Trending December 2023 # Ml Trends For Solving Business Intelligence Problems # Suggested January 2024 # Top 14 Popular

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This article was published as a part of the Data Science Blogathon


In September 2023, Gartner released a separate report on artificial intelligence technologies, ranging from conceptual concepts like neural networks to hardware implementations of Machine Learning algorithms in the form of industrial robots and unmanned vehicles.

Notably, in this report, autonomous vehicles (drones, unmanned cars, and other vehicles) are now at the bottom of frustration, while Automatic Machine Learning (AutoML), Explainable AI (XAI), chatbots, and other conversational user interfaces are at the peak of inflated expectations. And in general, speech recognition systems and video card-based process acceleration (GPU) tools have reached a productivity plateau.

From the point of view of industrial applications, the most promising are technologies for creating robotic software for the automation of production processes and calendar planning. These trends correlate with the most in-demand trends in the Internet of Things, which people have been discussing since 2023.

ML Trend #1 – AutoML

AutoML is Automated machine learning or AutoML. To tell the truth, the AutoML paradigm is about having one big button that lets you “build a good model”.

The popularity of such tools grows every day, but it is too early to talk about AutoML as a stand-alone approach, especially in the context of large corporations.

ML Trend #2 – XAI

We are talking about Explainable Artificial Intelligence (Explainable AI or XAI).

The fact is that it is extremely important for the business user to understand the logic behind the decision-making, which is more typical for areas of activity that were historically dominated by easily interpretable models such as logistic regression or decision trees (calculation of credit risks, targeted marketing, insurance).

Recently, methods like LIME and XSHAPE have been closing the gap between interpretation and accuracy and, judging by the activity in the academic environment, they are expected to spread further.

ML Trend #3 – RL

RL or Reinforcement Learning is learning with reinforcement and is essentially a development of the concept of continuous A/B testing with the only difference that instead of two segments we have thousands of them, and the process functions continuously.

Once it was used only for games, but in recent years it has been used to solve great business problems. Today experts continue to improve the methods of using RL for business. There are already lots of successful application cases on the market:

– choosing the most appropriate campaign in marketing optimization;

– personalization of pages and mailings in digital marketing;

– work with bad debts in credit risks, etc.

ML Trend #4 – graph analytics

Graph analytics refers to a set of methods that focus on analyzing the structure of relationships between entities, rather than the properties of those entities. For example, connections between people in social networks, connections of bank account through transfers passing through these accounts, different ownership structures, etc.

The methods of graph analytics are used to analyze the structure of relations and to identify non-obvious relations. As for the ML problems, graph analytics gives the opportunity to build stronger predictors – variables that describe the neighborhood of the entity of interest. For example, we can answer the question of how the credit rating of the firm is affected by the rating of its counterparties.

Using methods of graph analytics, you can be limited not only to direct links but also to neighborhoods by links of different lengths.

Today graphs are successfully used to analyze entities that have a “natural” network structure, such as social networks. It is predicted that graphs for entities with non-obvious network structures will become more and more frequent. Such graphs are good for building sequences of customer events or analyzing cause-effect relationships for marketing communications management tasks.

ML Trend #5 – ModelOps

MLOps (Machine Learning Operations) is a kind of DevOps for machine learning that helps standardize the process of developing machine learning models and reduces the time to roll them out into production.

MLOps helps to break down the barrier between Data scientists and Data engineers. What often happens is that the Data Scientist experiments develop a new model, gives it to the Data Engineer, and goes off again to set up new experiments and try new models. And the Data Engineer tries to deploy that model in production, but since he was not involved in its development, it may take about several months to do so. It can take up to six months from the time he starts developing the model to its deployment in production. All that time the model is not working and useful, the data becomes obsolete, and the reproducibility of experiments becomes an issue.

With MLOps, the new model is quickly put into production and begins to benefit the business. MLOps also solves model tracking, versioning, and model monitoring tasks in production.

If you use the MLOps approach and special tools, like Kubeflow, in conjunction with proper planning, such as at chúng tôi you can significantly speed up the process of rolling out experimental models into production, which means that they will solve business problems faster.

