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Category: Analytics

The data analyst’s guide to analysis with tableau

Posted on by Imarticus Learning

The data analyst’s guide to analysis with tableau

Tableau is a powerful data analysis tool that can help you find insights into your business data. It’s easy to start with tableau, but some advanced features require more knowledge and skill than just downloading the software.

 In this guide, we’ll go over everything from starting with tableau to more advanced techniques for getting the most out of this tool.

Getting Started with Tableau

A tableau is an excellent tool for visualizing data. You can use it to analyze data from small sample sizes to large databases. Data scientists, data analysts, and business users alike can use it. Tableau is fast and easy to use: no training required!

Aggregating Data

Aggregating data is the process of summarizing data by a single value. For example, if you have a table with sales by month and product type, you could aggregate this information into one row showing how much revenue each month generated from each product.

Tableau can do this for you using built-in functions or custom aggregation formulas. You can also use tableau’s built-in tools to perform any number of different kinds of aggregations:

  • Summarizing grouped values into one cell 
  • Counting unique values within a group 

Tableau has a very easy-to-use interface that allows you to create these in no time! 

Using Multiple Visualizations to Tell a Story with Data

You can also use multiple visualizations to tell a story with data. The art of data storytelling involves effectively conveying insights from a dataset through stories and visualizations. You can also use it to motivate your audience to take action by putting data insights into context.

The following three elements are crucial to data storytelling:

Data: The foundation of your data story is a thorough analysis of accurate, complete data. You can also comprehensively understand the data by analyzing it using descriptive, diagnostic, predictive, and prescriptive analysis.

Narrative: An oral or written narrative, a storyline, conveys insights drawn from data.

Visualizations: Using visual representations of your data and narrative can help you tell your story memorably and understandably. 

More Advanced Features of Tableau

Natural language processing (NLP) is a field of artificial intelligence that focuses on extracting information from textual data using computers. You can use NLP to identify keywords in a sentence, create sentiment analysis models and perform sentiment classification.

Sentiment analysis involves the study of opinions expressed in social media posts or other forms of text. Opinion mining involves studying existing opinions and how they relate to each other so you can predict future opinions based on current ones.

Explore a career in data analytics with Imarticus Learning

 With the help of this data science course, you could land a career in tableau. Put your new knowledge to use by using data science to build complex models that generate crucial business forecasts and insights.

Course Benefits for Students:

  • Students will be familiar with the most popular data science methodologies and tools, data analytics, and machine learning basics.
  • Students who complete 25 real-world projects and case studies under the supervision of business partners will receive a data analytics certification course.
  • The ability to display data using a data analytics tool is one of the most sought-after skills on the market. 

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

5 Business Skills Critical For A Data Scientist’s Career

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5 Business Skills Critical For A Data Scientist’s Career

The emergence and widespread acceptance of technology, especially internet services, has significantly impacted how businesses are run these days. The amount of data available has helped companies to dissect and analyze them to serve their consumers better. In this process, a data scientist becomes an important individual.

As a professional, a data scientist works to study and interpret that data in simpler terms to help everybody understand what it represents. Doing so helps other departments to comprehend the strengths and weaknesses of the organization or a particular product or service and plan accordingly to pave a path for future success.

Nonetheless, besides having the right technical know-how, to have a successful data science career, you need to know a few business skills as well.

5 Best Business Skills to Become a Successful Data Scientist

Business skills are primarily the soft skills that one needs to work in an organization. These skills primarily determine the type of employee one is and helps in building a perception. So, you don’t want a negative one, do you? Well, then, keep the following pointers in mind –

Finding why you have been hired

Following the completion of your data science course, you apply for a job. After getting hired, you need to understand the reason behind your appointment.

Well, yes, you know the designation you have received, but understanding the job at hand is essential to perform your duties better. Along with that, you also need to learn your company’s expectations of you.

Once you are clear of these objectives, it will give you a clear roadmap on how to work and behave. More importantly, what is the goals to fulfill during your tenure in a company?

Negotiating office politics

Another skill concerns interpersonal relations with your office colleagues. How you tackle cultural misunderstandings or how you handle disagreements, and how to deal with office politics. Although young data scientists neglect these soft skills, you need to take them seriously. These are some small aspects that can have a significant effect on your career. Here your technical expertise does not matter. What matters is your presence of mind in various tricky situations.

