Contact Us
×

Category: Analytics

AI for Data Analytics and Data Visualisation

Posted on by Imarticus Learning

AI has emerged as an innovative force in the disciplines of data analytics and data visualisation. This cutting-edge technology, distinguished by its ability to duplicate human cognitive functions transforms how businesses extract insights and display data.

AI in data analytics analyses massive amounts of data rapidly. It excels in detecting patterns, trends, and anomalies, allowing data scientists to make data-driven decisions with unrivalled precision.

AI-driven developments, on the other hand, enhance data visualisation. AI-powered technologies can generate dynamic and interactive visual representations of big datasets automatically. These technologies not only improve the visual appeal but also discover hidden insights by dynamically adapting to changing data.

AI is an invaluable catalyst in the transformation of data analytics and data visualisation, moving organisations towards improved efficiency, informed decision-making, and competitive advantage. If you are looking for AI ML training, keep reading to learn about various tools for data analytics and data visualisation.

AI for Data Analytics

Artificial intelligence algorithms excel in data processing and analysis with machine learning and deep learning. They can filter through enormous amounts of structured and unstructured data in the blink of an eye, discovering patterns, correlations, and outliers. This capacity enables data analysts to extract actionable insights and make educated decisions with previously unheard-of precision.

One of AI’s primary capabilities in data analytics is its capacity to adapt and learn spontaneously. AI models can improve themselves over time, honing their analytical skills as they encounter new data. In quickly changing situations, this agility guarantees that data-driven insights stay accurate and up to date.

The use of AI in data analytics improves productivity, accuracy, and the capacity to derive meaningful insights from data. In an increasingly data-centric world, it enables organisations to make data-driven decisions that promote development and competitiveness.

AI for Data Visualisations

By automating the development and modification of visualisations, AI algorithms have opened up fresh possibilities in data representation. Based on the underlying data, they may choose the best chart kinds, colour schemes, and layouts, improving the accessibility and comprehension of complicated information. This automation speeds up the visualisation production process while also ensuring consistency across different datasets.

AI also enhances data visualisation interaction, allowing users to explore deeper data sets and find nuanced patterns and insights. Users may question visualisations in plain English using Natural English Processing (NLP), converting static charts into responsive tools for real-time exploration.

AI transformed data visualisations from static to dynamic, predictive, and highly adaptable tools. In an increasingly data-driven world, the synergy between AI and data visualisations has propelled organisations towards improved data interpretation, decision-making, and competitive advantage.

Best AIs for Data Analytics and Visualisation

AI has given data analytics and visualisation new ways to optimise automation. They are:

1. RapidMiner

RapidMiner is a data science platform well-known for its flexibility. It enables users to easily do complex analytics, machine learning, and data preparation activities. RapidMiner helps organisations extract useful insights from their data with a user-friendly interface and a wide variety of data integration options, enabling data-driven decision-making and innovation.

2. Tableau

Tableau is a well-known data visualisation and business intelligence application. It enables businesses to turn complicated data into interactive, visually appealing dashboards and reports. Users easily analyse data using Tableau, obtaining actionable insights as a tool for informed decision-making. Its user-friendly design and powerful analytical capabilities make it a must-have for professionals in the business environment.

3. Polymer

Polymer is a potent AI tool that transforms data into a simplified, adaptable, and powerful database. Polymer, like other outstanding AI tools, does not require any code. The programme uses AI to analyse data and improve users’ comprehension of it. Polymer does all of this without requiring a lengthy onboarding procedure. All a user needs to do is submit their spreadsheet to the platform, which quickly converts it into a streamlined database that can then be analysed for insights.

4. DataRobot

DataRobot is a well-known automated machine learning platform enabling businesses to accelerate and simplify their data science activities. It uses complex algorithms to automate model construction, allowing users to quickly design and deploy machine learning models. With its simple user interface enables data professionals to leverage the potential of artificial intelligence for predictive analytics and data-driven decision-making.

5. Power BI

Developed by Microsoft, Power BI is a business intelligence and data visualisation software that enables users to connect through vivid data sources. It converts raw data into actionable insights and builds interactive reports. It is preferably used in businesses for analysing data and decision-making.

6. Talend

Talend, a prominent data integration and management software provider, offers complete data solutions to businesses. It allows for the seamless integration, transformation, and analysis of data across several platforms. Talend’s user-friendly interface and strong capabilities enable organisations to realise the full value of their data, enabling informed decision-making and operational efficiency.

7. IBM Watson Analytics

IBM Watson Analytics is a robust platform for data analysis and visualisation. It provides effective analytics tools enabling users to analyse data and make data-driven choices. Watson Analytics simplifies complicated data analysis to maximise the value of their data.

8. PyTorch

PyTorch is a deep-learning framework for machine-learning applications. It provides a flexible and dynamic computational graph, allowing for the efficient creation of neural networks. PyTorch is preferred for AI and deep learning applications among researchers and practitioners owing to its user-friendly interface, solid GPU acceleration support, and large community contributions.

9. KNIME

KNIME is a data analytics and machine learning platform, that enables organisations to handle, analyse, and visualise data more effectively. It helps data professionals build data workflows for a wide range of applications, making it an essential instrument for data-driven decision-making and advanced analytics.

10. Google Cloud AutoML

Google Cloud AutoML is a machine learning product that allows businesses to easily design and deploy unique machine learning models. It simplifies the ML model construction process, allowing users to easily leverage the endless possibilities of AI for a variety of applications ranging from image recognition to natural language processing.

Conclusion

By automating data processing, discovering insights, and dynamically constructing interactive visual representations, AI improves data analytics and data visualisation. It enables professionals to make data-driven decisions more effectively and identify hidden trends, revolutionising how businesses extract value from their data.

If you are looking for AI learning courses, check out Imarticus Learning’s Executive Programme in AI for Business.

This AI for executives course would upscale your skills in AI and machine learning, and would also help you become an influential business leader.

To know more, check out the website right away: https://imarticus.org/

The significance of industry-recognised Certification in supply chain management

Posted on by Imarticus Learning

Navigating the complex world of supply chain management with certified expertise

Supply Chain Management (SCM) is a coordinated process that involves the efficient planning and management of logistics. This process is essential for ensuring that goods move smoothly from the producer to the consumer.

In today’s rapidly evolving corporate world, there is increasing demand for professionals who are skilled at managing these vital networks. This is where certification in supply chain management can become an asset, potentially boosting your career prospects.

Opportunities and challenges of the growing SCM industry

In today’s global economy, effective SCM is central to an organisation’s success. Well-managed supply chains lead to lower costs, higher customer satisfaction, and enhanced competitive edge.

