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Category: Analytics

Become A Pro By Managing Uncertainties In A Supply Chain

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Become A Pro By Managing Uncertainties In A Supply Chain

Supply chain risk management is a critical aspect of any business. It helps minimize disruptions’ impact and improve the supply chain network’s overall efficiency. However, some companies are still reluctant to use supply chain risk management techniques and instead rely on ad hoc responses or reactionary measures when there is an issue. 

How do deal with uncertainties in a supply chain?

Supply chain managers can use several techniques to deal with uncertainties in a supply chain. These techniques are:

  • Manage the risk: Supply chain managers can take steps to manage the risk arising from an uncertain event, such as by increasing inventory levels or by changing suppliers if the existing one fails to meet quality standards.
  • Mitigate the risk: In a few cases, it may be possible for a supplier to mitigate risks that otherwise would have led to a disruption in operations during an emergency or natural disaster.
  • Transferring risk: If you cannot prevent certain risks from occurring but are willing and able to assume them yourself instead of passing them along through insurance coverage (e.g., weather-related incidents). Therefore, consider transferring these risks onto yourself instead of trying insuring against them directly through another service provider like an insurance company (unless this makes sense given other factors involved). 

Types of risks in the supply chain

  • Financial risks are the most obvious and affect the supply chain’s ability to generate revenue. For example, there will be no revenue if you can’t sell a product because it isn’t available in stores.
  • Supply chain risks include production delays or quality issues that prevent you from fulfilling orders on time. They also have physical damage caused by external forces during shipment (e.g., bad weather).
  • Operational risks are more subtle than financial or supply chain risks and can affect your company’s ability to perform its functions properly—however indirectly this may seem at first glance. For example, poor communication between departments within your organization could lead to duplicate purchases of materials needed for production.

The future of supply chain management is likely to be driven by uncertainty and will require companies to develop new strategies for dealing with it. Fortunately, many tools are also available to help you manage them effectively.

In conclusion, we can say that the future of the supply chain is bright. With the help of technology and proper planning by companies, it’s easy to avoid any risk and ensure smooth functioning at all times.

Discover a supply chain management career with Imarticus Learning

Students interested in the SCM course can get the most recent information by completing the supply chain certification course.

Course Benefits For Learners

  • To provide students a complete understanding of the sector and position them for a prosperous future as certified Supply Chain analysts, we include them in significant technologies and initiatives, including six real-world projects.
  • Students may prepare for highly sought-after occupations like demand planner or data scientist, which are in high demand among firms today, by completing supply chain analytics courses!
  • Aspirants will learn to become data-centric and improve Supply Chain decision-making using the Python programming language.

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

The logic behind reading programming language in supply chain analytics course training

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The logic behind reading programming language in supply chain analytics course training

Coding is an excellent option to improve your analytical and business thinking skills. With the right tools, you can become more productive and efficient in your work. This post will discuss which programming language is best for supply chain analytics course training and why learning these languages will help improve your analytical skills!

An overview of supply chain management course

Supply chain management is the process of managing a company’s supply chain. It involves identifying, purchasing, and delivering products or services to meet customers’ needs.

Supply Chain Management (SCM) refers to all activities involved in bringing raw materials from suppliers to end users and related information about these transactions. It includes planning for future requirements and monitoring performance at every stage of the product life cycle through stages, including production, distribution & sales, etc., to ensure efficient operation within all aspects, including cost reduction, improved quality control, etc.

The best logic behind learning R, Python, and SQL languages during supply chain analytics course training

R is a free alternative to the proprietary Microsoft® Excel®. Python is another open-source programming language that you can use for data analysis, manipulation, and automation of your business processes.

SQL is Structured Query Language, a vendor-neutral database query language developed by IBM in the 1970s under their System R project (now called DB2). It has been widely adopted by most major manufacturers today, including Intel®, Oracle®, Microsoft® SQL Server®, etc., so if you want to learn what kind of databases are out there, then this course will help you understand them better!

Which programming language to learn?

It is essential to choose the correct programming language for you. Here are some things to consider:

  • R – This is the most popular programming language in the supply chain analytics field.
  • Python is a general-purpose programming language that you can use for data science and machine learning tasks such as prediction, classification, or regression models.
  • SQL – This database management system has been around since the 1960s, making it one of the oldest languages today (along with COBOL). It’s also known as Structured Query Language (SQL) because it’s an order-by-clause syntax used when working with databases like MySQL or Oracle Data Vault.

