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Tag: data analytics career

What 60% of data analytics learners do wrong

Posted on by Imarticus Learning

Data science is a field that is as demanding as it is difficult. It has become a necessary part of our lives. Whether managing education, retail or corporate, data analytics has come in really handy in recent years. Corporate especially is a field where data analytics helps a lot as there are always big amounts of data to be processed. It is in no way an easy job. The job market is also very demanding, but thankfully numerous positions are being offered across the globe. 

This is why if you are thinking of switching to a data analytics career, then you should learn data analytics properly. Fortunately, a lot of institutions in India offer compact courses on it. Such an institution is Imarticus Learnings who offer a solid data analytics certification course with placements. This will not only cover the basics of ‘what does a data analyst do’ but also hone your skills to a different level. Now, here, we are going to elaborate on some primary mistakes that a majority of data analytics learners do wrong to help you avoid them altogether. Please read on to learn more.

What does a data analyst do?

A data analyst needs to process big data, including the current trends of a market, the inefficiencies present in the current system of a company, changing market trends, changes in customer demands, and so on very quickly. This is the only way to analyze certain problems and address them accordingly. Data analysts need to make suggestions for a more profitable approach for the company that they are in. They also need to collaborate with other departments to make a plan that works for all and even supervise it regularly. So, mistakes are not appreciated.

The mistakes to avoid

There are some primary mistakes that beginners end up making that can become harmful to their careers. They are, as follows:

  • Jumping into things headfirst: You need to analyze the problem first properly before jumping into conclusive solutions. The best way to deal with this is to scope the entire value of delivery from the get-go. This comes in really handy later as it gives a clear value of what data science can bring with each step.
  • Exploratory Data Analysis (EDA) is a must: Although EDA might seem like a tedious aspect, it is a must. It gives you the edge in both competitions and real-life projects. Skipping it entirely and jumping straight into modeling can turn out to be a real problem later on.
  • Spend time on feature engineering: This is directly linked to your building models. You need to spend enough time building predictive parameters after the initial processing and cleaning of a data set. Although directly jumping to grid searches and model building without this might work in some cases, that does not work well when you are trying to build a proper score.
  • Global models are part of the process: It is necessary to have the entire picture in mind before getting into projects seriously. This will help you make a plan with minimum efficiency and easier structures if the client has limited resources.

 

  • You also need to talk to domain experts regularly as they can provide insights you might overlook sometimes.
  • Know the basics properly.
  • Improve your connections.

Conclusion

The job can seem intimidating at first, but there are also some seriously interesting aspects to it. For a better understanding, learn data analytics with Imarticus Learnings’ data analytics certification course to give your career the boost it needs.

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.

Which languages should you learn for data analytics?

Posted on by Imarticus Learning

Data science is a fascinating topic to work in since it combines high statistical and mathematical abilities with practical programming experience. There are a variety of programming languages in which a prospective data scientist might specialize.

In this article, we will tell you how by learning machine learning and taking a python course you can obtain a Data analytics Certification. 

big data analytics courseWhile there is no one-size-fits-all solution, there are various factors to consider. Many factors will determine your performance as a data scientist, including:

  • Specificity: When it comes to sophisticated data science, re-inventing the wheel each time can only get you so far. Master the numerous packages and modules available in the language of your choice. The extent to which this is feasible is determined by the domain-specific packages that are initially accessible to you! 
  • Generality: A smart data scientist will be able to program in a variety of languages and will be able to crunch statistics. Much of data science’s day-to-day job is locating and processing raw data, sometimes known as ‘data cleaning.’ No amount of clever machine learning software can assist with this. 
  • Productivity: In the fast-paced world of commercial data science, getting the work done quickly has a lot of appeal. This, however, is what allows technical debt to accumulate, and only rational procedures may help to reduce it.
  • Performance: In some circumstances, especially when working with enormous amounts of mission-critical data, it’s crucial to maximize the performance of your code. Compile-time languages are often substantially quicker than interpreted languages and statically typed languages are far more reliable than dynamically typed languages. The clear trade-off is between efficiency and productivity.