However, this dynamic field is not without its challenges. Professionals in this sector often face issues such as fluctuating demand, complexities in inventory management and the necessity to adapt swiftly to changing market conditions.

To succeed in this intricate and demanding environment, you need specific knowledge and skills that set you apart from others. Here, a certification in supply chain management can be your key.

It’s not just a certificate; it’s a reflection of your dedication, skills and commitment to overcoming the diverse challenges that the supply chain presents.

Certification in supply chain management provides validation of your expertise across various key areas. This includes procurement, logistics, operations and risk management. With this certification, you signal to employers that you have the skills they need, which can give you a significant advantage in the job market.

In this article, we delve into the significance of SCM in our current world. We will also outline how a certification in supply chain management is not just a credential but a powerful testament to your ability to navigate the complex world of supply chains, making you an invaluable asset to any organisation.

The impact of certification in supply chain management on career advancement

Skill set and knowledge

Earning a certification in supply chain management gives you a complete understanding of how the entire supply chain works. It teaches you how to look at data, manage stock levels and make plans to run operations more smoothly. It’s great for new graduates or seasoned professionals looking to advance. The courses cover everything from basic ideas to more advanced strategies.

Credibility and networking

Getting a certification in supply chain management lets you connect with top people in the field. It makes your professional profile stand out and helps you meet others in your industry, such as potential mentors and employers. Plus, it gives you access to supply chain events, workshops and online groups where you can keep learning and working with others.

Career advancement and growth

Earning a certification in supply chain management can be a game changer for your career. It helps you move up faster, from a beginner’s role to a top management position. Why? Employers prefer hiring people with this certification. They know you can jump in and quickly help the company with its supply chain work. This fast track could mean a quicker rise in your job and a shot at a higher paycheck. Moreover, research back this up: people with this certification usually earn more than those without it.

Technological advancements

The supply chain world keeps evolving, thanks to new technologies like artificial intelligence and blockchain. With a certification in supply chain management, you prove that you’re keeping up with all these fresh and exciting changes. This certification shows you know how to use the latest digital tools, making you super valuable to companies that want to stay ahead in the race. They’re always looking for ways to improve. With this certification, you’re showing them you can help.

Demand for skilled professionals

Certification in supply chain management is like adding a powerful tool to your skill set. It bridges the gap between textbook knowledge and hands-on work, making you ready for real-world challenges. This training sharpens your ability to spot and solve problems early, which is a skill employers value highly.

Sealing success with certification in supply chain management

If you’re eager to dive deep into SCM, it’s time to step up your game. This field has its own set of challenging problems. But with the right training, you can find effective solutions. The certification in supply chain management is more than a piece of paper – it’s your guide to solving real-world issues smoothly and smartly. Think of each solution you create and each lesson you learn as a step forward in your journey to becoming a supply chain expert.

The certification in supply chain management isn’t just about learning; it’s about thriving in the fast-paced world of supply chains. This programme is designed to empower you through hands-on training. Learn from industry experts, practice with engaging materials and leave the course ready to make a big, positive impact in your career. For more details on the course and its many benefits, check out Imaticus Learning.

Supply Chain Planning: What is it and How is it Used?

Posted on by Imarticus Learning

In a world where global trade spins faster than our planet, there lies a pivotal axis ensuring every cog and wheel moves seamlessly—Supply Chain Planning (SCP). Just as a conductor leads an orchestra to ensure every instrument plays its part flawlessly, SCP harmonises the myriad elements within supply chain management.

But exactly what is supply chain planning, and what really goes on behind the curtains of this intricate dance? Let’s dive in.

Understanding Supply Chain Planning

At its essence, Supply Chain Planning is the strategic approach to optimise the production and distribution processes. It’s about determining what to produce, how much to produce, and where to store it to meet anticipated demand. By doing so, it reduces waste, minimises costs, and ensures timely deliveries.

Yet, SCP isn’t just about the here and now. It involves forecasting future demands, anticipating challenges, and devising strategies to navigate those challenges. In other words, it’s like playing chess, thinking several moves ahead.

Components of Supply Chain Planning

SCP isn’t a monolithic process. It’s an umbrella term covering several interrelated activities. Let us explore the key components to supply chain planning to unravel each.

  • Demand Planning: Estimating future customer demand using historical data, market trends, and predictive analytics.
  • Supply Planning: Deciding how to best meet that demand. This could involve ramping up production, sourcing more materials, or optimising inventory levels.
  • Production Scheduling: Detailing the manufacturing process, ensuring resources like manpower and machinery are effectively utilised.
  • Distribution Planning: Strategising the transportation of goods from manufacturing hubs to distribution centres, retailers, and finally, to customers.

The Digital Revolution in SCP

The world of Supply Chain Planning isn’t immune to the sweeping winds of digitisation. Advanced algorithms, AI-driven analytics, and real-time data feeds are transforming traditional SCP models. These tools provide unprecedented clarity, enabling businesses to make more accurate and agile decisions.

For those intrigued by this evolving realm, logistics management courses online can offer great assistance to strengthen an optimistic career. With courses like the Advanced Certification in Digital Supply Chain Management from IIT Guwahati, you can dive deep into the modern tools and techniques reshaping SCP.

Why Supply Chain Planning Matters

It’s tempting to think of SCP as a behind-the-scenes process significant only to businesses. However, its impacts are far-reaching:

  • Consumer Satisfaction: Efficient SCP ensures products are available when and where consumers want them.
  • Cost Efficiency: By optimising resources and minimising waste, SCP can lead to significant cost savings.
  • Sustainability: Effective planning reduces overproduction and waste, aligning businesses with eco-friendly practices.
  • Resilience: In a world where disruptions are inevitable, from geopolitical issues to natural disasters, robust SCP provides a roadmap to navigate these challenges.

Incorporating Flexibility: The Adaptive Side of SCP

In an ideal world, plans are made and executed to perfection. However, in the dynamic landscape of global trade, unforeseen changes are more of a rule than an exception. Modern Supply Chain Planning understands this. It’s no longer just about creating a robust plan but also about ensuring flexibility. This adaptive side of SCP allows businesses to quickly pivot in response to unexpected challenges, be it a sudden surge in demand, supply disruptions, or geopolitical shifts.