Learning programming languages is just one of the many ways that will help you improve your analytical and business thinking skills!

Discover a supply chain management career with Imarticus Learning

Managers might use supply chain management online training to make strategic, operational, and tactical decisions. By combining conventional supply chain planning with data-driven decision-making, get ready to contribute to developing more tactical and inventive global supply chains in the future.

 Course Benefits For Learners:

  • Students are exposed to significant technologies, including six real-world projects, to give them a thorough grasp of the industry and prepare them for a successful career.

  • Learners may get ready for highly sought-after jobs like demand planners or data scientists by finishing a supply chain management and analytics degree!

  • Python will teach aspirants to become data-centric and enhance supply chain decision-making.

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

The Most Influential People In The Data Analytics Industry

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The data analytics industry is growing at an exponential rate. Simply put, there is a lot of innovation and creativity in extracting knowledge from any data to make decisions based on past performance or future possibilities. Many professionals are driving the industry’s evolution by seeking and employing the best practices in this sector. This post lists the most influential people in this field.

Rohit Tandon, vice president and strategy WW head of HP Global Analytics

He is an experienced business leader well renowned for inventing and launching new companies into success. HP Global Analytics expanded under Rohit’s direction from a small team to a sizable analytics company. Global Analytics is now tasked with leading HP’s analytics delivery ecosystem and bringing together related departments to generate innovations that support HP’s corporate goals as a member of the Corporate Strategy team.

Pankaj Rai, Director of Global Analytics at Dell

The Director of Dell Global Analytics is Pankaj Rai (DGA). Pankaj has worked for Dell for around eight years and DGA for over five years. Before this, he supervised all strategic and corporate planning-related efforts for Dell in India while working with the office of the president of India. In this capacity, he was in charge of aiding Dell in diversifying and expanding its footprint in India and serving as Dell’s external representative in industry forums.

 Sameer Dhanrajani – Business leader, Cognizant Analytics.

As the head of Cognizant Analytics’ end-to-end business spheres, Sameer is in charge of developing differentiated strategies for the company’s analytics consulting, platforms, and services. He is also responsible for producing best-in-class GTM, business development, operational excellence solutions exercises, and transformational analytics engagements.

Amit Khanna- Partner at KPMG 

Amit has also put in a lot of effort to help organizations strengthen their analytics capabilities. He has spent much time developing numerous colleges’ analytics and data scientist curriculum. In addition to working with significant international customers to create their analytics organization and adopt a fact-based culture, he individually has two analytics patents.

Anil Kaul, co-founder, and CEO of Absolutdata

Dr. Anil Kaul is a well-known authority in the field with more than 16 years of expertise in marketing research, strategic consulting, and quantitative modeling. Over his four years at McKinsey & Co. in New York, he provided consulting services to over 20 Fortune 500 businesses. 

Learn Data Analytics with Imarticus Learning

This is the best data analytics certification course with a placement that will help you learn data science in the real world. Students will develop sophisticated models that yield crucial business forecasts and insight.

Course Benefits for Learners:

  • Data analytics, machine learning fundamentals, and the most widely used data science tools and approaches should all be familiar to students.
  • To receive a tableau certification, students must finish 25 real-world projects and case studies directed by corporate partners.
  • One of the sought-after abilities in the market today is the ability to visualize data by utilizing data analytics online training. Therefore, recent graduates and those just starting their careers might consider enrolling.

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

Lessons learned from my career as a supply chain manager

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Lessons learned from my career as a supply chain manager

The world has been remarkably affected by the COVID-19 pandemic since 2019. It affected all industries positively and negatively, including supply chains. It tested the limits of supply chains while highlighting its weaknesses that caused massive disruption in operations. About 94% of fortune companies experienced supply chain disruptions during COVID-19. Though the situation is becoming normal slowly, this period left us with some vital supply chain lessons that will be applicable now and in the future:

Importance of Visibility:

Supply chain managers should have end-to-end visibility of the supply chain. It helps understand issues in the supply chain immediately. The vast and profound analysis of the supply chain can play a crucial role in different unexpected scenarios and determining the right response to minimize the impact on the supply chain. Thus, near-future plans contribute to corrective actions. Supply chain market research claims that most (around 69%) companies do not have end-to-end visibility, affecting their decision abilities. Visibility is the baseline to understanding the supply chain and optimizing operations; thus, never underestimate the visibility in supply chain management. It is time to opt for new technology to help manage and track the supply chain fr source to destination at each node for all companies to avoid disruption in operations.