How to communicate effectively

It is an extremely trivial skill, but it is the most important of all business skills. You should be crystal clear in communicating your messages with other parts of the team. It will also help you to deliver presentations effectively. Top experts in the field of data science have the knowledge to produce statistics that deliver the inference to the spectators. Communication is easy to learn, and mastering it might help you go a long way.

How to set project objectives

If you get the responsibility of handling projects, then you will need to master the art of setting goals and managing the expectations of the team and stakeholders. It will ultimately help you in starting a project and completing it successfully. This will help you win the trust of people in the organization and rise high in your career.

Delivering project goals while handling your career

The core of your technical skills acquired in a data science online training program depends upon your understanding and utilization of some key concepts and data frameworks developed by software developers like agile or scrum, ML ops, and Jira boards. Sticking to these concepts, you will be able to steer towards your goal despite variable expectations and the absence of clarity in a data science project. Mastering these will help you in choosing between various job opportunities in the future. It will also help you strengthen your CV. You could even work independently or handle a freelance career.

Final Words

Becoming a data scientist is not a tricky job, with various data science courses available online from education institutions like Imarticus. However, these business skills, or soft skills as they are called, should be cultivated by you. So, to sum up, take good care of this side of your professional life as it will make a similar impact on your career as securing a great grade in a data science online training.

Learn Artificial Intelligence: Train An AI Engineer To Play A Snake Game Using Python

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Learn Artificial Intelligence: Train An AI Engineer To Play A Snake Game Using Python

Artificial Intelligence (AI) studies how to teach computers to do things that require intelligence when done by humans. The primary objective of AI is to create an AI system capable of performing cognitive processes associated with human minds, such as problem-solving and learning. 

AI has become a household name thanks to movies like Terminator 2: Judgment Day, in which artificial intelligence seeks to destroy all humans; or Robot, where AI finds love and becomes more creative than any human artist ever could be; or even Wall-E, where humanoid robots are designed for manual labor but end up saving the world! 

However, there are many different ways to use artificial intelligence besides these fantastical scenarios—not all involve destroying humanity either!

What is Artificial Intelligence?

Artificial intelligence is a broad field of computer science concerned with developing intelligent computer systems. AI research is concerned with the subject of how to create machines capable of intelligent behavior.

In practice, AI applications may get used in a variety of ways, including:

  • Machine learning is a unique way of training computers to learn from data without directly programming them.
  • Natural language processing entails teaching computers to comprehend human language and respond naturally.
  • Predictive analytics is a technique that uses artificial intelligence to forecast future events, trends, and behaviors.
  • Computer vision is a computer’s capacity to perceive and comprehend digital pictures.

What Are The Different Types Of Algorithms Used For Building AI Systems?

There are many different types of algorithms used for building AI systems. These are machine learning, deep learning, neural network, reinforcement learning, and natural language processing. 

Game playing and expert systems also use some algorithms to perform their tasks optimally. You can use genetic algorithms (GA) for optimization problems with no known solution or when there are too many variables involved in finding a solution for problems such as scheduling issues. 

Artificial Intelligence is indeed the future. It will help us make our lives easier and better. And if you want to use this in your daily life, all you have to do is get started with the basics. 

The famous Snake game is one example of this. This classic game has been around for decades, but recent AI advancements have enabled an entirely new way to play.

AI can generate a first-person view of the game environment instead of the standard top-down view. This gives the user a new viewpoint on the game and creates a more immersive experience.

Artificial intelligence may also get utilized to generate more lifelike snake motions. Traditional Snake games employ simple pixelated visuals, but artificial intelligence (AI) may generate realistic snakes that slither and coil like the real thing.

Explore AI and Machine Learning courses with Imarticus Learning.

Become an AI engineer by attending the E & ICT Academy deep learning Artificial Intelligence certificate course. Students will benefit from this AI course as they prepare for jobs as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • Students work on 25 real-world projects to get industry experience and prepare for a rewarding career in data science.
  • You may impress employers and exhibit your abilities with an E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed credential.
  • This IIT artificial intelligence course will assist students in obtaining lucrative professions in artificial intelligence and machine learning.

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

The big 5 predictions for Machine Learning certifications in 2022

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The big 5 predictions for Machine Learning certifications in 2022

The future is AI. Machine learning is no longer just an aspect of artificial intelligence but one of the most pivotal building foundations of innovation and new possibilities. 

