course should have these five attributes

1. A superior curriculum

Make sure that the programme covers all the contemporary subjects required to excel in data science and machine learning, today. Machine learning with Python, fundamentals, and uses of SQL, data visualisation with tableau, Python programming, and Statistics with data science.

2. A pedigree certification

It is important to have recognised credentials on your resume. For instance, an IIT Roorkee certification course not only offers a superior pedagogy, but the certification is also well-respected in the industry. Another benefit is that you will also have access to world-class faculty from such an institution.

3. Hands-on training

Today when companies hire you they are looking for professionals who can hit the ground running from Day 1. I suggest choosing programs that offer live training via capstone projects. These projects simulate real-world environments, in which learners need to address real challenges, and also work collaboratively with other learners. This is a great opportunity to build real-world skills.

4. Campus immersion

While a good certification program may be hosted completely online, Some also offer a compass immersion engagement. This means that you may be able to spend some time at campuses like IIT Roorkee which celebrate a culture of innovation. You can get to engage with faculty batchmates as well as mentors from the campus.

5. Career services and support

The fifth pillar that completes the circle includes access to comprehensive career-related services. This includes job interview training, resume/profile building, a network of mentorship, and placement support.

In Conclusion

The future is bright for those who invest in skill-building early on. Pursuing a professionally-designed certificate programme in data science and machine learning is an investment in your career. It equips you to develop contemporary in-demand skills that help you leverage technology to address real-world challenges and accelerate career growth.

To know about the Certificate Program In Data Science And Machine Learning, contact us through chat support, or drive to our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Job Assurance in top companies: Can your data science course get you a job?

Posted on by Imarticus Learning

Data science is a rapidly growing field that has seen a huge surge in demand over the past few years. As the demand for data scientists grows, many individuals are asking the question – can my data science course get me a job in one of the top companies?

In this article, we will explore the answer to this question and how a Data Science Course With job assurance can help you secure a placement in one of the top companies.

Data Science Overview

Data science is a field of study that combines computer science, mathematics, and statistics to analyse and interpret large datasets. Data scientists are responsible for collecting, analysing, and interpreting large datasets to answer business questions and develop predictive models. Data science is becoming increasingly important in many industries, including finance, healthcare, retail, and education.

Data Science Job Assurance

The demand for data scientists is growing rapidly in the corporate world as companies are looking to leverage data to gain a competitive edge. Data science professionals have the potential to make a significant impact on a company’s bottom line by uncovering new insights and driving innovation. Top companies such as Google, Apple, Amazon, and Microsoft are actively recruiting PG In Data Analytics for their teams.

Data Science Course with job assurance plays an important role in the placements of top companies, as it helps organisations identify the best candidates for their roles. Data scientists are skilled in using advanced analytics tools, including machine learning and AI, to analyse large datasets and uncover patterns and trends. They also help organisations build predictive models to make better decisions and improve their business operations. Data science is also used to optimise marketing and product strategies and identify new opportunities in the marketplace.

Benefits of Taking a Data Science Course

Here are some of the many benefits of taking a Data Science course:

Learn to Analyze Data

best data analytics course

A data science course will provide students with the necessary skills to analyze data and draw meaningful conclusions. This is essential for any modern business, as data is constantly being generated and needs to be interpreted in order to make informed decisions.

Develop Problem-Solving Skills

A data science course will also help students develop problem-solving skills. Data science requires students to identify a problem, understand it, and find the best solution to it. This is a valuable skill to have in any field.

Understand Big Data

Big data is becoming increasingly important in the business world. A data science course will help students understand how to process, store, and analyze large datasets. This can be a great asset when looking for a job in the current market.

Develop Technical Skills

Finally, a data science course will also help students develop technical skills such as coding, database management, and machine learning. These skills are becoming increasingly important in the current job market, and a data science course can be the perfect way to gain these skills.

Better Placement Opportunity

One of the biggest benefits of a Data Science Course With Placement is the improved placement opportunity it can provide. Data science is a highly sought-after skill, and employers are always looking for candidates that possess the necessary skills and knowledge. By completing a Data Science Course With Placement, you will demonstrate to employers that you are up-to-date with the latest advancements in the field and have the ability to apply them to real-life scenarios.

Finding The Right Data Science Course

There are a few factors that you must have in your mind while finding the right data science course:

A. Researching Available

Courses When looking for the right data science course, the first step is to do some research. There are many online courses available, so it’s important to evaluate each one to determine the best fit for you. Look for courses that cover topics that are relevant to your career goals or that focus on specific programming languages or technologies. Be sure to read reviews and ask for feedback from other students who have taken the course.

B. Cost and Time Commitment

When looking for a data science course, be sure to consider the cost and the time commitment. Many online courses are offered at a discounted rate, but be sure to read the fine print to ensure that you understand what you’re signing up for.

Parting Words

Data science is an exciting and fast-growing field that has become increasingly popular over the past few years. With the right data science course and the right attitude, it is possible to secure a placement in one of the top companies. However, it is important to remember that success does not come overnight and that dedication and hard work are needed in order to achieve success. With the right attitude and determination, you can make the most of your Data Analyst Training and secure placement in one of the top companies.

Ready to take your data science skills to the next level and land the job you’ve always wanted? Sign up for our PG In Data Analytics program today!