Keep an Eye on New Suppliers:

Companies have always preferred low-cost suppliers, but the COVID-19 pandemic has given us some crucial lessons about suppliers. According to the Foley & Lardner LLP Global Supply Chain Disruption and Future Strategies Survey, about 70% of companies agreed that supplying from the lowest-cost suppliers will not be the sole criteria while making decisions on new or existing suppliers. Instead, companies should focus on more reliable and flexible supply chain procedures to ensure continuity in the supply chain.  

Importance of Better Collaboration with Suppliers:

The pandemic revealed poor supply chain collaboration, negatively impacting businesses worldwide. To collaborate better with suppliers, companies had to eliminate traditional ways of keeping records and tracking, such as spreadsheets, email threads, and PDFs, and instead embrace collaboration platforms. These platforms make the supply chain transparent, optimized, and streamlined among stakeholders. About 100+ large companies across sectors agreed in the McKinsey survey that regular collaboration with suppliers results in higher growth, greater profitability, and lower operating costs.

Analysis and Forecasting:

Managing demand and supply in the company operations needs a supply chain manager to analyze the current and future demands accurately and forecast the smooth supply. There are various methods and techniques to anticipate and prepare for the future. Data-driven and systemized analysis and predictions help smoothly resolve issues in the supply chain. It also enables us to compare multiple predictions and renegotiate rebate agreements.

Configurability for Better Adaptation to Speed Disruption:

Though it is important to analyze and predict future needs, it’s equally important to be able to reconfigure the needs of the supply chain. Thus, supply chain managers should have enough contacts with suppliers, good management skills, the right operational capabilities, and reconfigurability to adapt to changing business needs.

 Connectedness:

Companies having different technologies to manage supply chains through multiple platforms should share data and keep it accessible for connectedness and end-to-end visibility at any point in time. It provides information on how employees should manage operations and iterate production plans based on supply chain conditions. Connectedness improves efficiency, agility, growth, and production. 

Ethics and Responsibility:

Consumers and people account for businesses for the safety and well-being of society. Nowadays, companies are conscious of making the right choices about ethics, sustainability, and social responsibility. Supply chain management plays a critical role in environment-friendly procurement and supply. Various technologies are used in modern businesses to obtain visibility of raw source materials.

Supply chain management is a challenging, simultaneously interesting domain if any of you are thinking of a career in supply chain management. As the modern world is based on digital technology, it will be beneficial to do a supply chain management certification course before you enter the industry. Also, if you are already a part of the supply chain, you can always upgrade and update your knowledge with such courses. Contact us or visit any of our training centers in 

Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad to get more information on the supply chain management certification course and supply chain analyst online training.

A guide to understanding Tableau concepts

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A guide to understanding Tableau concepts

When you analyze data in Tableau, you have a choice. You can either choose to display the data continuously or discretely.

Continuous – A continuous chart is easy to read. The bars or lines in the chart never indicate a break in the data. If you’re comparing two or more sets of data, the continuous manner of displaying the data is the best choice. If you want to see how one number affects another, this is the best way to do it.

Discrete – A discrete chart is one in which the data is broken into discrete bars or lines. This is the best choice when you want to compare two or more sets of data. It’s also a good choice when you want the viewer to know that there is a difference. This may be something like comparing the sales numbers of two stores.

What are row level, aggregate level, and table level in Tableau?

This is probably the most confusing aspect of Tableau for beginners.

By row level, we mean that the data is organized in rows.

By aggregate level, we mean that the data is organized at the level of aggregates.

For example, we have company names and numbers at the row level. At the aggregate level, we would group the numbers under one name.

Tables can add visual interest and can provide another layer of data that can be used to understand your data further.

At the table level, we would join all the data from the previous examples under one name, “Technology.” This name could be a column in the table or a filter on the view.

A quick overview of Tableau features

Decision Trees – A powerful tool you can use to see the relationship between different data sets. For example, let’s say you want to see what your customers like the most about your products. A decision tree will help you visualize this data. You can either use Tableau’s built-in decision tree tool or create your own.

Visual Crosstabs – This is a great way to create a table that shows the breakdown by category or by a specific value in a column. With this table, you can compare two columns or two columns and a value.