Integration with Other Business Domains

Supply Chain Planning doesn’t operate in isolation. It’s intertwined with other business domains like finance, marketing, and sales. For instance:

  • Finance: Budgetary constraints and forecasts can influence supply chain decisions. Investing in new machinery or opening a new distribution centre must align with the company’s financial health and goals.
  • Marketing: Promotional campaigns can lead to spikes in demand. SCP must be in sync with marketing to anticipate and cater to these changes.
  • Sales: Real-time sales data feeds into the demand planning aspect of SCP, ensuring accurate production schedules and inventory management.

By fostering a symbiotic relationship with these domains, SCP can truly optimise its strategies and drive holistic business growth.

The Human Element in SCP

While technology plays an ever-growing role in modern Supply Chain Planning, the human element remains irreplaceable. It’s people who provide insights that raw data might miss, cultivate relationships with suppliers and partners, and make judgment calls when unforeseen challenges arise. Training, continuous learning, and fostering a culture of collaboration are pivotal.

Investing in human capital – through mentoring, workshops, or specialised courses like those offered by esteemed institutions – ensures that the human side of SCP is as sharp and effective as its digital counterpart.

Wrapping Up: The Unsung Hero of Global Trade

Supply Chain Planning might not make headlines, but its influence is undeniable. From the smartphone in your pocket to the food on your table, SCP ensures goods move efficiently and sustainably across the globe.

So, the next time you marvel at the vast array of products available at the click of a button, spare a thought for the meticulous planning that made it possible.

Data Analysis Techniques: Trend Analysis and Time Series Analysis

Posted on by Imarticus Learning

In today’s data-driven world, extracting meaningful information from vast data is crucial. It serves a huge purpose in almost every sphere.

Whether it is a business looking to optimise operations or a financial analyst predicting market trends, data analysis is a definitive tool for unlocking hidden insights.

Among various techniques from a data analyst’s toolkit, two of the most powerful ones are trend analysis and time series analysis. It helps in identifying patterns, making informed decisions, and even predicting future outcomes.

We will dive deep into understanding these two effective techniques of data analysis. So, whether you’re a seasoned data analyst or beginning a career in data science, this blog will equip you with knowledge about trends and time series data.

Data Analysis and its Techniques

Data analysis is the systematic process of cleaning, transforming, interpreting, and extracting actionable information from the data. It encompasses a range of methods and tools to scrutinise, process, and extract insights from data. Some of these techniques are statistical analysis, machine learning, data mining, and visualisation methods.

Apart from these, there are two prominent data analysis techniques that one must master if considering a data science course in the future. These are trend analysis and time series analysis. These approaches empower individuals to discover meaningful patterns, correlations, insights, and trends to help make effective decisions across various industries and domains through data science training.

Trend Analysis

This effective data analysis technique deciphers long-term patterns and directional changes hidden within datasets. It involves examining data points over time to identify consistent trends.

To perform trend analysis, data analysts employ statistical methods and data visualisation tools, foundational knowledge in any data science course. These tools help reveal recurring patterns, growth or decline trends, and fluctuations within the data. And from these insights, businesses can adapt their strategies, optimise their operations and even anticipate market shifts.

Examples of Trend Analysis

There are several domains where one can make the most out of trend analysis, making it a crucial component of any data analytics course. Some of the examples are given below:

  • In financial markets, trend analysis assists in predicting stock price movements and assessing investment opportunities.
  • The marketing industry identifies its consumer’s preferences and emerging market trends with the help of trend analysis.
  • In the wide field of epidemiology, this data analysis technique tracks disease outbreaks.
  • The field of climate science benefits from this as it is helpful for studying long-term weather patterns.

Types of Trend Analysis

Trend analysis encompasses various approach various approaches to interpreting data patterns over time. Here are some types that help extract actionable insights from time series data:

  • Linear- Identifies consistent upward or downward trends, facilitating predictions based on historical patterns.
  • Seasonal- Captures recurring patterns within fixed time intervals, enabling businesses to adapt to marketing trends.
  • Exponential- Focuses on exponential growth or decay patterns, valuable for highly volatile scenarios.
  • Cyclical- Uncovers long-term cycles of ups and downs, crucial to understanding market fluctuations.
  • Breakpoint- Identifies abrupt changes, vital for recognising shifts in data patterns
  • Non-linear- Applies advanced mathematical functions to accommodate non-linear data behaviours.

Time Series Analysis

In this specific technique of data analysis, analysts record data points at consistent intervals over a set period of time. But this isn’t just an act of collecting data over time, it is much more than that.

The time series analysis shows how variables change over time, along with providing additional sources of information and a set order of dependencies between the data.

This analysis typically requires an extensive data set to ensure consistency and reliability. Its analysis can cut through noisy data, assuring that any trends or patterns discovered are not outliers and can account for seasonal variance.

Organisations use time series analysis to dig deep into why a particular trend or pattern has occurred. And when they analyse data over consistent intervals, they can even use predictive analytics to forecast future events better. However, apart from forecasting, some other key types of time series analysis are:

  • Classification- Assigning categories to data.
  • Descriptive Analysis- Identifying patterns like trends, cycles, or seasonal variations.
  • Intervention Analysis- Talks about the impact of an event on data.
  • Exploratory Analysis- Highlight the main characteristic of time series data.
  • Segmentation- Splits the data into segments and shows the underlying properties.
  • Curve Fitting- Studies the relation of variables within the data.
  • Explanative Analysis- Understand the cause-and-effect relationship within it.

Examples of Time Series Analysis

To understand time series analysis better, we must know the places it is applied in. There are several industries including finance, retail, and economics where this crucial technique from data analytics certification course is highly used as currency and sales are always changing.

Some other examples of time series in action are weather data, stock prices, rainfall measurements, quarterly sales, brain monitoring, industry forecasts, and many others.

Component of Time Series Analysis

The force behind changing the attributes of a time series is its components. There are four main key components of time series analysis:

  • Trends- Represents long-term patterns to indicate the variable’s change in direction over time.
  • Seasonal Variations- Recurring patterns that occur at fixed intervals within a time series.
  • Cyclical Variations- These represent longer-term oscillations in time series data, typically not fixed to specific time intervals, discussed in advanced data science certification.
  • Irregular or Random Movements- Unpredictable fluctuations in time series data, often caused by unforeseen events.

Conclusion

In today’s digital age, data analysis has become a cornerstone of informed decision-making across industries. One can draw out the benefits of data analysis in many ways. It plays a pivotal role in offering a career in data analytics and eventually helps organisations make data-driven decisions and drive innovation.

To decipher data’s hidden narratives, there are two data analysis techniques that stood out as potent tools. From predicting market trends to tracking disease outbreaks, trend analysis, and time-series analysis offer invaluable insights.