These can be viewed as a pair of axes to some extent (Generality-Specificity, Performance-Productivity). Each of the languages listed below can be found on one of these spectra. 

Let’s look at some of the more popular data science languages with these key ideas in mind. What follows is based on research as well as personal experience from myself, friends, and coworkers – but it is by no means exhaustive! Here they are, roughly in order of popularity:

    • R: R is a sophisticated language that excels in a wide range of statistical and data visualization applications, and it’s open-source, which means it has a vibrant community of contributors. Its current popularity is a reflection of how effective it is at what it accomplishes. 
    • Python: Python is a fantastic language for data research, and not only for beginners. The ETL process is at the heart of most of the data science processes (extraction-transformation-loading). Python’s generality is appropriate for this task. Python is a tremendously interesting language to work with for machine learning, thanks to libraries like Google’s Tensorflow.
    • SQL: SQL is best used as a data processing language rather than as a sophisticated analytical tool. Yet ETL is critical to so much of the data science process, and SQL’s endurance and efficiency demonstrate that it is a valuable language for the current data scientist to grasp. 
    • Java: There are several advantages to studying Java as a primary data science language. Many businesses will value the ability to easily incorporate data science production code into their existing codebase, and Java’s performance and type safety will be significant benefits. However, you won’t have access to the stats-specific packages that other languages provide. That said, it’s worth thinking about, especially if you’re already familiar with R and/or Python.

 

  • Scala: When it comes to working with Big Data using cluster computing, Scala + Spark are wonderful options. Scala’s characteristics will appeal to anybody who has worked with Java or other statically typed languages. However, if your application doesn’t deal with large amounts of data, you’ll likely discover that adopting alternative languages like R or Python will increase your productivity significantly.

 

Conclusion

At Imarticus we commit to giving the best quality education, so if you are interested in getting a data analytics certification, taking a python course, and learning machine learning come and visit us! 

Related Article:

https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/

Python for Data Science: 5 concepts you should remember

Posted on by Imarticus Learning

Python for Data Science: 5 Concepts You Should Remember

The cheat sheet is a helpful complement to your learning since it provides the fundamentals, which are organized into five sections, that any novice needs to know to get started on data analytics courses online with Python. When learning data science, you should also have python training. Here are the main concepts. 

5 concepts in Python for Data Science

  • Variables and data types: Before you begin learning Python, you must first understand variables and data types. That should come as no surprise, given that they form the foundation of all programming languages.

Variables are used by the computer program to name and store a value for subsequent usages, such as reference or modification. You assign a value to a variable to save it. This is known as variable assignment, and it entails setting or resetting the value stored in one or more places identified by a variable name.

    • String instruments: Strings are a fundamental building component of computer languages in general, and Python is no exception. When it comes to dealing with strings, you’ll need to understand a few string operations and procedures.

 

  • Lists: Lists, on the other hand, will appear to be more useful right away. Lists are used to keep track of an ordered collection of elements that may or may not be of distinct sorts. Commas divide the elements into a list, which is encased in square brackets.
  • Tuple: A tuple is an ordered collection of immutable objects. Tuples are lists of sequences. Tuples and lists vary in that tuples cannot be altered, although lists may, and tuples use parentheses while lists use square brackets.

 

  • Dictionaries and Libraries: Python dictionaries allow you to link together disparate pieces of data. In a dictionary, each item of data is kept as a key-value pair. Python returns the value associated with a key when you specify one. All key-value pairs, all keys, and all values may be traversed. When you’ve mastered the fundamentals of Python, though, it’s time to move on to the Python data science libraries. You should look at pandas, NumPy, scikit-learn, and matplotlib, which are the most popular.

Installing Python

If you haven’t already, you should install Python now that you’ve covered some of the fundamentals. Consider installing Anaconda or another Python distribution. It is the most popular open data science platform, and it is based on Python. The most significant benefit of installing Anaconda is that you have immediate access to over 720 packages that can be installed via conda.