Heatmaps – This is a great way to visualize your data like the crosstabs. All you need to do is select a measure and create a bar chart. Then, you can add a visual pattern to the bars.

Time-Series Charting – This is great for visualizing a history of data. You can use an actual time series or create a visual that spans multiple data points.

Interactive Charts – This is the blending of interactive and visual data. You can use this feature to create dashboards that support complex queries and deliver dynamic visuals.

Advanced Reporting – Tableau’s reporting features are some of the best out there. You can create custom reports or use their built-in report templates.

Linking to Data Sources – Like many data visualization tools, Tableau allows you to connect to external data sources. You can access your SQL database, Excel spreadsheets, Amazon machine learning model, or Google Spreadsheets.

What are the Dimensions and measures in Tableau?

When you create a visual in Tableau, you are creating a visual using dimensions and measures. A dimension is a way to categorize your data. Let’s say you have a marketing team that tracks different metrics related to their work. You could create a dimension to store this information. Let’s say you create a dimension called “Metrics” that contains the following information:

Tableau features

You can integrate Tableau with many data sources, including SQL databases, Excel spreadsheets, Google Spreadsheets, Amazon Web Services, and more.

– Tableau has a free software product and a low-cost subscription product.

– Tableau’s subscription-based product has more functionality.

– Tableau can be used for data discovery, analysis, reporting, and visualization. – Data sources can be internal or external.

– Tableau can be used for self-service BI or for managing a BI project.

– Tableau can be used for data hygiene, wrangling, quality, modeling, and more.

– Tableau is available on all major operating systems, including Windows, macOS, and Linux.

– Tableau is highly customizable and offers different charting and visualization styles.

With this knowledge, we know you can get an idea of data visualizations when using business intelligence software like Tableau. So what are you waiting for? Apply for an Imarticus Learning course today by visiting our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad or contact us through chat support.

Switching careers to data science post-pandemic? A certificate program in data science and machine learning will help

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Switching careers to data science post-pandemic? A certificate program in data science and machine learning will help

We all know how the COVID-19 pandemic has shaken social norms and reshaped business practices, impacting some industries more than others.

Moreover, the pandemic has forced us to grow accustomed to learning everything online. So, why not use it to advance our careers in new and exciting ways?

If this sounds interesting to you and you plan to make a career switch to the field of data science, read on.

The field of technology, especially data science, is among the few areas that saw an increase in job opportunities during the COVID-19 pandemic.

This increase in opportunities was because all businesses were compelled to go digital. Their dependence on understanding and interpreting data became essential during the lockdowns and work-from-home setups.

Since going completely digital is the ‘new normal,’ the main focus of all businesses now is on ways to revitalise themselves and gain momentum with data’s help.

The phenomenon will help them better understand customer behavior and derive meaningful insights from them. 

The demand for data science specialists is thus rising, even though supply is somehow limited. Due to this severe talent shortage, there is a golden opportunity for graduates, software engineers, and even novices to switch to the field of data science. 

For a seamless career shift to data science and machine learning, you can apply to Imarticus Learning’s certificate program in data science and machine learning, designed in collaboration with iHUB DivyaSampark at IIT Roorkee. 

Data Science And Machine Learning Career Paths

We have discussed in detail how you can make a career switch from different fields to the sphere of data science and machine learning.

  • For Individuals with a Bachelor’s degree in Mathematics, Statistics, and Computer Science

If you have a degree in mathematics, statistics, or computer science, you have an excellent chance to succeed in the rapidly evolving field of data science.

However, even if you have a strong foundation in mathematics and statistics, or have basic coding experience because of your computer science background, simply being a graduate will not help you secure a job as a data scientist.

To stand out and advance your career in data science, you must learn and have work-ready experience in different programming languages, including R, SAS, Python, Tableau, Hadoop, and Spark.

You should also improve your ability to work collaboratively with other developers on GitHub, learn about cloud-based model deployment, and be familiar with Docker and Docstrings.

Our data science and machine learning course combines comprehensive case studies, theory, and hands-on experiential learning and is ideal for recent statistics, computer science, and mathematics graduates.

With the help of this certification course, you can surely get all the features and facilities mentioned above right from the comfort of your home.

  • For Individuals with a Bachelor’s degree in Other Disciplines

Suppose you are a novice with a degree in a course unrelated to data science, such as commerce, business administration, or medicine.

In that case, your odds of finding a data science job are relatively low compared to a computer science graduate or employed software engineer.

