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Tag: Big Data and Hadoop

The career outlook for data scientists with a Postgraduate degree in Data Analytics

Posted on by Imarticus Learning

Data science is one of the most buoyant fields with myriad career opportunities and lucrative pay now. As the world is progressing towards big data and metaverse, the role of data and artificial intelligence will impact our lives more than ever before.

However, individuals must note that the role of data science includes a legion of disciplines and include multiple job titles such as data analyst, data engineer, program analyst engineer, and machine learning specialist. Therefore, to stay ahead of the pandemonium concerning job seekers, a PG in data science in India can significantly boost the chances of employment.  

big data analytics courseFuture of Data Science Jobs

Businesses and other allied sectors have now realized the importance of big data; thus, the demand for data scientists is soaring. As per a report by the Times of India, the volume of global data is projected to touch 180 zettabytes by 2025. To manage this enormous volume of data, the skilled workforce is bound to surge more in the upcoming years. 

LinkedIn’s Emerging Job Report has ranked data science as the fastest-growing field in the world and has estimated the market to reach USD 230.80 billion by 2026. Also, ‘The Humans of Data Science’ reports that data science will create around 11.5 million jobs by 2026. So, a data scientist career will reach new heights soon, and for new professionals, this might be the correct time to step into this booming field. 

Data Scientist Salary in India

India currently holds the second position as the most prominent recruiter in the field of data analytics and data science, next to the United States. In India, the average salary of a data scientist is approximately INR 698,412 per annum. But, for freshers, the salary hovers around the INR 500,000 mark per year. Also, an early-level data scientist can earn around INR 610,811 per annum with around 1 to 4 years of experience. Additionally, with experience and expertise, the salary increases significantly and might reach around INR 1,700,000 per year.   

Responsibilities of a Data Scientist 

Although the job responsibility might vary with the sector, in general, the responsibility of a data scientist remains the same. A few of these are: 

  • Analyzing large amounts of information and data to discover underlying patterns and trends 
  • Pre-processing unstructured or structured data
  • Building predictive models and ML algorithms 
  • Identifying valuable data sources for automating the collection process 
  • Provide impactful solutions for business challenges
  • To form an ensemble modeling by combining models

Skills Required for Data Scientists 

Individuals looking to learn data science online must understand that the job role of data scientist demands specific skills. So, they must enroll in a course that will provide adequate knowledge and skills that will allow them to sustain themselves in this field. Nevertheless, a few of the most demanding skills are: 

  • Knowledge in data mining
  • Skills in Python, SQL, and R 
  • Familiarity with Java, Scala, or C++
  • Understanding operation research and machine learning 
  • Strong mathematical skills
  • Problem-solving attitude
  • Strong communication skills 

In case individuals are looking to pursue a career in data science, it is always important to know the employer’s perspective. A master’s or diploma degree with prerequisites skills is often demanded. Hence, a postgraduate degree in data science in India might help secure a job.

And, to proceed with this career, individuals need to acquire skills with time. Also, as more and more businesses incorporate machine learning and artificial intelligence into their systems for better productivity, the jobs will soon expand to new horizons. Hence, this is clearly the most suitable time to jump into data science.

How learning a tableau course can enhance your career prospects

Posted on by Imarticus Learning

With the advancement of technology, data skills are in demand. Everything we do revolves around the analysis of people’s behavior and understanding the statistics behind their decisions. Tableau is a computerized program that improves this analysis by making data more simple and accessible. It converts big data into a small and understandable form, at the same time giving an insight into the small data. 

The Tableau course at Imarticus will build a career in business intelligence and data analytics. You can get answers fast and also develop an unforeseen insight into statistics.

Tableau Career Opportunities

Today, companies have an enormous inflow of data with implications in their business. Therefore business corporations across the globe need an interactive and easy-to-use tool that can examine the data while giving an insight into it. 

Tableau software helps these corporations to visualize, explore, examine, and share the data so that they can take timely action and spread their business.