Trend analysis reveals long-term patterns and directional shifts, while time series analysis dives deep into data collected over consistent intervals. Both approaches empower organisations to harness data for strategic advantage, adapt to changing circumstances, and forecast future outcomes.

If you aspire to master these data analysis techniques and become a data analyst, consider Imarticus Learning’s data science course with placement assistance . This comprehensive program equips you with the skills and knowledge needed to excel in the dynamic world of data analytics, ensuring you’re well-prepared to tackle real-world data challenges and drive innovation in your chosen field.

What aspects to look for before enrolling in a data science course?

Posted on by Imarticus Learning

Data science courses are immersive training programmes that aim at providing the advanced knowledge and skills required to pursue a career in this field.

With the completion of a data science course, you will be equipped with in-depth knowledge of important concepts and advanced techniques used in the field. These certifications ensure that you have better chances of getting a job.

It’s crucial that you do some research before deciding to join any course. Analysing will help you understand if the data science course is relevant to your chosen field. Apart from polishing your skills, the certification will make your recruiter feel that you have some passion for the field while interviewing for the job. Getting a certification from a reputed firm will also assure them that you are capable of adding some value to the company.

In this article, let’s discuss the different aspects to look for while joining a data science course. 

Here’s how you can choose the right data science course 

Latest industry-ready curriculum

As a data scientist, you are expected to master topics such as computer science, Machine Learning, statistics and programming languages like Python.

There are data science courses available today which help you specialise in a specific data science field. Or if you are focusing on developing a particular skill set, data science courses can also cater to that need.

It’s important to talk to experts in your domain before deciding on the area and type of training you want to take up. They can assist you in choosing the appropriate curriculum updated with the latest market trends.

Also, carefully review the course content and make sure that it aligns with your career goals. Some of the topics generally covered in a data science course include:

  • Machine Learning
  • Coding
  • Python Programming
  • A/B Testing
  • Statistics
  • Linear Regression
  • Intermediate Excel
  • Databases (for eg. MySQL)

Practical learning methods 

A theoretical understanding of the subject is crucial to have a thorough understanding of the core concepts. However, remember not to spend the whole time taking notes. A practical understanding of concepts is even more important to become a data scientist. A solid data science course will equip you with the required practical knowledge and skills to tackle real-world problems. It’s these valuable skills learned through project-based training programmes that will help you in securing a job.

Flexibility

While you are researching programmes, identify the class structure that will fit your schedule. An online training programme is a convenient way to join a data science course without sacrificing your responsibilities. You can take an online programme from anywhere. The only requirement is a good internet connection. This way, you can embrace your own pace while learning. However, not many online programmes provide networking opportunities and team-building practices. You should ensure that the chosen course offers these opportunities, as they will benefit you in the future while applying for a job.

Reputation of institution

Are people you know working in the industry familiar with their organisation? Are they experts in this field? Do companies give any value to their certification? These are some of the important questions to be answered while searching for a data science course to join.

Placement reviews

When you join a training programme, one of your least expectations would be to get placed in a decent company. However, you should note that there are organisations that will not fulfil their promises. Remember to check out placement options and reviews before enrolling in any data science course. Only a reputable organisation like Imarticus Learning can provide a data science course with placement.

Key takeaway

Data science has grown to be the backbone of many industries. Today, top companies are on the lookout for people who can add skills to their organisations.

Imarticus Learning has years of experience in providing a postgraduate course in this field. The data science course will equip you with the advanced analytical skills required to grow in your profession.

It is a 6-month training programme which will be conducted online. We assure you that you will be able to develop the skills required to ace in your field by the end of the course. Ours is a job-specific curriculum which incorporates several real-world projects and case studies.

Imarticus Learning is a reputed institute which guarantees networking and interview opportunities for top graduates. Our alumni working at top organisations like Deloitte, Infosys, KPMG, Capgemini and Wipro can vouch for that.

Visit Imarticus Learning now to join the course.

Visualisation of Multivariate Data

Posted on by Imarticus Learning

Multivariate data visualisation is an invaluable instrument for illuminating convoluted relationships and patterns within datasets.

Researchers and analysts need to get insights, make educated decisions, and effectively convey findings.

In essence, multivariate data consists of several variables or qualities, each with its own set of values, creating a multi-dimensional puzzle.

Data scientists use a variety of visual tools, such as scatter plots, heatmaps, and parallel coordinate graphs, among others, to uncover hidden gems.

These visualisations give a complete picture of data relationships, allowing the detection of correlations, trends, and outliers, much like experienced cartographers charting unidentified territories.

This blog will delve into methodologies, best practices, and real-world applications that examine various elements of multivariate data visualisation.

If you want to have a booming career in data science or take up a data science course, keep reading to learn in detail about multivariate data visualisation.

What is Multivariate Data Visualisation?

The graphical depiction of data sets that include several variables or characteristics is multivariate data visualisation.

It is a critical tool for data analysis and exploration, allowing analysts and researchers to acquire a better grasp of complex relationships within multidimensional data.

In a nutshell, multivariate data visualisation extends data beyond numbers and tables, transforming it into accessible and informative visual representations. It enables for the simultaneous evaluation of numerous variables using charts, graphs, scatter plots, heatmaps, and other visual approaches, revealing patterns, trends, and correlations that would otherwise be buried in raw data.

Types of Visualisation in a Multivariate Analysis

Types of Visualisation in a Multivariate Analysis

Data visualisation with multivariate analysis has its classifications. While making a career in data science and analytics, this knowledge will be useful.

1. Geometric Representation

Geometric representation is an important type of visualisation in multivariate analysis. Techniques like scatter plots, parallel coordinate plots, and multidimensional scaling are utilised for transforming complicated data into geometric forms and patterns.

These visualisations enable analysts to get useful insights and make well-informed choices by allowing them to explore links, clusters, and sequences within multidimensional data.

2. Icon-Based Representation

In multivariate analysis, icon-based representation is a visual approach that uses symbols or icons to convey information about data points.

Each icon’s characteristics, such as size, colour, or structure, indicate specific characteristics entitling rapid and intuitive comprehension of complicated data links.

This form of visualisation simplifies understanding data and is suitable for expressing multivariate ideas simply.

3. Pixel-Oriented Representation

Pixel-oriented representation is a type of visualisation used in a multivariate analysis that uses individual pixels organised in a grid to represent data points.

The colour or intensity of each pixel indicates distinct features or values, allowing analysts to see patterns and variances in the collection.

This method provides a particular visual perspective, which is beneficial for studying vast and complicated datasets, where pixel-level details can reveal deep linkages and nuances.