So, what are the top 5 predictions for Machine Learning and AI:

AI solutions will infiltrate the corporate world – In the past few years, the demand for data scientists has skyrocketed. With digitization taking over, manual labor had to be replaced with the likes of AI. Last year witnessed a boom in the application of AI to compensate for the turmoil in the labor supply chain. Customer service solutions turned to AI with chatbots and various other automated services to combat the pandemic’s impact on labor and businesses.

With technology advancing alarmingly, businesses have no choice but to adapt to AI. AI has become the oil that keeps the corporate world running smoothly today and businesses that don’t adopt new technology are not far from falling out of the race. 

More and more students will enroll in machine learning courses – Machine learning has become an indispensable need in the corporate world. With businesses looking to expand, data has become an integral part that demands data scientists who can decode it and analyze it to provide innovative solutions to real-time problems. Machine learning examines complex data automatically and companies need data scientists who can give them insight into bigger data by creating an algorithm that they otherwise wouldn’t have access to.

With the increase in demand for data scientists, more and more students will turn to courses like machine learning with python or learn data analytics online via the various courses available on the internet. Increased creative AI – Today, whenever we go on the internet, we see online mascots and other characters that aren’t real people. They’ve been created through the application of AI. Several different industries like music, fashion, and art have been using AI for years now to generate creative content.

Although AI has not yet penetrated mainstream media, given how fast technology is gaining ground, a computerized version of today’s mainstream content like AI-generated award shows is a powerful possibility. 

Synthetic data generation

2021 witnessed companies struggling to acquire data and manage it efficiently. It is but natural that they would turn to technology that would enable them to generate synthetic data. 

What is synthetic data? Synthetic data is not based on data collected from the real world instead, it is data that is created via an algorithm. Synthetic data comes with its own set of security and privacy features which can prove to be extremely beneficial to companies. It has been predicted that by 2025, synthetic data will be accountable for 10% of data generated in its entirety.

The healthcare unit and marketing domain are gradually but most definitely switching to synthetic data. Renowned companies like Apollo are fabricating synthetic datasets to be used in line with autonomous vehicles. As more professions start experimenting and incorporating synthetic datasets to create unique products and services, we will eventually see the rise of this form of data in every aspect. 

AI and machine learning will be key to achieving sustainability – Industries today are trying to adopt more sustainable forms of technology. From small startups to major brands in the fashion industry, attaining sustainability has become the prime theme for most companies and the adoption of AI has played an important role in working towards achieving that goal. 

Conclusion

AI and machine learning have become imperative in the innovation and development of society. The need to consistently model tune AI is critical to creating an ethical and efficient future for machine learning. 

Supply Chain Planning In 2022: What, Why, And How?

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Supply Chain Planning In 2022: What, Why, And How?

The supply chain is an integral part of every business. It is the backbone of any organization and the key to its success. Supply chain management is an especially important aspect of business operations because it allows companies to ensure that they have enough products at all times, which means they can meet customer demand without overstocking their inventory.

To excel in the supply chain in 2022, below are certain steps that need to be taken:

1. Have a long-term strategy

The first step is to develop a long-term strategy for the supply chain. This will help ensure that all resources are used efficiently, leading to increased profitability and reduced costs. Set clear goals for your supply chain, then work backward from there to figure out what needs to happen to meet those goals. Know what your customers want, and make sure you’re delivering it—that means having open channels of communication with them so that you can ask questions and understand what they need and expect from their suppliers.

2. Well-organised warehouse operation 

The second step is to have a well-organized warehouse operation with an accurate inventory tracking system and good quality control measures. This will help reduce the number of stockouts, which are one of the main reasons for the loss in sales. Be aware of new technologies and their potential impact on your business.

In a world where everything is connected, we need to be able to track our products from their origin to their final destination. This means that we need to be able to identify each product and even where it was made. This is especially important in the automotive and pharmaceutical industries as any product delay can potentially affect the entire product category if it’s not handled correctly and can affect product quality during shipping or storage.

3. Open-end communication

The third step is to ensure good communication between buyers and suppliers so that orders can be placed on time without any delays or mistakes being made due to a lack of communication between these two groups within an organization.