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Business Analytics Vs Business Intelligence

Business Analytics vs Business Intelligence

Business Intelligence is one of the most important aspects of data analysis and is an integral part of modern companies. The definition of business analytics techniques is somewhat ambiguous and is constantly changing according to the changing dynamics of the companies. In a nutshell, business analytics can be defined as a set of applications, practices, skills, and technologies that help companies make strategic and vital decisions, thereby allowing the company to achieve its goals and ambitions.

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Let us study much more about Business Analytics and Business Intelligence in detail:

After understanding the importance and immense potential of data analytics, many brands and organizations have started investing many resources in them. However, most of this data analytics is limited to dashboards and reports, whereas the field of data analytics is large and has many more possible opportunities. While the popular forms of data analytics are essential, it is necessary to understand that many forms of data analytics can come together to help brands become empowered in their decisions and choices. At the same time, it is essential to remember that companies are becoming more independent and keen on expanding their horizons through technology, so they must identify the value of data and its interaction at all possible stages.

The ability to break down concepts and gain a proper insight into how data functions can help companies build and manage applications independently. At the same time, this insight can help companies gain knowledge about how various units of a company work together on the one hand and the requirements of the IT sector to develop products and services that can enable effective communication and goal achievement hand.

The article on Business Analytics vs Business Intelligence search is structured below.

Business Analytics vs Business Intelligence Infographics

Below infographics on Business Analytics vs Business Intelligence throws light on the significant differences between the two.

Why is understanding data so important for companies?

While it is easy to understand why data is an essential aspect of modern companies and brands, there are also also certain pitfalls related to it. The first and most important is security, while the other two include integrity and accuracy, which are equally, if not more important. Once these three things have been guaranteed, determining effective results through data analysis is the only important thing left. Every company knows that data is used to provide valuable insights. When brands are armed with these insights, they can make decisions that improve their overall functioning and management. However, rarely is data used in a raw state; they have to be processed and presented so that they can apply strategically and comprehensively.

The latest analytical tools make it much easier for companies to gain these insights, but there is always a journey to make this data usable and valuable. Maintaining data accuracy at all stages is extremely important because inaccuracy in data can lead to wrong insights, and,, if implemented,, can affect the entire functioning of the company negatively. That is why the quality of the data sample is much more important than the quantity of data. Many companies,, instead of focusing on the quality, focus on gathering large amounts of data without thinking about whether it is correct or incorrect. Added to this, integrity plays a vital role in data analytics.

What is business intelligence skills?

We can analyze and investigate business performance using multiple methods to restructure it and achieve profitable gains and solutions. Top analytics consulting firms believe that business intelligence skills and analytics techniques will undergo significant changes and greater adoption in the coming years. Many analytics feel that companies will now shift from information technology reports to developing business intelligence tools capable of delivering informed choices about companies’ growth strategy and development. These will lead to four significant changes, faster processing capabilities, mobile applications, social decision-making models, and more spending on solutions providers.

Business analytics techniques have the power to process data at a much more rapid pace.

The amount of data available in a company is almost endless. To make sense of this data, there is a need to handle this vast data systematically and quickly. Today, BI analytics has gone mainstream, and even small and new companies are looking at using this technique to harness the immense potential in the market. Besides, many companies need this technology to forge ahead and explore newer opportunities and challenges. That is why analytic marketers are searching for new methods to create business analytical tools that can quickly process data and can be adopted by companies across different sectors. With tools that can be used across IT teams, these business analytical tools redefine how companies function and carry.

Business analytical tools are at the next stage of development, namely mobile applications.

Mobile smartphones are gaining rapid acceptance across the globe. According to a new report, almost 2 million worldwide will have smartphone access by the end of this year. Business marketers must look for new ways to integrate smartphones into business analytics techniques. Besides, many marketers and company professionals rely on mobile phones to keep them updated on the functioning of their company, especially when they are traveling or away from their offices. Business analytics companies are looking to invest in mobile BI functions, and software designers will soon look at manufacturing products aimed at mobile phones rather than desktop users. With many companies and brands already going mobile, business analytics techniques on smartphones already have a ready audience.

Business analytical tools should enable companies to make decisions on social platforms.