Tableau Analytics should have analytical skills. They should be problem-solving, innovative, and detail-oriented. They should also be a team worker and know business intelligence tools and Query languages.

With the data analytics course with placement by Imarticus, you will become a Tableau professional. Our program covers all the fundamentals and topics for building a promising Tableau career. We will teach you everything from scratch so that your career moves to the peak level. After the completion of this course, you will have varied career options, such as: business analytics certification courseTableau consultant

  • Data analyst
  • Business analyst
  • Business intelligence analyst
  • Business intelligence developer
  • Business intelligence manager

As a Tableau developer, you will prepare visualization and presentation and conclude data to improve business excellence. Tableau visualization will assist you to create innovative solutions for business problems.  

Tableau professionals can work on business problems and provide technical solutions for them. The visualization of the data will help them in finding an innovative solution and they can also work with the storage tools. With the development and expansion of the organization, the inflow of business data will also increase. Tableau Analytics can also enhance the system of the organization to meet this increase in data.

Data visualization and business intelligence are the requirements for the success of business organizations. The growth of many organizations depends upon these. Thus, the future of a Tableau professional is promising and bright.

Data Analytics Certification

We know that data is the backbone of every organization. With the increase in data, its storage is also increasing. Therefore, data visualization tools like Tableau help us to visualize data and examine the results.

At Imarticus, we know the value of data science. With our Data Analytics and Machine Learning Course, you will learn the real-world application of data science. You can build significant models that will give insight into the business. You can also make predictions.

If you are looking for a career in data science and Analytics, our course will help you become a Tableau professional. We have a 100% track record of interview and placement after completing this course successfully.

Understanding regularization in machine learning

Posted on by Imarticus Learning

A machine learning model is a set of algorithm expressions that understands and analyses mounds of data to make predictions. Why is it that sometimes a machine learning model does great on training data but not so well on unseen data? It happens because, at times, this model becomes an overfitted model or even an under-fitted one.

Data fitting is very crucial for the success of this model. In this model, we plot a series of data points and draw the best line towards the relationship between the variables. 

This model becomes an overfitting one when it gathers the details with the noise present in the data and tries to fit them on the curve. 

The underfitting model neither learns the relationship between variables nor classifies a new data point. At Imarticus, we help you learn machine learning with python so that you can avoid unnecessary noise patterns and random data points. This program makes you an Analytics so you can prepare an optimal model. 

Meaning and Function of Regularization in Machine Learning

When a model becomes overfitted or under fitted, it fails to solve its purpose. Therefore, at Imarticus, we teach you the most crucial technique of optimal machine learning. In this program, we coach you to become an Analytics by learning the procedures to add additional information to the existing model. 

In the regularisation technique, you increase the model’s flexibility by keeping the same number of variables but at the same time reducing the magnitude of independent variables. This technique gives flexibility to the model and also maintains its generalization.

Regularization Techniques

The regularization techniques prevent machine learning algorithms from overfitting. It is possible to avoid overfitting in the existing model by adding a penalizing term in the cost function that gives a higher penalty to the complex curves. Regularization reduces the model variance without any substantial increase in bias. Python classes also help in this technique.

To become an Analytics, you have to understand these two main types of regularizations:

  • Ridge Regression
  • Lasso Regression

Ridge Regression:

In this type of regularization, we introduce a small amount of Ridge regression penalty bias for a better long-term prediction. It solves the problems when the parameters are more than the samples. It decreases the complexity of the model without reducing the number of variables. Though this regression will shrink the coefficients to the least dominant predictors, it will never make them zero.

Lasso Regression:

In this regularization technique, we reduce the complexity of the model. The penalty weight includes the absolute weight in the Least Absolute and Selection Operator. The coefficient estimate equals zero, and it provides the feature selection. 

But, if predictors are more than the data points, this model will pick non-zero predictors. This model also selects the highly collinear variables randomly. 