In addition, it is also advisable for organizations to invest heavily in technology such as RFID (radio frequency identification). This would help them increase productivity by reducing manual intervention at every stage of receiving goods from vendors until they reach customers’ homes through delivery vans. It would make it easier for companies to handle large volumes without compromising quality due to increased automation levels throughout their supply chains.

4. Make Sure Your Supply Chain Planning Is Up-to-Date

Supply chains are constantly changing and evolving, so your company must keep up with the latest trends and developments. This means staying on top of any new technology that could affect how you manage your supply chain and paying attention to industry trends that may affect your ability to compete.

– Make sure your supply chain is environmentally sustainable and ethical.

– Minimise costs by optimizing inventory levels, transportation routes, and other factors.

– Attract customers who care about quality manufacturing processes.

– Increase customer satisfaction by ensuring that products arrive quickly and in good condition.

5. Integrate New Technologies Into Your Supply Chain Management Processes

  • The best way to stay competitive is by keeping pace with technological advancements that can improve your efficiency and effectiveness at every stage of the supply chain process—from manufacturing through distribution and fulfilment, all the way up until delivery of goods or services back into stores or homes.
  • Use new technologies such as artificial intelligence (AI) and machine learning (ML) to optimize decisions about where products should be manufactured based on demand and other factors.
  • Develop an approach for tracking and managing the carbon footprint of their products through all stages of the supply chain, from sourcing raw materials to transporting finished goods to customers.
  • Implement a program for tracking and managing waste throughout the supply chain, including manufacturing and retail.

Supply chain management has become more critical in recent years because:

-Increased globalization has made it necessary for companies to respond quickly to changing customer demands worldwide while reducing costs.

-Improved technology has made it easier to track inventory levels throughout the supply chain so that customers can get what they want when they want it.

-Growing customer satisfaction is an essential goal for many businesses today because satisfied customers are likely to return soon – which means repeat sales. 

-Supply chain management also allows businesses to integrate their production processes with those of other companies to produce more efficiently. This means that businesses can reduce costs by sharing resources, such as equipment or personnel, across multiple locations or businesses within their organization.

Learn more about supply chain planning in the supply chain and management and analytics course offered by Imarticus learning, which takes you through the key areas from which supply chain operations can be optimized for maximum efficiency. Book a call today to get started, and get certified from IIT Roorkee to start your supply chain management career.

 

Why choose a career in Derivatives Markets?

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Why choose a career in Derivatives Markets?

A derivative is a financial instrument whose value gets derived from another asset’s value. There are many different types of results, including options, futures, and swaps, and it is the marketplace where you can trade these instruments.

If you are looking for a challenging and exciting career, you should consider becoming a trader in the derivatives market. It is an incredibly fast-paced and dynamic industry, and it offers many opportunities for growth and advancement. This blog will discuss some benefits of choosing a career in derivatives markets. We will also provide information on how to get started in this field.

Why Choose a Career in Derivatives Markets?

There are multiple reasons you should consider choosing a career in derivatives markets. Here are some of the benefits of working in this industry:

  • The derivatives market is a fast-paced and dynamic industry.

The derivatives market is constantly evolving, and traders must think on their feet and make quick decisions. Derivative Market is a fascinating field to work in, and it offers many opportunities for growth and advancement.

  • The derivatives market is a global industry.

The derivatives market is a global industry, which means that traders can work anywhere in the world. It offers traders the ability to travel and experience different cultures while working from home or abroad.

  • The derivatives market is a lucrative industry.

The derivatives market is very lucrative, and traders earn high salaries and bonuses. It is an opportunity to make money while working in a fast-paced environment.

  • The derivatives market is an exciting industry.

The derivatives market is a fascinating field to work in, and it offers many opportunities for growth and advancement. It is an excellent opportunity for anyone who wants to make money while working in a fast-paced environment.

How to get started in the derivatives market?

The first step to getting started in the derivatives market is to gain some experience. The best method to do this is by interning or working as an assistant trader for a few years before becoming a full-time trader. There are many different types of assistant roles available, and each one has its responsibilities and skill set. 

The second step to getting started in the derivatives market is finding an internship or assistant trader position. There are several means to do this: ask friends for recommendations, apply directly through companies’ websites, network at job fairs, and attend financial industry events.

The third step to getting started in the derivatives market is studying finance and economics. It is not mandatory, but it will help you better understand how the financial markets work. 