Social media platforms are viral and present in almost all countries worldwide. Today, all companies are on social media platforms, making it essential to have business analytical tools that integrate social network capabilities with decision-making capabilities. While this might be a little tricky and complex, integrating business analytical skills with social networking may no longer be an option but a requirement in the coming years.

Increased spending on business analytics techniques consulting

With so many complex and practical applications available in the market, experts feel that there will undoubtedly be an increase in business analytics techniques consulting, especially in the coming years. Companies are pressuring business analytics firms to provide faster and better tools to help achieve their business analytical goals.

Business Analytics vs Business Intelligence, How are they different?

This is how business analytics techniques can help companies. Now coming to BI. Defined as a technology-driven process for analyzing data and presenting actionable information to help companies, BI encompasses a lot of business intelligence tools, applications, and methodologies. BI is, therefore, an umbrella term and a focused concept. Both business analytics techniques and business intelligence skills are related terms. They are generally strategies and decisions that can help companies across sectors like research and development, customer care, credit, and inventory management. Both of these help companies meet business challenges and use fresh opportunities that arise within the sector.

Business analytics techniques and BI can have far-reaching consequences for the functioning of brands and companies across categories. Some areas they can help impact include critical product analysis, improved customer service, up-selling opportunities, simplified inventory management, and competitive price insights. By allowing companies to understand customers’ and client’s needs in real time, they can help maximize resources effectively and minimize losses.

Business Analytics vs Business Intelligence – Future

But with time, this will become a necessity because social media is a growing platform that no company can ignore, not today and not in the coming years. That is why many corporate companies are now looking at BI programs that can help them not just upgrade their decision-making abilities but also reduce their operational costs and help them use existing opportunities.

Data interpretation and manipulation methods of choice keep changing according to the market’s requirements. That is why companies must be clear about the tools and techniques they use to reach their eventual goal. When companies understand the flexible nature of the economy and their business, it becomes much easier for them to handle these changes through tools that can reach the goal, even in challenging situations.

Conclusion – Business Analytics vs Business Intelligence

In conclusion, Business Analytics vs Business Intelligence both have immense potential, and there are a lot of challenges present in both these sectors, primarily related to the field of technical and social networking. Companies must remember that business analytics techniques are not the same as BI. The requirements of one field are different, and so are the benefits of each of them. A company can only use technology effectively if it invests in it and uses it in a proper and systematic manner. By working with end-users, consultants can help companies use the right tools to use the data to make decisions that will empower the brand and take them to the next level of growth and development.

Recommended Articles

This has been a guide to Business Analytics vs Business Intelligence – How they are different. They both can help brands & companies use correct data to reach their goals. These are the following external link related to Business Analytics and Business intelligence, so go through the links for more details.

Business Intelligence Architect (Sap Bw

Designation – Business Intelligence Architect (SAP BW / Qlikview)

Location – Pune

About employer – Datwyler

Job description:


Reporting to the Head of ERP Services will have global responsibility for leading and overseeing the complete life cycle design and development of our enterprise data warehouse, data marts, analytics reports and dashboards.

The successful candidate will need to communicate effectively with business stakeholders at all levels of the organization and translate their information needs into technical requirements. Additionally, the successful candidate would apply Business Intelligence design and development best practices to develop and deploy robust analytic solutions.

As new information sources are being integrated into the business intelligence platform, the business intelligence architect would collaborate with the business stakeholders as well as IT stakeholders to ensure and validate that the platform is delivering accurate, complete and timely information that drives informed decision making.

You will be responsible for the technical architecture and maintenance of the Business Intelligence architecture and tools including, SAP BI/BW and QlikView and other 3rd-party data warehouse applications.

You will design and develop ETL, dimensional models, reports and dashboards and work closely with the ERP developers to develop and implement end to end solutions.

You will provide support maintenance, issue management, issue analysis, troubleshooting, issue resolution, validation and testing for existing and new data marts, reports and dashboards in all the BI platforms and tools (SAP BW, Qlikview).

You will identify and analyze existing data sources and develop queries to extract source data to support analytic report and dashboard requirements.

You will analyze quality of existing data and work closely with the ERP Services team to recommend application enhancements to insure data integrity.

You will provide content knowledge to the management and you will support the full lifecycle of solutions from execution of new projects through operation to departure.