Data Analytics Certification 

The certification in AIML will train you as an Analytics. It will help you understand how regularization works. After completing the certification program at Imarticus, you will know to shrink or regularise the estimates to zero. You can also enhance the model that can accurately calculate the data.

How to get started in Python: An overview of recent trends

Posted on by Imarticus Learning

Are you very interested in programming? Then you need to know the programming language Python. No, it’s not exactly about pythons and snakes, so you can let your puppy loose.

Why Python, specifically? It’s approachable, simple, and adaptable to a range of situations. And because a growing number of programmers all around the world are using and appreciating it.

In fact, according to a recent rating published by IEEE Spectrum (a prestigious engineering and applied science newspaper), Python will be the most used programming language in 2020, followed by JavaScript, C++, C, and Java.

Python’s popularity has been stable in recent years, and this trend is unlikely to reverse. Python tutorials are the most popular on Google, according to the PYPL portal, and everyone wants to learn Python nowadays.  

This explains why Dropbox, Netflix, Facebook, Pinterest, Instagram, and Google all employ Python in their technical growth. Additionally, NASA is included in this list of “tech celebrities” that use Python. Do you see why it’s important for you to be aware of it?

Python is quite popular, and everyone wants to learn more about it. You, too, would not be reading this article if you weren’t.

Projects and programs made in Python

  • Netflix

Netflix, the platform that had a growth of 16 million subscribers during the first quarter of 2020, also uses Python. Its engineers prefer this programming language mainly because of its available libraries.

  • Instagram

Yes, the app you use to share images frequently uses the Python programming language on its backend (what runs on a server). In other words, Instagram is implemented on the open-source web development framework Django which is written entirely in Python.

  • Google

This is one of the big projects that also use the Python programming language, in addition to C++ and Java.

What are the characteristics of Python?

The Python programming language is known for being simple, quick, and having a short and easy learning curve. It is free to use and share because it was created under an open-source license.

But what does “multi-platform”, “multi-paradigm” and “interpreted” mean, here is the explanation:

– Multi-platform: Python can operate on a variety of platforms, including Windows, Mac OS X, Linux, and Unix.

– Multiparadigm: Because it is a programming language that allows a variety of programming paradigms (development models), programmers are not forced to utilize a particular style. Python supports which programming paradigms? Programming styles include object-oriented, imperative, and functional programming.

– Interpreted: Python “interprets” the programmer’s code, which implies it both interprets and executes it.

Python may also be used as an extension language for applications that require a programmable interface since it is dynamically typed (when a variable can take values of multiple kinds or adapts to what we write).

What is Python and what is it for?

Python is a multi-paradigm, multi-platform interpreted programming language used mostly in Big Data, Artificial Intelligence (AI), Data Science, testing frameworks, and web development. Due to its vast library, which has a wide range of features, it qualifies as a high-level general-purpose language.

In 1989, Guido van Rossum, a Dutch programmer, decided to construct an interpreter for a new scripting language he was developing.

His significant expertise in creating the ABC system – an interactive, structured, high-level programming language – aided his efforts to develop a language that was more intuitive, simpler, more powerful. Python, the successor of the ABC language, was born in 1991 (yep, he is a millennial at 29 years old).

Conclusion

At Imarticus we offer a Data Analytics course where you will learn more about how to get started in Python and you will receive more than an overview of recent trends. Visit our website today and enroll in one of our analytics programs. 

Top 7 career options in data analytics

Posted on by Imarticus Learning

The world of data analytics is constantly growing and changing. With the help of new technologies, we can do more with data than ever before. The data analyst field has seen massive growth in recent years. Data analysts use their skills and knowledge to analyze large data sets and turn them into meaningful information.

Companies or organizations can use it for business purposes such as making decisions on product lines or marketing campaigns or personal reasons like choosing a career path.

The job markets for data analytics are flourishing, and the number of jobs is growing. Data is everywhere, and a career in data analysis has never been more straightforward or promising. 