Discover Investment Banking Courses Online with Imarticus Learning

This 180-hour CIBOP (Certified Investment Banking Operations Professional) course will comprehensively equip students for a career in investment banking operations, treasury, or clearing services. Learn investment banking by understanding complex securities and derivative products, their trade lifecycles, and the activities associated with investment banking operations.

Course Benefits For Learners:

  • Students at Imarticus Learning work in most of the world’s investment banks with the CIBOP accreditation, which the London Stock Exchange recognizes!
  • Our investment banking course covers complex securities, derivative instruments, and the trading lifecycles and functions of these assets.
  • Enroll in investment banking courses with placement assurances certified by India’s prominent academic institutions to impress employers and display proficiency.

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

Learn The Evolution of Data Science- From AI to ML

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Learn The Evolution of Data Science- From AI to ML

Data science is a discipline that draws from various fields of study, including artificial intelligence (AI), machine learning (ML), and statistics. The goal is to extract insights from large amounts of information, which you can use in various applications.

 Data scientists work with large datasets to solve complex problems by applying techniques based on these fields of study. In this post, we’ll explore the evolution of data science from AI to ML so you can better know what it means to be a data scientist today!

An Introduction to AI

AI refers specifically to technologies created using various approaches derived from machine learning techniques—for example, deep neural networks (DNNs), reinforcement learning agents such as neural networks with associative memory and temporal difference methods—and evolutionary algorithms such as genetic algorithms (GA).

AI is used in various industries and has become a vital part of our society. For example, AI is used in healthcare to help diagnose diseases, improve patient outcomes and reduce costs. In agriculture, it’s used to monitor crops and detect disease before it spreads. 

In education, it gets used as an assessment tool for students or teachers who need help with specific skill sets such as computational thinking or problem-solving. You can also apply this in transportation (for example, autonomous vehicles), financial services (credit scoring), or other areas where humans interact closely with machines.

An Intro to Machine Learning

ML is an approach to artificial intelligence (AI) in which computers learn without being explicitly programmed using statistical models based on historical data or user input. Machine learning uses algorithms that make decisions without human intervention or explicit programming instructions; its goal is to solve complex problems without requiring human intervention at every step.

The Evolution of Data Science- From AI to ML

The evolution of data science has been an exciting ride. From the early days of artificial intelligence (AI) to machine learning and deep learning, we have seen many advances in AI and ML over the past few decades.

The next level of evolution will get driven by another technology: artificial intelligence, or AI. We are already seeing some of the first signs of this new development.

Data science is an interdisciplinary field that draws upon principles from computer science, mathematics, statistics, physics, and engineering to help us understand the ever-increasing volume of data produced by our society.

Artificial Intelligence has been around for years, but it wasn’t until recently that machines could outperform humans in certain areas. The use of deep learning (DL) had enabled AI algorithms to make big leaps forward in accuracy and speed since 2015 when Google released DeepDream. This image recognition toolkit uses convolutional neural networks (CNN) trained with large datasets as part of its machine learning pipeline process recognition tasks such as image classification.

Explore a machine learning certification course with Imarticus Learning.

With the help of this IIT data science and machine learning certification course, students can launch their data science careers. Through this curriculum, students will learn artificial intelligence, data science, and machine learning fundamentals and the knowledge necessary to apply these ideas in the real world.

Course Benefits For Learners:

  • Students will learn Python in this five-month course, which IIT faculty members created to help them understand data mining and machine learning methodologies.
  • This data science course will be taught live online by the best instructors in India.
  • Our online data science program will help students build a strong foundation in data science.

 Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Read This Quick Guide Before You Go For A Data Science Interview 

Posted on by Imarticus Learning

Read This Quick Guide Before You Go For A Data Science Interview 

First of all, congratulations on receiving an interview invitation for your dream job!

This article covers all the important information you need to prepare for your upcoming interview. But first, let’s look at the meaning and importance of data science. 

“Data is information, and data will define the future,” Prime Minister, Narendra Modi, remarked at the Audit Diwas event. This demonstrates the possibilities of data science and machine learning. Today, every business needs people who can translate cutting-edge technology into usable data insights. 

As a result, there is a greater demand for experienced data scientists to assist companies in making various decisions. This is an excellent opportunity to join this burgeoning sector. So, study data science, apply for your dream job, prepare for the interview, and get started as a data scientist.