However, you can do away with this disadvantage by taking a data science course. All you need is hard work and the ability to learn quickly. You would be happy to know that many technology companies prefer self-taught data, science professionals.

Beginners in the field are also encouraged to join our 5-month online course, as it equips them with the practical knowledge required to make data-driven decisions and secure their dream job quickly.

Benefits of Our Certificate Program in Data Science and Machine Learning

Following are some key benefits that our students enjoy after getting enrolled in this course : 

  • The Imarticus Learning Data Science and Machine Learning Course are designed to help you begin your data science and machine learning journey, regardless of your level of knowledge in statistics, analytics, or coding.

  • This programme has been created in collaboration with iHUB DivyaSampark @IIT Roorkee. It will teach you the fundamentals and forms of information science and machine learning. Moreover, it will also provide you with the necessary knowledge to implement and apply these concepts to real-world problems.

  • The course has a holistic curriculum full of projects and exercises to help you get all the necessary practical exposure and hone your job-relevant skills.

  • In this 5-month programme designed by renowned IIT faculty members, you will learn to use data mining and machine learning tools with Python. You will also learn how to use data-driven insights to impact organisational growth positively.
  • You will get additional chat support and undergo online career counselling sessions.
  • The course is scheduled for weekends, so you can complete it at your own pace and with the utmost comfort.

You can advance your data science career by enrolling in our Data Scientist course to make a switch to job roles like data scientist, business analyst, data analyst, or machine learning engineer.

Final words

We at Imarticus Learning have validated and customised applied learning solutions ideal for beginners and working professionals who want to update their skills and data science knowledge.

So, if you are looking for a quantifiable program offering practical knowledge through engaging sessions and project work, check out our course.

This helps make a robust career in the field of data science and make a swift switch to it. To know more, contact us through chat support or visit our nearest training centers in Bengaluru, Chennai, Ahmedabad, Thane, Pune, Mumbai, Gurgaon, or Delhi.

Top 5 Commonly Used Supervised Machine Learning Algorithms

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Top 5 Commonly Used Supervised Machine Learning Algorithms

Machine learning algorithms can let machines do surgery, play chess, and become more intelligent and human-like. We are in an era of continual technological advancement, and by seeing how computers have developed through time, we may make predictions about what will happen in the future. 

The democratization of computer tools and methods is among the revolution’s key distinguishing characteristics. Data scientists have created powerful data-crunching computers during the last five years by effortlessly implementing cutting-edge methodologies. The outcomes are astonishing.

Supervised machine learning algorithms are the common ways to solve problems in supervised classification. Supervised machine learning algorithms operate on both sets of data by finding patterns within supervised learning algorithms to classify new unseen unlabeled datasets.

5 COMMON MACHINE LEARNING ALGORITHMS 

 

  • Linear Regression

 

One of the well-understood algorithms in statistics and machine learning is linear regression. At the price of explainability, predictive modeling primarily focuses on reducing a model’s error or producing the most precise forecasts. 

 

  • Logistic Regression

 

Don’t be misled by the name! It is a classification method rather than a regression one. Based on a collection of independent variables, it estimates discrete values (binary values like 0/1, yes/no, and true/false) (s). It determines the likelihood that an event will occur by fitting data to a logit function. It is known as logit regression as a result. As a result of predicting the likelihood, its output values range from 0 to 1.

 

  • K-Means

 

It is a technique for unsupervised learning that addresses clustering issues. Data sets get divided into a certain number of clusters— let’s call it K—in such a way that each cluster’s data points are homogeneous and heterogeneous from those in the other clusters.

 

  • Decision Tree

 

Nowadays, a widely used machine learning algorithm is the decision tree algorithm, a supervised learning technique used to categorize issues. For both categorical and continuous dependent variables, it performs well when categorizing. 

 

  • Dimensionality Reduction Algorithms

 

Businesses, governments, and research institutions store and analyze enormous volumes of data in the modern world. As a data scientist, you know that a wealth of information gets included in this raw data; the difficult part is identifying important patterns and variables.

Explore Machine Learning and Artificial Intelligence Course with Imarticus Learning.

Attend the E & ICT Academy deep learning Artificial Intelligence certificate program to discover how to become an AI engineer. This machine learning certification will help students prepare for careers as data scientists, data analysts, machine learning engineers, and AI engineers.