4. Hierarchical Representation

Hierarchical representation, in multivariate analysis, organises data in a hierarchical, layered format. It uses tree diagrams or layered structures to illustrate changeable relationships.

This visual technique facilitates recognising hierarchical patterns within datasets, allowing the discovery of connections and subgroups among variables, and increasing the depth of analysis and comprehension in complicated multivariate data.

5. Hybrid Representation

Hybrid representation in multivariate analysis combines numerous kinds of visualisation methods, such as scatter plots, bar charts, and heatmaps, to offer a complete picture of complicated data sets.

This strategy improves comprehension of multiple variable interactions by harnessing the capabilities of numerous visual tools, allowing analysts to extract key insights and patterns more effectively.

Why Conduct Multivariate Data Visualisation?

While getting a data analytics course or a data science course, one might wonder what is the need for conducting multivariate data visualisation.

One might often have this as a part of their data science training. Read to know about it in a simplified manner.

  • Comprehensive representation: Multivariate data visualisation gives an extensive overview of complicated datasets, allowing analysts to comprehend subtle relationships and patterns among various variables.
  • Pattern Recognition: It facilitates the detection of hidden patterns, correlations, and trends that would otherwise be missed by numerical analysis alone.
  • Outlier Detection: Visualisation aids in the rapid identification of outliers or abnormalities in data, which is critical for quality control or anomaly detection activities.
  • Effective transmission: Visual representations make complicated data more accessible and clear to varied stakeholders by simplifying the transmission of results.
  • Decision Support: By providing facts in a simple and usable style, it enables decision-makers to make educated choices.

Steps to Apply Multivariate Analysis for Data Visualisation

While doing a data analytics certification course, the learner often comes across the methodology to apply multivariate analysis for data visualisation. They are:

Step 1: Data Preparation
Begin by collecting and cleaning the data, resolving missing values and outliers, and ensuring that it is in an analysis-ready shape.

Step 2: Variable Selection
Select the variables or qualities that are significant to the study, taking into account their influence on the research question or aims.

Step 3: Data Reduction
When working with high-dimensional data, use techniques such as Principal Component Analysis (PCA) or Factor Analysis to decrease dimensionality while keeping crucial information.

Step 4: Visualisation Methods
Based on the data and objectives, choose appropriate multivariate visualisation approaches. Scatter plots, heat maps, and parallel coordinate graphs are all popular possibilities.

Step 5: Encoding Variables
To effectively depict their relationships in the chosen visualisation, map the selected variables to visual qualities like colour, size, or location.

Step 6: Interactive Tools
Implement interactive components in the visualisations to allow viewers to dynamically explore the data and gain deeper insights.

Step 7: Pattern Recognition
Examine the visualisations for patterns, trends, clusters, or outliers in the data.

Step 8: Interpretation and Communication
Interpret the insights acquired by visualisations and successfully communicate findings to stakeholders via reports or presentations.

Step 9: Refinement
Iteratively improve the visualisations and analyses in response to feedback and new research questions, ensuring that they remain aligned with the shifting objectives.

Step 10: Documentation
To ensure transparency and repeatability, document the whole multivariate analysis process, including data sources, techniques, and outcomes.

Conclusion
Multivariate data visualisation is an essential tool for unravelling complex and significant data. It allows the researchers to extract useful insights and make informed decisions. It connects data and knowledge enabling an effective communication system in different domains. This kind of visualisation plays a significant role in exploratory data analysis and research.

Check out Imarticus Learning’s Postgraduate Programme in Data Science and Analytics to become a data analyst.

This programme will give you 100% job assurance with chances to upskill yourself. It is both for fresh graduates and early career professionals, so anyone can join this data analytics course in india with placements. Get in touch via chat support on our Contact Us page. Alternatively, visit one of our training centers in Mumbai, Thane, Chennai, Pune, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Related Posts:

The Impact of Data Analytics on Customer Experience and Engagement

Posted on by Imarticus Learning

Data is king in the modern digital world, and organizations can increasingly use data-driven choices to enhance customer experience and engagement. Businesses now have unparalleled visibility into consumer behavior and preferences because of the acquisition and analysis of customer data.

By leveraging data analytics, companies may create individualized and targeted experiences that improve customer pleasure, loyalty, and revenue. In this blog, we’ll look at the impact of Data analytics on consumer engagement and experience and how organizations use this technology to stay competitive. 

So grab a seatbelt and get ready to learn how data analytics has the extraordinary potential to alter consumer experiences.

What is the impact of data analytics on customer experience?

Data analytics is a potent tool for businesses to understand their customers better. It can help you understand your business better, too. When implemented properly, data analytics can also improve employee performance and productivity.

According to a Forbes Insights survey of 357 executives from large organizations, the benefits of moving toward data-driven customer experiences (data-driven CX) include enhancing revenue generation, enabling cost reduction, and hastening process efficiency and quality improvements. 

The term “data analytics lifecycle” refers to the sequence of steps and stages that must be completed to evaluate and derive meaning from unstructured data. Steps in the lifecycle include gathering, preparing, modeling, analyzing, and visualizing data.

How can data analytics improve customer experience?

In summary, data-driven decision-making means using data analytics to make better decisions. Data analytics can help you make better choices and adapt your product or service to meet customers’ needs.

Customer experience analytics is becoming necessary for organizations looking to improve customer experience and engagement through improved targeting, personalization, and targeted offers.

Data pertaining to sales numbers, market research, logistics, linguistics, or other behaviors are organized by a data analyst. They use their technological know-how to guarantee the quality and accuracy of the data. Then, it is processed, organized, and presented in a way that supports improved decision-making by people, companies, and organizations.

  • Data extraction from primary and secondary sources using automated technology.
  • Eliminating corrupt data, resolving code issues, and other issues.
  • Rearranging data into a legible format is necessary to develop and maintain databases and data systems.
  • To determine the worth and quality of data through evaluation.
  • To find and correct coding problems, examine reports and performance metrics.
  • Finding, analyzing, and interpreting patterns and trends in massive data sets that might help with diagnosis using statistical methods

Benefits of Data Analysis for Improved Customer Experience

When it comes to managing customer experience and engagement, data analytics is key. 

  • With the right insights into your customers’ needs, you can build a better product that meets their wants and needs. 
  • This will lead to improved communication and engagement with your customers, boosting loyalty by giving them more reasons to stay loyal. 
  • It enables you to identify opportunities for growth through targeted marketing campaigns that generate revenue for your business.