Data Analytics Careers: The Top Seven Choices

Data analytics is a booming industry, and the job market shows no sign of slowing down. Data Analytics jobs are in high demand across all sectors at every career level, from entry-level to executive management. There are numerous possibilities while choosing your career as a data analyst! 

Here are seven popular choices for entering the world of data analysis:

Data analyst: This is the most common role in data analytics and refers to a professional who extracts insights from data using various techniques, such as statistical analysis and machine learning.

Data engineer: Data engineers are in charge of designing, building, and maintaining the architecture and infrastructure for collecting, processing, and storing data.

Data architect: Data architects work with large quantities of complex data to design high-level structures that inform how they should get stored in a database or file system. This role is especially relevant in big data projects where you need an experienced professional dealing with terabytes of data.

Data scientist: A data scientist is a statistician who analyzes patterns in large sets of complex datasets to extract meaning and information that can be used for decision-making or reporting the findings back to the business stakeholders.

Business analyst: This role involves working with company executives, project managers, marketing teams, and other business professionals to identify and define business problems addressed with data analytics.

Data visualizer: Data visualization is the process of transforming data into graphical representations that are easy to understand, communicate and share. As a data visualizer, you’ll be responsible for designing and creating effective charts, graphs, and other information graphics to help others visualize the data.

Data manager:  Data Manager is responsible for designing and maintaining an enterprise-wide database and overseeing compliance with records management policies.

Learn Data Analytics online with Imarticus Learning

Learn the fundamentals of data science and critical analytics technologies, including Python, R, SAS, Hadoop, and Tableau, as well as nine real-world projects. This data analytics certification course helps students get in-demand future abilities and begins their career as data analysts.

What students draw from this course:

  • Students can participate in fascinating hackathons to solve real-world business challenges in a competitive scenario.
  • Impress employers & showcase skills with data analytics certification courses recognized by India’s prestigious academic collaborations.
  • World-Class Academic Professors to learn from discussions and live online sessions.

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

Here’s why music created by AI is better than you think

Posted on by Imarticus Learning

Artificial Intelligence or AI is capable of carrying out tasks that are much more advanced than just arranging words to generate lyrics. AI already has the ability to offer an immersive listening experience by adapting to a user’s preference. As seen in Spotify and Apple Music for a long time, AI systems understand the user’s preference and recommend songs that the user will enjoy.

AI has gone a step further and now is also able to compose completely personalized music for users. AI can understand certain benchmarks such as harmony, structure, and balance, using which, AI models can generate songs or background music based on the input provided by the user.

Is AI Capable of Creating Better Music Than Humans?

If AI is able to compose music without human supervision, people who need background tracks or copyright-free songs might not need music producers or artists as much as they currently do. Purchasing AI-created music also is easier as there are no royalties while the music generation process would be faster and available on demand.

Yes, with vast amounts of data and training, AI can help in creating a very capable autonomous music generation system, however, it will still be relying on historic data and other pieces of music in order to generate future songs. But, due to the vast amount of data available, the probabilities are limitless and if taught to truly identify good music, AI can become capable of generating hit songs one after another using the very same data.

Even coming up with new songs are just mathematical likelihoods for AI and by analyzing enough combinations, AI is bound to come up with good music. Similarly, meaningful lyrics can also be generated with Natural Language Processing or NLP. However, it will take a while till AI systems become as sensitive to the context of lyrics and innovative in using musical notes.

How AI is Helping in Creating Music?

Even though completely AI-generated music has not reached the Billboards Top 10 yet, services such as AIVA uses AI and Deep Learning models for composing soundtracks and music for users. This helps both small content creators and mainstream celebrities generate music for YouTube, Tik Tok, Twitch or Instagram. This is a cheaper alternative as well. Amper is another great online tool for content creators and non-musicians to make royalty-free music based on their own preferences and parameters. Amper has been created by the music composers who are behind creating the soundtrack for movies such as ‘The Dark Knight’. 