However, a dependency and environment manager, as well as Spyder’s integrated development environment, are included (IDE). As if these tools weren’t enough, you also receive the Jupyter Notebook, an interactive data science environment that lets you utilize your favorite data science tools while easily sharing your code and analyses. In a nutshell, everything you’ll need to get started with Python data science!

After you’ve imported the libraries you’ll need for data science, you’ll probably need to import the NumPy array, which is the most significant data structure for scientific computing in Python.

Conclusion

Here at Imarticus, we offer python training and tools to learn data science via our data analytics courses online. Come visit us today and start your career in data science online. 

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Python Coding Tips For Beginners

Python For Beginners – What Is Python And Why Is It Used?

Why is Python of Paramount Importance in Data Analytics?

Python Developer Salary In Terms Of Job Roles

Learn NLP: How are chatbots created?

Posted on by Imarticus Learning

Chatbot, conversational bot, Artificial Intelligence assistant, intelligent virtual assistant, conversational agent, digital assistant, conversational interface, we find endless names, some more accurate than others, to refer to this technology. Experts do not agree on which one is the best or what subtle differences there are between each one, but what is clear is that they are everywhere.

Conversational assistants answer countless questions and tasks, such as buying a train ticket, knowing the stock of a product in a store, buying movie tickets, ordering food at a restaurant, or checking the weather in your city with the mobile.

It is common to use Machine Learning and Natural Language Processing in Artificial Intelligence to create these chatbots, achieving that, based on examples, they are able to detect what the user needs through text and to maintain a conversation with concrete and coherent answers. With the CIBOP program from Imarticus, get an opportunity to learn more about chatbots and how Natural Language Processing with python can achieve this.  

Types of Chatbots

Although it is clear that these machines have the purpose of making our lives a little easier, there are different types of chatbots depending on the purpose they have:

  • Some assistants have the purpose of maintaining an unstructured conversation, imitating those of the people. A good example of this is BlenderBot, from Facebook, designed to be able to carry on a conversation as if it were a human: with its own personality, showing empathy, knowledge, feelings, etc. 
  • Others are designed for short conversations and are also capable of solving certain specific tasks. For example, Apple’s Siri, which is capable of following short dialogues and responding to tasks such as sending a message, setting an alarm, or searching for a song.
  • Another type is chatbots specialized in specific tasks for specific domains. These are tools that provide solutions to limited complex problems, such as booking a flight, ordering food, analyzing health problems, or, for example, buying a train ticket. 

Normally these chatbots use Machine Learning and Natural Language Processing techniques to provide solutions and respond to user needs. 

Within the Natural Language Processing techniques, they need the understanding of natural language (NLU) to understand what the user has said and to be able to respond to it (for this, they use the intentions, entities, and dialogue flows). On the other hand, using natural language generation (NLG) they are able to return answers prefabricated or custom responses through, for example, query databases.

Steps To Create a Chatbot

But the important question that arises here is how do you create a chatbot? There are platforms that help to design a conversational agent, analyze data from conversations, search databases, or train chatbots in a relatively simple way. Some of the many available on the web are Language Understanding (LUIS) of Microsoft, Google Dialogflow, or Watson Assistant IBM.

These tools are usually based on intentions, entities, and flows of dialogue to build conversational agents. By integrating Natural Language Processing with python, chatbots can be specialized in specific tasks depending on the demand. We, at Imarticus, offer Natural Language Processing courses to learn and create chatbots.

Is a Chatbot the Same as a Virtual Assistant?

Some specialists believe that what differentiates a bot from a virtual assistant is the high degree of customization of the latter. In this way, while the chatbot is the face of a company, to whose codes or particularities the user has to adapt to achieve their goal, it is the personal assistant who adapts to the user and not the other way around. 

Does a Tableau Certification Really Matter? Here’s What You Need To Know

Posted on by Imarticus Learning

Tableau presently offers five different certifications. Here, we’ll go through each one, their distinctions, and the necessity of combining them with the Data analytics & machine learning course that we provide at Imarticus in order to get a decent Tableau certification salary.

What does it mean to get certified in Tableau?