You will facilitate the business requirements gathering sessions and ensure the information is captured/ documented as deliverables out from the business requirements gathering sessions.

You will analyze the business requirements comprehensively together with the business process owners, verifying the underlying business needs recommending analytic report/dashboard solutions that meet business needs as well as leverage business intelligence discipline best practices.

You will provide end user training and develop effective training material.

You will assist in outlining new developments business case justification to ensure that proposed solution is capable of delivering articulated business benefits to the business owners.

You will develop, manage and maintain change management documentation to comply with IT change management processes

Qualification and Skills Required

Bachelor’s degree in Business, Information Services, or Computer Science is required

Preferred experience in Life Sciences industries (Pharmaceutical, Medical Devices, Medical Products, Biotech).

Strong experience building and enhancing BI objects such as Info Objects, Info Cubes, Info Sources/Data Sources, Master Data, Transformations, Update Rules, ODS, Extractors, Info sets, Aggregates, Process Chains, User Exits

Strong experience of ETL from SAP and Non-SAP data sources

Experience in ABAP development, as needed for ETL.

Strong experience with BI reporting tools; experience in Qlikview will be considered a strong plus

Business/functional process from an extractor perspective and reporting knowledge in the ERP areas: Sales and Distribution, Production and Planning, Financials and Controlling, Quality Management and Materials Management.

Strong knowledge of ITIL or COBIT frameworks

Expert in implementation of projects (ASAP Methodology)

Planning and estimating the work and resources required to deliver

Monitoring and controlling schedule, cost & effort variance

Client relationship management, strong negotiation and collaboration skills.

Demonstrated skills in creative and critical thinking, teamwork, decision-making and time management.

Excellent written and verbal communication and presentation skills.

Experience of working in a large and complex organization.

Ability to work independently and as part of a team

Willingness to travel

Languages: Fluent English, Dutch or German a plus

Interested people can apply for this job can mail their CV to [email protected] with subject as Business Intelligence Architect (SAP BW / Qlikview) – Datwyler Sealing – Pune

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C++ Program For Solving Cryptarithmetic Puzzles

   m := 1    for each letter i from right to left of word1, do       ch := word1[i]       for all elements j in the nodeList, do          if nodeList[j].letter = ch, then             break       done       val1 := val1 + (m * nodeList[j].value)       m := m * 10    done    m := 1    for each letter i from right to left of word2, do       ch := word2[i]       for all elements j in the nodeList, do          if nodeList[j].letter = ch, then             break       done       val2 := val2 + (m * nodeList[j].value)       m := m * 10    done    m := 1    for each letter i from right to left of word3, do       ch := word3[i]       for all elements j in the nodeList, do          if nodeList[j].letter = ch, then             break       done       val3 := val3 + (m * nodeList[j].value)       m := m * 10    done    if val3 = (val1 + val2), then       return true    return false    if n letters are assigned, then       for all digits i from 0 to 9, do          if digit i is not used, then             nodeList[n].value := i             if isValid(nodeList, count, word1, word2, word3) = true                for all items j in the nodeList, do                   show the letter and corresponding values.                done                return true       done       return fasle    for all digits i from 0 to 9, do       if digit i is not used, then          nodeList[n].value := i          mark as i is used          if permutation(nodeList, count, n+1, word1, word2, word3),             return true          otherwise mark i as not used    done    return false #include<vector< using namespace std; vector<int< use(10); struct node {    char letter;    int value; }; int isValid(node* nodeList, const int count, string s1, string s2, string s3) {    int val1 = 0, val2 = 0, val3 = 0, m = 1, j, i;       char ch = s1[i];       for (j = 0; j < count; j++)          if (nodeList[j].letter == ch)             break;             val1 += m * nodeList[j].value;             m *= 10;    }    m = 1;       char ch = s2[i];       for (j = 0; j < count; j++)          if (nodeList[j].letter == ch)             break;             val2 += m * nodeList[j].value;             m *= 10;    }    m = 1;       char ch = s3[i];       for (j = 0; j < count; j++)          if (nodeList[j].letter == ch)             break;             val3 += m * nodeList[j].value;             m *= 10;    }    if (val3 == (val1 + val2))       return 1;    return 0; } bool permutation(int count, node* nodeList, int n, string s1, string s2, string s3) {    if (n == count – 1){       for (int i = 0; i < 10; i++){          if (use[i] == 0){             nodeList[n].value = i;             if (isValid(nodeList, count, s1, s2, s3) == 1){                cout << “Solution found: “;                for (int j = 0; j < count; j++)                   cout << ” ” << nodeList[j].letter << ” = ”                   << nodeList[j].value;                return true;             }          }       }       return false;    }    for (int i = 0; i < 10; i++){       if (use[i] == 0){          nodeList[n].value = i;          use[i] = 1;          if (permutation(count, nodeList, n + 1, s1, s2, s3))             return true;             use[i] = 0;       }    }    return false; } bool solvePuzzle(string s1, string s2,string s3) {    int uniqueChar = 0;    int len1 = s1.length();    int len2 = s2.length();    int len3 = s3.length();    for (int i = 0; i < len1; i++)       ++freq[s1[i] – ‘A’];    for (int i = 0; i < len2; i++)       ++freq[s2[i] – ‘A’];    for (int i = 0; i < len3; i++)       ++freq[s3[i] – ‘A’];    for (int i = 0; i < 26; i++)          uniqueChar++;       cout << “Invalid strings”;       return 0;    }    node nodeList[uniqueChar];    for (int i = 0, j = 0; i < 26; i++) {          nodeList[j].letter = char(i + ‘A’);          j++;       }    }    return permutation(uniqueChar, nodeList, 0, s1, s2, s3); } int main() {    string s1 = “BASE”;    string s2 = “BALL”;    string s3 = “GAMES”;    if (solvePuzzle(s1, s2, s3) == false)       cout << “No solution”; }