Roles in Data Science

It’s critical to figure out which role is best for you:

  • Data Analyst: The most popular choice for persons who learn data science is a data analyst. Pulling data from SQL databases, performing exploratory data analysis, managing Google Analytics, assessing A/B test results, and understanding excel are all common jobs for data analysts.
  • Data Engineer: Individuals who specialize in creating and developing data infrastructures for data-driven businesses with a high volume of traffic are called data engineers. Designing data models, implementing data processing systems, and administering SQL and NoSQL databases are examples of everyday tasks.
  • Data Scientist: A data scientist’s job profile combines the responsibilities, tasks, and activities of both a data analyst and a data engineer. 

Data Science Interview Guide

  • Programming: Good programming abilities are mentioned in every job description as an eligibility requirement because no data science position is complete without the ability to alter data. Almost every technical interview begins with a programming question.
  • SQL skills: Every analyst is expected to be able to extract data from a relational or non-relational database. Several analyst job descriptions expressly call for experience developing complicated SQL queries to collect data. To crunch datasets, one should be familiar with Python libraries such as NumPy, pandas, and scipy.
  • Machine learning: Machine learning is a crucial topic to cover for data science jobs at the middle and senior levels. Algorithms, linear regression, machine learning problems, decision trees, and other topics should be covered.

Common Interview Questions

Preparing frequently asked data science interview questions is the best way to answer difficult questions and get your dream data science job. Following are a few commonly asked questions that you can prepare for:

  • Why do you want to enter the data science field?
  • What are the various ways to extract valuable insights from databases?
  • What are all the steps to make a decision tree?
  • How to use recommender systems in data science?
  • What all data science courses have you completed?

One of the best ways to learn and start a career as a data scientist is to complete the best data science courses.

Post Graduate Program (PGP) in Data Analytics and Machine Learning

The PGP in Data Analytics and Machine Learning is a nine-month part-time weekend-based working professional program. A six-month full-time program offered on weekdays is another option for completing the course. This program teaches you to apply data science in the real world and construct predictive models that improve business outcomes. 

This guaranteed job assurance program is excellent for recent graduates and professionals interested in pursuing a career as a data scientist. The PGP in Data Analytics and Machine Learning programs has the following benefits:

  • The data science course offers an assured placement program where students get interview calls and placement opportunities from top data science companies.
  • It follows an industry-oriented curriculum that companies around the world accept.
  • It is a perfect blend of practical and theoretical knowledge, where hands-on training is provided to students through various platforms and real-world case studies.

Conclusion

Data science is an ever-growing industry with ample job opportunities for fresh graduates and working professionals. Suppose you want to enter this field, pursue a data scientist career, complete various data science courses, and prepare well for the interview. The PGP in Data Analytics and Machine Learning can help you learn essential skills and topics related to data analytics, machine learning, and data science. So, join the course and delve into the world of data.

Contact us now, or visit one of our training centers in Mumbai, Thane, Pune, Bengaluru, Delhi, Chennai or Gurgaon for queries related to the PGP in Data Analytics and Machine Learning.

Related Articles:

The Perfect Guide To Understanding The Data Science Career Path
Transitioning to Data Science: How To Get There?
The Rise of Data Science in India: Jobs, Salary & Career Paths in 2022
Top 5 Trending Jobs In The Data Science Industry

How a supply chain management training is proving helpful during the pandemic

Posted on by Imarticus Learning

How supply chain management training is proving helpful during the pandemic

As the world got rocked by an unprecedented pandemic, the supply chain management course proved to be one of the best decisions for some students. With this type of training, businesses are more prepared to ramp up their production and need a healthy workforce to handle it. In this blog post, students will learn the importance of a supply chain management career. So, let us start with the fundamentals of supply chain management.

What is supply chain management?

Supply chain management is a network of entities involved in the process that begins with raw materials and leads to production, warehousing, distribution, and marketing. The goal is for distributors to get products into the hands of consumers efficiently by reducing costs while also maintaining product quality at all times. 

It is where supply chain management training comes in!

Supply Chain Management Training: Why it’s important?

SCM is a thorough understanding of demand and inventory management. How stock prices fluctuate based on market movements are just reasons companies should invest in staff members who have undergone supply chain management training.