Numerous approaches exist for firms to use data analytics to enhance consumer engagement and experience, including:

  • Recognizing consumer requirements and preferences: Businesses may use data analytics to segment their customers depending on their demographics, behavior, and feedback. 
  • Improving customer loyalty and retention: Data analytics may assist firms in measuring and enhancing their clients’ loyalty and satisfaction. 
  • Improving customer journey and touchpoints: Businesses may map and improve their customer journey and touchpoints across various channels and platforms using data analytics. 

What are the different Data Analytics Tools?

Data analytics is a critical component of customer experience and engagement. Customer-centric data analytics can track marketing campaigns’ success, improve operations’ efficiency, and improve product development. 

The following tools help you leverage your data:

  • Customer Relationship Management (CRM) Software – CRM software manages customer relationships across all touchpoints, including email, phone calls, and online interactions. Your CRM system must have robust reporting capabilities so you can quickly see how well each channel is performing against its goals or KPIs (key performance indicators).
  • Social Media Analytics – Social media platforms provide unprecedented access to how people interact with brands on social media channels like Facebook or Instagram. However, these platforms don’t offer many analytics options beyond basic sentiment analysis, which doesn’t provide enough information about what customers say about their experiences with your company when interacting through these channels.
  • Web Analytics – Web analytics tools give you insight into where visitors come from within your site and what activities they do once they are there – allowing you to optimize advertising campaigns accordingly.
  • Sentiment Analysis – Sentiment analysis helps determine whether users feel positively toward something by analyzing text content found within written messages posted online, such as comments left under blog posts or tweets.

The Final Words

How organizations communicate with their consumers is being revolutionized by data analytics. Companies may learn important insights about consumer behavior, tastes, and requirements by utilizing the power of data. With this data, companies can target their marketing efforts and customize their services to provide a wonderful consumer experience.

It isn’t easy to overstate the influence of data analytics on customer experience and engagement. Businesses can better understand their clients, anticipate their requirements, and deliver services that meet their expectations. Customers feel appreciated as a consequence, and brand loyalty rises.

Companies that do not use data analytics risk slipping behind their rivals in this data-driven environment. Businesses may gain a competitive advantage and spur development by investing in data analytics tools and tactics. 

Enroll in the excellent Data Science and Analytics course that Imarticus Learning is offering. With the help of this extensive curriculum, you’ll have the practical knowledge and abilities to use data science in everyday situations and build analytical models that boost corporate performance.

Supply Chain Transparency: Meeting Consumer Demand for Ethical Sourcing and Product Information

Posted on by Imarticus Learning

The concept of supply chain transparency has been around only for some time now. In recent times, supply chain transparency has emerged as a crucial concept depicting a paradigm shift from traditional business practices.

With heightened consumer awareness, stricter regulatory practices, and policies for ethical trade practices, the significance of supply chain transparency has increased. 

Companies must actively understand the events taking place upstream in the supply chain and effectively convey this information both within the organisation and to external stakeholders for supply chain transparency.

In this blog, we will explore the elements of supply chain transparency in detail that would help to pursue a career in supply chain management. Keep reading to learn more.

What Is Supply Chain Transparency?

Supply chain transparency is the extent to which the various stages, processes, and entities involved in producing, distributing, and delivering goods or services are open and visible to stakeholders and even to customers. 

It involves sharing comprehensive information about sourcing, manufacturing, distribution, and environmental or social impacts. This transparency fosters accountability, ethical practices, and informed decision-making throughout the supply chain. 

By disclosing details about origins, practices, and conditions, companies enhance trust among consumers, investors, and regulators, while promoting sustainability and fair labor standards. Overall, supply chain transparency ensures a clear view of the journey a product takes from its creation to reaching the end consumer.

Benefits of Supply Chain Transparency

The benefits of supply chain transparency are discussed below: 

  • Customer Loyalty: Transparent supply chains foster customer loyalty by showcasing sustainability efforts and product origins, resulting in increased revenue from repeat customers. 
  • Enhanced Conversion Rates: Shifts to purpose-driven messaging lead to improved conversion rates, click-through rates, and email revenue, underscoring how transparency positively affects customer engagement. 
  • Stronger Customer Connections: Such sustainable practices create deeper emotional ties between customers and brands, yielding substantially higher lifetime value. 
  • Trust and First-time Purchases: Transparent brands are likely to enjoy more consumer trust and are preferred by buyers making initial purchases, particularly those emphasising ethical practices. 
  • Durable Supplier Relationships: Establishing robust supplier ties ensures ethical practices and quality, translating to fewer errors and recalls, and reinforcing supply chain robustness and profitability. 
  • Preparedness for Regulations: Transparent supply chains proactively address disruptions and align with evolving regulations. Emerging laws emphasise supply chain transparency.

Challenges in Achieving Supply Chain Transparency

Achieving supply chain transparency might be difficult. Here is why:

  • Complex Global Supply Chains: Modern globalisation has intricately woven complex supply chains, often involving multiple tiers of suppliers, making it difficult for companies to trace the origin of goods and monitor practices at every level. 
  • Outsourcing and Subcontracting: Suppliers may outsource production to third parties, leading to challenges in ensuring fair labor practices and ethical standards throughout the extended supply network. 
  • Opacity in Lower Tiers: Human rights abuses and environmental violations frequently occur in lower tiers of suppliers, which can be challenging to monitor and address effectively. 
  • Limited Control: Even when ensuring ethical practices at one stage, factors like cancelled orders by other brands can jeopardise the fair treatment of workers, revealing the limited control brands have over the entire supply chain. 
  • Accountability Pressures: Consumers hold brands responsible for any supply chain issues or inaccuracies, even if the complexity of global supply networks makes complete oversight challenging. 
  • Managing Expectations: Brands must educate consumers about the intricate nature of supply chains to manage expectations while maintaining transparency to build trust. 
  • Shifting Standards: Changing regulations, certifications, and internal goals create complexities in new product development, requiring adherence to evolving standards and sourcing compliant suppliers. 
  • Balancing Disclosure: Striking the right balance between transparency and protecting sensitive information poses a challenge, as disclosing too much could have unintended consequences. 
  • Sourcing Challenges: Developing products that meet stringent ethical and environmental standards demands sourcing from manufacturers and vendors that align with these requirements is tough.