Alex the Kid is a UK-based Grammy-nominated music producer who has used ‘heartbreak’ as a theme and with the help of Machine Learning (ML) and Analytics, has created the hit song ‘Not Easy’. The song even features celebrity music artists such as Wiz Khalifa, Sam Harris, and Elle King.

The hit song had reached the 4th rank in iTunes’ ‘Hot Tracks’ chart within 2 days of its release. Alex used IBM Watson for analyzing billboard songs of the last 5 years as well as cultural and socially relevant content, scripts, or artifacts in order for including references to these elements within the song. Then, the producer used Watson BEAT, the ML-driven music generation algorithm powering the cognitive system for coming up with various musical backgrounds till he found the most suitable combination. 

Conclusion

Artificial intelligence and Machine learning courses can definitely help one learn AI topics for getting involved in interesting projects such as those mentioned above. A Machine Learning and Artificial Intelligence course, such as one offered by Imarticus, are essential for building AI systems such as soundtrack generators or lyrics generators. 

What’s happened to the data analytics job market in the past year?

Posted on by Imarticus Learning

A data scientist has been one of the topmost jobs people have been trying to land for a long time. And well after witnessing the benefits of data science and analytics in literally every sector, there is no wonder why. It helps in fields like education, retail, customer service, the health sector, and tourism. It helps corporate firms where it matters. That is, in processing, analyzing, managing, and storing a vast amount of data.

It also helps them to make predictions according to the changing market trends and client demands. This is why it is important to learn data analytics if you want to pursue a career as a data analyst. 

A lot of institutions offer good data analytics courses in India. Check out Imarticus Learnings’ data analytics certification course to hone your skills properly. This will provide you with enough exposure and real-life experience which, in turn, will help you land your dream data analytics job. 

However, last year saw the data analytics job falling behind in the charts for the first time. Now, is it finally coming down from its throne, or is it just another victim of the coronavirus? That is what we are trying to figure out here. Keep reading to learn more.

Is the market decreasing or a victim of Covid-19?

2020 saw a lot of upheavals globally. From educational institutions being shut down to corporate offices going on hiatus for months and some small businesses going out of business altogether, it was a year of getting used to the new normal. With that came the trend and the necessity to work from home.

Not to mention the terrible loss people faced all over the world. Unfortunately, with the new variant on the rise once again, the troubles seem far from over as of now. This also caused a lot of people out of jobs overnight. Not only that, but a lot of jobs went out of practice as well. 

People are still figuring out how to cope with this unprecedented situation. So, as of now, it is really up for debate as to what caused this upheaval in the hierarchy of job positions. Some things come into play though when it comes to changing market trends. Let us look at the situation by trying to analyze those.

Economic factors that factor into changing trends

About three major factors disrupt an ongoing situation, especially in the job market. Those are, as follows:

  • Demand: The reason why any job ranks as the topmost is its demand. Thankfully, the demand for a data analytics job is still very high, as it still ranks as number three on the list. So, the era of data science is far from over.
  • Supply: The supply of data scientists is quite low as of now. And, it seems that it is going to stay that way for years, so the job is going to keep reigning over for a long time.
  • Growth: Growth is a major factor when it comes to any job being relevant. And, the market for data scientists is still growing. In fact, if reports are to be believed, then this field saw an increase of about 650% since 2012. So, it is safe to say that the market will remain relevant in the coming years.

Conclusion

To begin your career as a data analyst, you need to learn from the best. Check out Imarticus Learnings’ data analytics course and boost your career to the max. 

What no one will tell you about data analytics job applications

Posted on by Imarticus Learning

Do you know what the data analytics job roles are? At Imarticus we look at the keys to this professional profile, what their work consists of and the main requirements to start a career as a data analyst. We also tell you all you should know about data analytics jobs.