Typically, students interested in this type of career can complete a Supply chain management online course that focuses extensively on business and marketing principles. Students will learn how processes can be improved by effectively providing resources at each stage of the distribution process, including manufacturing, warehousing, transportation, marketing, and distribution.

How A SCM Training Is Helpful During Pandemic

The importance of supply chain management gets neglected, but it is an integral part of any organization’s success. Good supply chains focus on internal processes and external factors such as suppliers, product development, finance & accounting, etc. They drive customer satisfaction by lowering risk exposure from poor quality or discrepancies with stock levels that might result from errors during production or logistics operations.

 The time when products arrive at their end destination should be minimum and consistent with the manufacturing time.  The quality of products needs to match customer expectations. The supply chain must reduce risk exposure caused by external factors such as poor weather conditions. It might affect transport costs or delays due to political events or strikes. 

 In many cases, this is important when companies lack capacity or because it makes sense from an operational cost-savings perspective. Since purchasing power may still help drive down costs and improve the bottom line. It is also an essential aspect of supply chain management. 

Why Enroll in SCM Program at Imarticus Learning

The Professional Certification in Supply Chain Management online course and Analytics is created in partnership with DoMS and E-learning Centre, IIT Roorkee, and industry professionals. Imarticus Learning aims to equip candidates interested in entering the operations and supply chain business with cutting-edge experience.

Supply Chain Analytics allows management to make data-driven choices at the strategic, operational, and tactical levels. There is a scarcity of experts with the process and analytical skills in the Supply Chain Management area. This credential prepares students for jobs such as Demand Planner, Data Scientist, Supply Planner, and Supply and Operations Planner, which are in high demand.

Some course USPs:

  • The Supply chain management course lets the students learn job-relevant skills that prepare them for an exciting Supply chain management career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations –  IIT Roorkee and Imarticus Learning.
  • World-Class Academic Professors to learn from through live online sessions and discussions. This will help students understand the practical implementation with real industry projects and assignments.

Introducing Certification in Artificial Intelligence and Machine Learning in Collaboration with E & ICT Academy, IIT Guhawati and Imarticus Learning

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Introducing Certification in Artificial Intelligence and Machine Learning in Collaboration with E & ICT Academy, IIT Guhawati, and Imarticus Learning

Machine learning is powered by AI. With ML, we can power programs that are easily updated and modified to adapt to new environments and tasks- resulting in quick progress on difficult projects. ML and AI are almost synonymously used in tandem when it comes to the latest tech trends. And, AI has disrupted many industries forever – like SaaS, Manufacturing, Defense, Analytics, public sector, and so on.

If you learn machine learning through the best Machine Learning & AI course, you are likely on a fast and high-growth career trajectory.

Here is why!

#1. ML & AI Are Skills of the Future

Skills in ML can affect your long-term employment prospects because the field is projected to experience rapid growth.

With the emergence of advanced technologies like the Internet of Things (IoT), Machine Learning is experiencing a certain surge in demand and popularity.

If you learn machine learning by taking up data science certifications, there is an increase in the probability that you will have better job prospects as compared to someone without ML skills.

#2. Solve Real-World Problems

There is a lot of talk about how AI will replace jobs, but the truth is it will create new job opportunities. As an ML engineer, for instance, you get to work on projects that have a big impact in the real world and lead to business solutions that are meaningful.

#3. Versatile Growth Opportunities in the Data Sciences

Machine learning and artificial intelligence (ML &AI) skills are beneficial to a data science career. Both positions give you the opportunity to let your knowledge of both fields expand. Switching back and forth between roles can quickly enable you to become an invaluable resource in today’s rapidly changing world.

You want to have an advantage as early as possible so that you can learn about solving new and undiscovered problems. When the time comes, these skills will be in great demand and allow you to secure a career path on the rise.

What does a growth path in this domain look like?

Typically, a machine learning engineering career path begins with being a Machine Learning engineer. Machine learning engineers develop applications that automate common tasks previously done by humans and take care of the repetitive ones for humans to do efficiently without error.

When you earn a promotion as an ML engineer, you are then promoted to be an ML architect! Their responsibility is to develop and design prototypes for applications that need development.

There are a few other roles in the field which one could take on as well. These include ML data scientists, ML software engineers, senior software architects, and more.

New tech arenas keep developing into this space. So, keep your interest, skills, and industry demands aligned for the best growth!

How to make a successful career in AI & ML?

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