Strategies To Attain Transparent Supply Chains

As supply chains become increasingly intricate, following these steps can help companies achieve transparency, align with ethical standards, and build resilient relationships with suppliers and customers: 

  • Risk Identification and Prioritisation: Begin by mapping your supply chain, understanding its complexities, and identifying vulnerabilities. Assess past disruptions, potential issues, and upcoming regulations to determine areas for improvement. 
  • Establish Goals: Based on the insights gained, set comprehensive goals for supply chain transparency. Embrace a holistic approach, addressing both environmental and social concerns, and collaborate with suppliers to align on shared objectives. 
  • Develop a Code of Conduct: Create a robust code of conduct that outlines the ethical and sustainable standards you expect from suppliers. Collaborate with suppliers to ensure alignment and commitment to these goals, addressing specific issues like safety and environmental impact. 
  • Measurement and Progress Tracking: Define clear metrics to measure progress towards your transparency goals. Utilise tools such as the Higg Index for standardised sustainability assessment, engaging both self-assessment and third-party verification for accurate evaluation. 
  • Supplier Relationships: Recognise the importance of relationships in supply chains. Support suppliers in meeting goals by offering financial investment or resources, fostering mutual growth and improvement. 
  • Stakeholder Disclosure: Communicate your progress transparently to stakeholders and customers. Avoid complexity and make actions understandable. Simplify disclosures to make it easy for consumers to comprehend your environmental and social impact efforts. 
  • Direct Customer Engagement: Connect customers directly to producers and suppliers in your supply chain, fostering a deeper understanding of the process. This not only enhances transparency but also enriches customer experience and quality improvement. 
  • Continuous Monitoring: Recognise the dynamic nature of supply chains and implement regular progress assessments. Continuously refine strategies and practices to ensure sustained transparency and alignment with goals.

Conclusion

Supply chain management optimises business operations and helps drive the overall success of any organisation. Supply Chain Managers contribute by enhancing efficiency and upholding customer expectations while reducing costs. Thus, supply chain management has emerged as a lucrative career option due to its increasing demand. 

If you want to pursue a career in supply chain management but do not know where to start, head to Imarticus Learning for a beneficial supply chain course. Imarticus Learning, in partnership with IIT Guwahati, has designed a comprehensive digital course for aspiring Supply Chain Managers. The Advanced Certification Program in Digital Supply Chain Management deals with aspects of vendor management, supply chain transparency, procurement, and much more to make students industry-ready. 

Not just this, at Imarticus Learning, students are given 100% placement assistance so that they can only focus on brushing their skills and not worrying about bagging a job. Head to Imarticus Learning to know more. 

How Indian universities can harness data analytics to ensure student success

Posted on by Imarticus Learning

Evolving educational landscape and how data analytics has emerged as a powerful tool

At its core, the mission of education remains unaltered: to facilitate learning and empower individuals. However, the method to accomplish this age-old mission is undergoing a technological metamorphosis. In today’s result-centric world, data analytics is revolutionising the education industry by transforming data into actionable insights. Institution are trying their best to succeed and gain top rankings, making the education industry a highly competitive market. Gone are the days when institutions relied on manual processes and traditional ways to run the business.
Data analytics is the process of collecting and examining data to draw valuable insights to make informed decisions. Implementing data analytics in education helps in multiple ways. Not only does it improve your institution’s administrative processes, but also enhances the learning experience of students. Making it ideal for Indian universities to set themselves up for both institutional and student success. Data-driven decision-making helps in improving overall academic outcomes and higher education quality.

How can data analytics solve the challenges that students face and enhance their learning experience?

Students can encounter several challenges at university. Be it learning how to effectively manage time, transitioning into university life or learning the art of studying effectively. There is a steep learning curve academically and personally for each student. Academically, data analytics has changed the learning ecosystem. Earlier, traditional tests and exams were the only means to analyse student performance. With data analytics, institutions can collect student data from various sources through data analytics tools. These sources include student information systems, interviews, surveys and other assessment activities.
This data can be tracked and analysed to learn the strengths and weaknesses of each student. It gives insights into their academic skills, response time to answer questions from different subject areas, questions they don’t answer etc. Teachers and mentors can use this information to provide meaningful feedback and offer extra support for students to improve in areas they lack. This way, institutions can create a better learning environment for students to flourish.

How can you apply data analytics in your university?

Indian universities can now perform data-driven decision-making using data analytics. Institutions can use data analytics tools to compile student information like enrollment year and chosen classes into individual profiles. They can also collect data on their learning behaviour such as their areas of interest and the pace at which they learn. By examining these data, you can learn what is going well and what areas need improvement.
For example, institutions can employ several AI/ML tools to monitor student actions, learning patterns and their progress. These tools can automate administrative tasks, thereby freeing educators to focus more on teaching. They can also facilitate adaptive learning programs that automatically adjust difficulty levels based on individual student performance. AI algorithms can analyse vast amounts of data to identify trends that can predict student outcomes, helping institutions to intervene before a student falls behind. Moreover, machine learning algorithms can suggest customised reading materials and resources based on individual learning styles and needs, further promoting personalised learning and student engagement.

Monitoring individual student performance

By combining data from various sources, institutions can evaluate the performance of students on multiple levels. They can set and track educational goals for each student on specific subjects. Based on the insights from the analysis, teachers can identify any possible learning gaps and design a personalised learning experience.
For instance, let’s assume a student is struggling with a specific topic. Here, the teacher can allocate more time for them to understand that topic. A new teaching method also can be introduced to help the student learn better. The teacher can also plan future learning assignments to enhance the student’s knowledge and skills.

Disaggregating student performance by selected characteristics

Disaggregating student performance data means looking at the achievement results by breaking them down into smaller subgroups. These subgroups can be based on different characteristics such as major, ethnicity and year of study. Disaggregating these data can help in identifying the underlying trends of student behaviour.

Data analytics enables you to dive deeper into teaching and learning outcomes. It highlights the issues that pertain to each subsection. By understanding learning gaps, you can plan your improvement strategies.

For example, factors like bias and privilege can have a huge impact on students’ learning experience. By disaggregating data based on demographic characteristics, you can ensure that the students’ results are not disproportionately determined by factors like their age, race and gender. You can make sure that your course is equally available for all students through careful analysis.

Identifying outliers for early intervention

To create an inclusive and effective learning environment, it’s essential to consider not just the majority of students but also those who exhibit unique or atypical learning behaviours – often referred to as outliers. These outliers may engage differently with learning materials, exhibit uncommon study habits, or face specific challenges that are not as prevalent among the general student population. They might be extremely gifted in certain areas or face unique obstacles that warrant special attention.

The advantage of identifying these outliers early on is manifold. Early intervention allows educators to tailor personalised learning experiences that cater to these unique needs and circumstances. This approach ensures that all students, regardless of where they fall on the learning spectrum, receive the targeted support and resources they require to succeed.