We are surrounded by data that, while it may not mean much in its raw form, can give significant value to many businesses and organizations when analyzed and turned into information. It’s not about who has the most, but who gets the most out of it at the end of the day.

The data analyst is a specialist who converts data into information so that they may make better-informed judgments. To that goal, these experts complete the following tasks:

In the discipline of data engineering, consider the following:

– Data acquisition: 

  • Dataset identification: data may be found in a variety of places (e.g. databases, social networks, etc.).
  • Acquisition: strategies for retrieving data for data analysis and processing.
  • Review of the information gathered (structure).

– Preparation: 

  • Exploration: using strategies to gain a better understanding of the data through preliminary analysis and a study of its nature (correlation, trends…).
  • Data cleansing (incoherent, duplicated, incorrect values, etc. ), transformation, and packaging into useful/manageable structures for processing.

In the subject of computational data science, there are a few things to keep in mind:

– Analyze: by deciding on the best strategies and creating processing models (predictive models, classification, clustering, etc.).

– Dissemination of data analysis/processing outcomes.

– Using the model’s conclusions in real-world situations, such as decision-making.

Data analyst profile

Due to the incipient process of digital transformation that many firms and organizations that already have a huge quantity of data but don’t know how to use it to gain commercial benefits have begun to handle, the data analyst’s profile is one of the most in-demand today.

With the rise of new occupations coming from technology demand, such as data analysts, the necessary training to perform the activities of this profile may be obtained in a variety of methods. STEM (Science, Technology, Engineering, and Mathematics) degrees are the ideal place to start if you want to learn the fundamentals of this field.

There are also many postgraduate and master’s degrees available to become an expert in this sector, such as a master’s degree in Big Data Analysis and Visualisation / Visual Analytics & Big Data.

Requirements to be a good data analyst

– Communication skills: describing the outcomes of the task to company or organization managers and directors who do not have a technical background.

– Dashboard design and implementation experience, particularly in the area of business intelligence.

– Familiarity with distributed storage systems

– Technological and “Machine Learning” foundation: algorithm creation, programming languages and databases management, and so on.

– Computer science, mathematics, and statistics knowledge: these profiles must be able to analyze databases, construct models, and forecast statistics, among other things.

– The capacity to evaluate data and draw judgments based on it is critical.

– The capacity to synthesize data in order to derive meaningful and relevant information.

– Analytical and creative skills: methodical, systematic, and creative workers do their tasks carefully, analyzing and processing data to develop answers to issues or company demands.

– Business acumen: understanding of the industry and the activities of the firm for which you work, as well as the ability to apply that knowledge to identify problems that can be solved through data analysis and processing.

Conclusion

If you want to find out what data analytics job roles entail, at Imarticus, we look at the most important aspects of this profession, what they do, and what it takes to get started in your career as a data analyst. We also cover all you need to know about data analytics jobs.

The Changing Face of the Retail Industry with the Emergence of Data Analytics

Posted on by Imarticus Learning

The introduction of new technologies like data analytics has revolutionized the way we think about retail. Even the figure of the retail professional is changing and evolving. Companies are in a phase of change and are looking for new professionals who understand the difficulties, issues, and challenges of the sector.

Read on if you want to know how data analytics drives the retail business, and to find out more about the roles of data science and retail banking in this industry.

Data Analytics in the Retail Industry 

Today, companies operating in the retail sector leverage the power of data analytics more than anything to ensure business continuity and growth. Retail employees have traditionally had relatively little training in their area of work. This trend is changing and must change if retailers are to improve the shopping experience and be able to adapt to new customer demands.

In today’s world, customers are becoming more and more dependent on e-commerce and no longer depend on going to a store to get information and rely on what the salesperson tells them; rather, customers rely today on store personnel to get information or resolve doubts that they themselves have not been able to find or resolve online. This requires greater professionalization of employees to meet the customer’s demands at the point of sale.