Course Benefits For Learners:

  • To get practical industrial experience and prepare for a fulfilling data science career, students work on 25 real-world projects.
  • You may amaze employers and demonstrate your talents with a certificate authorized by the E & ICT Academy, IIT Guwahati, and an Imarticus Learning-endorsed credential.
  • This artificial intelligence course will help students land lucrative jobs in machine learning and artificial intelligence.

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

A step-by-step guide to building a data science portfolio

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In the field of data science, it is integral that you have an impressive portfolio if you want to get a good job according to your skill level. Employers are constantly sceptical about people lying on their resumes and that is why they require the candidates to have proof of the work they have done previously before they hire them. You might have a lot of experience in the field as an intern or as an employee. But to have a good career in data science, you will have to convey that well to your future employers, and that is where a good portfolio comes in. 

Why is a Good Portfolio Important for a Data Science Professional? 

You might be wondering why is a good portfolio so important in the field of data science. The answer is pretty easy: because that is what employers are looking for. A portfolio is used to demonstrate your skills and all the certification course in data science, or work you have done previously. It impresses employers when they see that you not only have the knowledge, skills and certifications but also hands-on experience in the field. This boosts their confidence in your capabilities and also makes the hiring process much easier. They will interview you, but having a good portfolio will impress them and also make it clear to them that you take this career path seriously and that you have the necessary skills to back you up. That is why, if you are looking to get a job in the data science industry, you should invest in a good portfolio that will highlight your skills and experiences. 

Some Tips and Tricks to Make Your Data Science Portfolio Outstanding

Now that we have explained why you need to have a stellar portfolio to build a great career in data science, let us talk about how you should go about doing that. In simpler words, what are some of the ways to make your portfolio stand out from the rest and make sure you get the job you are intending to.

Exhibit Your Technical Skills 

As you can understand, this is probably one of the most important parts of building a great portfolio. You have to include the important technical skills that you have in your portfolio, but that does not mean you have to add all of them. You can add the more important ones and the ones that you feel are more important for the job roles while leaving the simpler, normal and cookie-cutter skills out of it. This shows off your better skills and makes your portfolio more attractive. 

Be Passionate 

Being passionate is important as it can persuade your employers. In the data science sector, it is important for employers to see that you are passionate about what you do. It tells them that you will take the job more seriously. 

Personal Brand 

Your portfolio is only a part of your identity. Your employers can find out a lot about you through your social media, or LinkedIn and GitHub profiles. Make sure that you link all of them in your Resume and portfolio to make your personal brand appealing to them as well. Your profiles should also convey your professionalism and passion for your chosen career path. 

Conclusion 

Now that we have covered all the bases about how to build a good portfolio, let us talk about how you can improve your skills and learn data science. You can add some amazing data science and machine learning certifications to your portfolio to make it more attractive to your employers. The IHUB IIT Data Science certification course which also teaches machine learning, will help you improve your portfolio for your next job. 

The Future of IIT supply chain management is Promising

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The Future of IIT supply chain management is Promising

Any business must include supply chain management (SCM) in its operations. It consists of all commercial operations from beginning to end, such as the transformation of raw resources into finished goods before they are shipped and delivered to customers. Given that supply chain management is one of the fields with the greatest rate of advancement, one can enroll in supply chain management training online courses such as the IIT supply chain management course to gain the necessary skill sets.

What is Supply Chain Management?

Supply change management is a complex framework or system that businesses of all sizes and in all industries use to handle and manage the many processes involved between product development and consumer delivery.

Some essential elements of supply chain management include resource acquisition, manufacturing, inventory management, demand management, supply management, product portfolio management, and sales and operational planning.

Why Choose a Supply Chain Management Career in India?

The new fad in the industrial scene in India is supply chain management. For those thinking of pursuing a Supply Chain Management career, they can begin with the help of supply chain management training online such as an IIT supply chain management course.  

best supply chain management and analytics courses

Additionally, the variety of career roles available in the industry of supply chain portrays it as an extremely alluring field for young prospects. Here are some reasons for choosing a career in supply chain management:

It Makes Growth Possibilities and High Job Satisfaction Promises

Jobs in the logistics and supply chain sectors are becoming more and more available as a result of the sector’s rapid expansion. You may rest confident that the supply chain industry offers lucrative positions and the best possible application of your expertise and abilities. Supply chain managers in India can make up to Rs. 859,249 LPA.