Predicting potential for optimal achievement

Predictive analytics seeks to identify difficulties that may arise in the future. Universities can employ data analytics to identify students who have the potential to excel. Teachers can then utilise specific tools or fascinating projects to push these students and help them achieve their maximum potential.

Preventing attrition from a course or programme

Data analytics can aid in identifying the top reasons why students leave the university before completing their course. You can use this information to improve areas that need attention and boost retention rates. Institutions can implement predictive analytics to locate at-risk students. The same will help you in understanding their personal risk level. With this information, educators can provide them with the required support. For instance, you can identify a student whose grades are falling before it gets too low using predictive analysis. If this particular student has been struggling for a while due to certain stressors, you can offer them the needed help.

Identifying and developing effective instructional techniques

Ineffective instructions can lead to poor grades. Students will become less interested in studying and develop a negative attitude towards education. Data analytics can help institutions in improving the delivery of instructions. By examining the student performance data, universities can identify the areas where students are struggling. They can change their instructional design to meet the needs of each student better.

Analysing standard assessment techniques and instruments

When universities check out how well students do in different tests and activities, they can figure out what subjects they’re good at and where they need to do better. Data analytics helps get this information and guides them in making the tests better. So, the tests end up showing how much students have actually learned.

Testing and evaluation of curricula

Data analysis is crucial to determining whether the teachings are making a difference. When universities look at how students are doing in different classes, they can see what parts of their programmes are strong and where they could do better. This enables them to continuously develop learning materials that improve student engagement.

Unleashing the power of data for a brighter educational future

Data analysis is revolutionising education. Institutions uncover invaluable insights into student learning patterns, enabling effective guidance. By carefully looking at student enrollment and course completion rates, you can make your strong points even stronger and work on your weak spots. This proactive strategy enhances engagement, elevates retention rates and attracts new students. Predictive analysis empowers institutions to foresee challenges, facilitating strategic planning. Data’s transformative role in education is undeniable, optimising experiences and outcomes for all stakeholders.

3 Key Takeaways

  1. Data analytics boosts personalised learning, enhancing academic outcomes and student engagement.
  2. Predictive analysis identifies at-risk students early, enabling timely intervention for higher retention.
  3. Real-time insights empower universities to continually optimise curricula, resources, and instructional techniques.

Top 5 Strategic HR Management Courses Of 2023

Posted on by Imarticus Learning

Importance of Strategic HR Management

Implementing Strategic HR Management (SHRM) in an organisation can yield numerous benefits. This approach enables the HR department to harness employee potential through strategic planning effectively.

The key role of SHRM revolves around organisational design, talent management, performance management, and leadership development. The entire planning processes align with an organisation’s broader goals and objectives.

HR management strategies collaborate with an organisation’s HR team and mission statement.

Top 5 Strategic HR Management Courses of 2023

To become a part of SHRM, you must hone your skills and upgrade your knowledge about strategic HR trends. And this is where Strategic HR Management Courses help. We have listed the top 5 courses in the field of SHRM that you should know about.

1. People Leadership & Strategic HR Management, IIM Lucknow

This is one of the best Executive Management programs hosted by Imarticus Learning. This course is specifically tailored for senior business managers and emerging HR professionals who aspire to become HR leaders.

  • Highlights of the course
  • New-age people leadership program
  • Curriculum designed for leaders
  • IIM Lucknow Executive alumni status
  • Masterclasses by Chief Experience Officers (CXOs)
  • Winsome learning experiences

2. Human Resource Management and Analytics CEC, IIT Roorkee

Powered by Imarticus Learning, this Strategic HR Management Course ensures you become leaders of the HR world. This programme will give you the knowledge and skills to succeed in strategic human resources in a changing business world. The program includes a range of topics, such as:

  • Managing diversity
  • Analyzing and designing job roles
  • Engaging employees
  • Creating effective hiring strategies
  • Conducting performance appraisals
  • Developing skills and competencies
  • Establishing a positive organisational culture.

Highlights of the course

  • Immersive learning
  • Premium access to Linkedin Learning
  • Pro access to Hirist and IIM Jobs
  • Certification from CEC, IIT Roorkee
  • Campus immersion at IIT Roorkee

3. Executive Programme in Strategic Human Resource Management, IIM Calcutta

This course aims to equip both middle and top-level managers. This is an ideal programme for line managers along with HR managers. The module covers the basics of HRM, leadership and talent management, corporate strategy, employee or industrial relations, and the links between strategy and HRM. In addition, the course covers important SHRM topics such as HR analytics and managing millennial workers. It also addresses current issues in international HR management and the economic workforce.

Highlights of the course

  • Case method study
  • On campus experiential
  • Self –assessment
  • Status of Alumni of IIMC Executive Education

4. Strategic Human Resource Management, IIM Ahmedabad

This Strategic HR Management Course by IIMA is best suited for CEOs, heads of strategic management, and HR. It also benefits employees involved in designing the organisational structures. The course helps learners develop a sense of identifying strategic HR issues in organisations. Thus, they can resort to the best choices in the given dynamic business environment.

Highlights of the course

  • Designing HR systems for sustained competitive advantage
  • Corporate Strategy and HRM
  • Strategic HR diagnosis
  • Flexi working: Strategic HR agenda
  • Managing International business

5. MBA in HR Analytics, Amity University

One key feature of SHRM is leveraging the latest data-driven technology. Amity’s online MBA in HR Analytics Course revolves around this crucial feature. With this course, HR professionals gain expertise in analysing data to manage people and meet business goals.

Highlights of the course

  • Live interactions with analytics experts and business leaders
  • Flipped learning model to enhance learning outcomes
  • Support to students through relationship managers
  • Virtual job fairs with over 100 hiring partners
  • Global education set-up with renowned International faculty

Benefits of enrolling in Strategic HR Management Course

Strategic HR Management Course inculcates sustainable value in learners. Globalisation, advancing technologies, and evolving social norms influence HRM. Hence, adopting a strategic approach to HRM issues is crucial. Employers now recognise the competitive advantage of including SHRM. A Strategic HR Management Course will always keep you ahead of your peers in the HR field.

Moreover, these courses ensure you stay current and meet the organisation and customer needs. The course prepares you to improve HRM efficiency and the organisation’s success.

Summing it up!

Are you looking to advance your career in HR? The Strategic HR Management Courses mentioned above can help you achieve this. These courses will help you establish credibility as the best strategic HR Manager. These will give you a deeper understanding of business, HR operating models, and detailed organisational design.

Along with these Strategic HR Management Courses, Imarticus Learning hosts many courses powered by the best universities to update your skills and knowledge.