Certification in any tool is a method to add proof of your abilities to your resume. As we develop in our usage of the tool and strengthen our analytical skills, the different levels assist in establishing a step-by-step approach to building our framework of possibilities with it.

Once we’ve determined it’s time to earn a Tableau Certification, we must follow what Tableau refers to as Exam Guide Prep, which is a series of instructions or recommendations that must be followed before taking any of the three levels of Tableau Desktop or the two levels of Tableau Server. 

big data analytics courseCertification Format

You may download the certification preparation guide from various pages to go over the different points on what is actually necessary to consider so that you can walk into the test with the best possible preparation. This is a $250 fee-based certification that is valid for two years from the date of purchase.

First and foremost, the test consists of a total of 36 questions that must be answered in two hours. These 36 questions might come in a variety of forms:

  • Practical questions (or Hand-On Questions): These are questions in which the statement indicates which file we must utilize (it’s in a folder on the virtual machine’s desktop) as well as the analysis’s real query. The use of level of detail (or LOD) expressions, table calculations such as the difference from, the difference in percentage from a ranking, or even a moving average calculation, and, finally, answering the question using different graphs such as a Bullet Graph, a Pareto Chart, or a Box and Whisker Plot are all examples of these types of questions. 
  • Theoretical questions: These may be divided into two categories: those in which we are asked if the argument is true or wrong, with only one potential answer, and those in which we are asked to choose all that apply, in which case more than one answer is required to respond correctly. These questions cover a wide range of topics, including how to use various data sources in Tableau Desktop, different types of computations or ways to integrate data in the tool, and various actions and tools that may be used to get the most out of Tableau’s interactivity.

The minimum passing score for the certification is 75%, which must be achieved on both types of questions that a candidate may encounter on the test. However, not all questions on the test are equal in value; those with practical substance will be the most useful, followed by multiple-choice questions with theoretical information, with true/false and single-choice responses coming in last. It’s worth noting that Tableau requires all potential options to be picked for the right response, thus a partially accurate answer will not get you any points.

The test is 2 hours long in total, although it is advised that a longer time be allocated in case there are additional duties relating to the virtual machine’s configuration and for the proctor to ensure that everything is in order before beginning the exam.

Skills to be Assessed

These four abilities will be examined during the exam, according to the Tableau Desktop Specialist study guide: 

  • Connect to and prepare data 
  • Exploring and analyzing data 
  • Sharing information 
  • Understanding Tableau concepts

Conclusion

Data literacy is more critical than ever before. There’s always something new to learn at college, whether you’re a freshman or a senior. Here at Imarticus, we encourage all our students taking the Data analytics & machine learning course to learn Tableau to access the Tableau certification salary in the market today. Come and visit us at Imarticus to learn more about Tableau Certification.

How Data Visualization Helps Data Analysts Make Effective Decisions Faster

Posted on by Imarticus Learning

In most organizations, the challenge is not the lack of data, but the excess of data. Companies have such a large quantity of data that it is difficult for them to organize and use it as a tool to prioritize activities. A large amount of data is often spread across different departments, including marketing, and sales.

Proper data visualization through algorithms can help a company reason through chaos. It can filter the data and digest it in a way that detects the right prospect at the right time. It also provides the rich context needed to greatly improve the efficiency of a company or a segment of it, like for example the sales team. 

Data Analysis for Effective Decision Making

Data visualization helps data analysts give an explanation and rationale as to why a recommendation is made, as well as contextual data from relevant applications, such as customer relationship management, which allows an individual to act on the recommendation more intelligently and effectively. 

Intelligent data analysis throughout proper data visualization allows the breakdown of information based on specific criteria. When a proper data analysis is carried out, not only is time saved but also, companies are able to support their decisions on facts and can be confident that they are making the most effective strategy.

Role of data analysts

Data analysts are in charge of examining a set of data with the objective of deriving conclusions in order to make decisions or simply to expand knowledge on a specific topic for which they contain information. Almost any industry or company can profit from proper data analysis if they have the required means of data visualization.