It has Comparatively Few Barriers to Entry

The nicest thing about pursuing a career in supply chain management remains that the logistics and supply chain sector offers talented professionals plenty of room to earn good salaries even without the need for advanced degrees. 

As for the candidates who have an undergraduate degree, technical training, and professional certificates, you can land supply chain profiles. You may be qualified for positions such as procurement analyst, logistics manager, operations research analyst, supply chain manager, purchasing manager, director of operations, senior consultant, etc. with a supply chain management certificate.

You Can Work Both Locally and Abroad

There are many supply chain occupations, both domestically and abroad. As a result, you can select employment profiles locally or regionally within the nation. And if you enjoy occupations that demand travel, many supply chain executive positions occasionally call for domestic and international travel.

You Have a Mixture of Industries to Pick From

As we previously stated, the supply chain is crucial to practically every sector. Regardless of their size, businesses and organizations are now funding supply chain management to streamline operations, broaden their market appeal, increase sales, and improve the general client experience. You can therefore work in any field with the help of a degree in supply chain management, such as manufacturing, retail, eCommerce, healthcare, oil & gas, the automobile, etc.

You Pick Up A Lot Of Communicable Skills

The moment you work in the business of supply chain, you understand and develop a wide range of abilities that you may use in a variety of supply chain professions and even in other fields. Financial planning, forecasting and research, workflow improvement, general company management and governance, national and international business conventions, and lastly, mechanical aptitude are some of these talents. 

Conclusion

The demand for qualified and certified supply chain specialists will rise in tandem as more businesses recognize the real potential of supply chain management. The worldwide supply chain management market has a lot of job prospects and is predicted to increase dramatically in the coming years.

How To Master Data Analytics In 6 Simple Steps

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How To Master Data Analytics In 6 Simple Steps

Data analysis follows a strict, sequential method, much like any scientific profession. Each step calls for a unique set of abilities and knowledge. But it’s crucial to comprehend the process as a whole to gain relevant insights. A solid foundation is essential for developing outcomes that withstand examination. This post will examine the key phrases in the data analysis process. This will cover how to specify your objectives, gather information, and conduct an analysis. 

Step 1: Defining the question

Identifying your purpose is the first stage in any data analysis procedure. This is frequently referred to as the “problem statement” in the context of data analytics.

Creating a hypothesis and planning how to test it is a necessary step in defining your purpose. Start by determining what business issue you’re attempting to resolve. Although it may seem simple, it might be more complicated. A data analyst must thoroughly understand the business and its objectives to frame problems appropriately.

Step 2: Gathering Data

This leads to the following action, data gathering. It’s time to specify the data you’ll need to get the answers to your queries now that you have a clear notion of them. You should check to see if the information is easily accessible within your company, such as through employee survey results or yearly performance assessments.

Step 3: Data Cleaning

The next step after collecting your data is to prepare it for analysis. Ensuring you are dealing with high-quality data requires cleaning the data. Getting rid of significant mistakes, duplication, and outliers—are all issues that always arise when combining data from several sources. Adding structure to your data—general “housekeeping,” such as correcting spelling mistakes or layout flaws, will make it easier for you to map and handle your data.

Step 4: Analyzing the data

You’ve finally cleared up your info. The fun part will now be examining it. Your aim will significantly impact the sort of data analysis you perform. But there are a lot of methods out there. Regression, time-series, and univariate or bivariate analysis are a few more you may be familiar with. But how you use them is more crucial than the wide varieties. What insights you hope to get will determine how to proceed. 

Step 5: Sharing your results

You’ve completed all of your analysis. You possess unique insights. Sharing these insights with the general public is the last phase of the data analytics process. This includes more than just disclosing the unprocessed results of your study; it also entails interpreting the findings and communicating them in a way that is understandable to various audiences. Because you’ll frequently present information to decision-makers, your insights must be completely transparent and unambiguous. 

Explore a career in data analytics with Imarticus Learning

customized corporate training

 This data analytics course with placement will help you learn data science in the real world. Students will develop sophisticated models that yield crucial business forecasts and insight.

Course Benefits for Students:

  • Data analytics, machine learning fundamentals, and the most widely used data science tools and approaches should all be familiar to students.
  • To receive a data analyst certification course, students must finish 25 real-world projects and case studies directed by corporate partners.
  • One of the sought-after abilities in the market today is the ability to visualize data utilizing a data analytics program. Therefore, recent graduates and those just starting their careers might consider enrolling.

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