Role of Data Analysts

Data analysis is the science of examining a set of data for the purpose of drawing conclusions about the information in order to make decisions or simply to expand knowledge on various topics, it is an indispensable tool for market forecasting and identifying good investment opportunities.

Many industries, like investment banks and retailers, are already using data analytics. With increasing competition in these markets, businesses are being shaped according to the demands of end-users. Data analytics is a key tool in helping them offer products and/or services that address these demands. 

Data Analytics for the Retail Industry

The retail sector is therefore increasingly demanding professionals with data analytics certification and marketing expertise, as analytical and creative skills are positively valued to find solutions in a changing environment. 

Many aspects of this type of company, from distribution to warehouse logistics, are changing and continue to change drastically in the coming years. Stores are and will be an important factor in a retailer’s sales, as the physical point of sale allows interaction with the customer that is impossible for now in online commerce.

Online sales are going to coexist with physical stores and therefore, new professionals with expertise in the omnichannel world who can relate to both worlds are required. Therefore, having trained staff capable of analyzing data, identifying weaknesses and strengths, and implementing the necessary changes in time will be indispensable for the retail industry to survive the technological revolution. 

Individuals with business analytics skills are being highly valued in these industries. At Imarticus, you can access data analytics courses online to learn how data analytics affects the retail industry. 

Why Imarticus for Data Analytics Online Course?

At Imarticus we offer a PGA Program in Data Analytics and Machine Learning design specifically for fresh graduates and early career professionals that want to pursue a career in Data Science and Analytics. We offer this industry-designed curriculum in partnership with many industry leaders.

During your formative years, we will provide you with real-life case studies via its data analytics courses that will train you for the real world. On completion of the data analytics program, our Imarticus team will guarantee you interview opportunities. Enroll today and begin our data analytics program!

How are Business Risks Predicted using Logistic Regression?

Posted on by Imarticus Learning

Logistic regression is a mathematical technique that estimates the probability of an event occurring. Using historical data to create a predictive model, you can use regression to predict business, investment, operational, and strategic risks. By understanding how these risks get indicated, you can better assess your company’s vulnerabilities and protect them from future losses.

This blog post will provide examples of how you might use regression in your workplace and explain what this technique does in more detail.

Why is Logistic Regression critical?

It is a statistical technique that tries to understand how the probability of an event occurring changes when one or more variables get altered. The method builds predictive models using data about previous incidents to use for proactively predicting future events. For instance, you could use regression to guess which customers are most likely to stop using your products and services.

Logistic regression can use to predict business risks in many ways, including:

  • Identifying the likelihood of a bad debt written off.
  • Assessing the probability that an IT system will cause downtime.
  • Estimating the risk that a new product or service will flop.

For example, suppose you are assessing the risk that a customer will default on their repayments. In that case, your model might include variables such as the loan amount and the borrower’s age. If you are trying to assess IT downtime risk, some variables might be how old a system is and its many users.

  • Assessing internal risk levels by quantifying how much staff turnover there has been over the past year. By using information about the average time, it takes for employees to complete their tasks.

For example, suppose you are trying to determine which product is most profitable. If you are trying to assess how quickly tasks are completed, some variables might be how long a study takes to complete and how many times it has met before.

  • You can use it to quantify the risk that you will not receive payment for goods or services supplied.
  • Assessing the likelihood of a customer is likely to leave your company’s favor based on variables. Such as their tenure, monthly spending, and how many requests they have made for support.
  • Predicting the probability of a new product being successful.
  • It determines the likelihood of a new employee bringing in a valuable new business.

Explore and learn with Imarticus Learning

This PG program is for industry professionals to help students master real-world applications from the ground up. Therefore students can construct strong models to provide meaningful business insights and forecasts.

This program is for recent graduates and early-career professionals who want to further their careers in Analytics, the most in-demand job skill. With this program’s job assurance guarantee, students may take a significant step forward in their careers.

Some course USP:

  • Risk management courses aid the students in learning job-relevant skills that prepare them for an exciting financial market career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.