Data analytics is a key tool that helps recommend products, services, actions, or decisions that address a specific demand. Data analysts also extract value from data thanks to visualization tools. Today many universities worldwide offer data analytics courses and data visualization courses that help people become experts in delivering commercial strategies for many industries.

Why Imarticus for data analysis online course?

Becoming a data analyst will open many doors to an enormous number of possibilities in many industries. It will give you professional analyst skills, new ways to make key data-driven decisions on aspects like how, when, or where to deploy resources, or how to engage prospects and leads more effectively.

You will become of value to the industry, as you will be key in eliminating their reliance on guessing outcomes and relying on gut instincts to make critical decisions. You can subscribe to a data analytics course offered by us at Imarticus and become a well-profiled professional in this new and demanded field!  

best data analytics certification courses in IndiaAt Imarticus we offer a Program in Data Analytics and Machine Learning that has been designed for fresh graduates and early career professionals that are seeking to pursue a career in Data visualization and Analytics.

This industry-designed curriculum is offered in partnership with many industry leaders, which will be key in providing you with real-life case studies, via our data analytics courses, that will train you for the real world during your formative years. Once you have received your data analytics certification, we will guide you through interview opportunities. Come and join our Imarticus team today! 

Conclusion

The value of data visualization for data analysts is increasingly obvious when compared to the past when there were no systematic methods to determine the next action on any given customer or even of a company. Thanks to professionals with data analytics certification, today companies are able to gauge the true impact of the data, by tracking metrics against previous practices and environment, in order to make effective decisions faster.

How Data Science is Making Personalization of Customers Feasible?

Posted on by Imarticus Learning

How Data Science is Making Personalization of Customers Feasible?

Data science opens the door to an enormous number of possibilities in customer experience management. It plays an increasingly important role in all areas of the customer relationship management lifecycle, but countless companies have yet to make this advanced technology part of their marketing tools.

One of the main reasons is the lack of full visibility of what can help them engage better with customers and the inability to quantify potential improvements. Nowadays, with the amount of information available to both consumers and businesses, the key to success is knowing how to offer personalized offers that appeal to each consumer. 

Data Science for the Hypersonalization of Customers

To better understand how data science can make sales and marketing actions more effective, it helps to think about one of the main responsibilities of these groups: acquiring new customers. To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help a company increase sales by effectively identifying those who represent real business opportunities. 

Intelligent data analysis allows the segmentation of leads based on their specific criteria, such as needs, purchasing power, geographic location, and other exclusionary criteria. In this way, it is possible to optimize prospecting efforts, allowing companies to increase their closing rates and, ultimately, business profitability. 

Role of Data Science

Data science extracts value from data through the combination of multiple fields, such as statistics, artificial intelligence, and data analytics. Data science involves the preparation of data for analysis, including steps such as data gathering, scrubbing, presentation, and manipulation. Data scientists can pursuit analytical operations and are able to review results to reveal patterns and enable businesses from different fields to gain informed insights.

To optimize commercial strategies in a highly competitive market, working around qualified leads is the basis for success. In that sense, data science can greatly improve projections and help you increase sales by effectively identifying those who represent real business opportunities. Today, more and more people are opting for a Data Scientist Career, as it is in increasing demand in many industries.

Why Imarticus for data science online course?

Not only is data science being key for market forecasting and finding good investment opportunities but also for smart marketing. As competition in the market increases, it is becoming more and more necessary to shape the business according to the demands of end-users. Data science makes it possible to offer products/services that address the needs of each user. 

Here at Imarticus, we offer an industry-designed curriculum on DSP Data Science Prodegree. In partnership with many industry leaders, we will introduce you to real business projects and case studies, throughout high-quality tech-enabled education. With one of our courses at Imarticus, not only will you learn data science, but also, we will provide you full placement upon completion of the program.

Conclusion

Data science opens a door to an enormous number of possibilities in customer experience management. It gives sales and marketing professionals a new way to make key data-driven decisions on how to deploy resources and engage prospects and leads more effectively, eliminating the reliance on guessing answers and relying on gut instincts in making critical decisions. You can subscribe to a data analytics course in India offered by Imarticus and become a well-profiled professional in this field! 

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.