Output Solution found: A = 4 B = 2 E = 1 G = 0 L = 5 M = 9 S = 6

One More Thing Artificial Intelligence Can Beat You At: Solving A Rubik’s Cube

Scramble a Rubik’s cube, and you will create one of 43 quintillion possible arrangements of those 54 colorful square stickers. But that part—the messing it up part—is easy. Solving it, as any amateur knows, is hard.

People are capable of figuring it out, of course, and doing so astonishingly quickly. The best, like 2023 champion Philipp Weyer, solve it in less than 7 seconds. And generally, the whizzes who specialize in getting the jumbled cube back to sides of pure red, blue, green, white, yellow, and orange, make that happen in around 50 moves.

While humans have been solving these puzzles for decades, it’s time for artificial intelligence’s turn: AI can now quickly compute a very efficient solution to a scrambled cube. And 60 percent of the time, this AI will calculate a solution that involves the fewest possible moves, which is generally around 20 or so. In fact, there’s a concept in the world of the Rubik’s cube known as God’s algorithm, which would be the way to solve a cube if an all-knowing deity eyeballed it and simply knew how to solve it in the fewest possible moves. “We are close to God’s algorithm,” says Pierre Baldi, a computer science professor at the University of California, Irvine, and the senior author on a new study describing the Rubik’s-Cube-solving bot in the journal Nature Machine Intelligence.

Before you start picturing a robot with mechanical fingers manipulating a cube and climbing atop a podium at speedcubing competitions, consider that this AI creation is just software. It solves the cube virtually. In fact, there is a decades-long tradition of using games as challenges for artificial intelligence systems, and they can already dominate at contests like chess, Go, and multiplayer Texas hold ’em poker.

When it comes to the Erno Rubik’s 1974 puzzle, traditional programs could already produce a solution to a scrambled cube using rules-based computing, but the news here is that a type of AI called deep reinforcement learning can now do so.

Since the Rubik’s cube is so complex, you can’t just expect an AI system to figure it out without training. And just virtually twisting and turning it and trying to solve it randomly definitely doesn’t work, either. Instead, the researchers behind the project began with baby steps—a cube that is very close to its solution, and just needed a few moves to complete. They progressed through “scrambles of increasing complexity” while teaching it, Baldi says.

“It’s like a child,” he says. “We first give it easy problems, and then progressively harder problems.”

So how does this algorithm stack up—how good a speedcuber is it? A version of the Baldi team’s algorithm is online, and you can try it out here. That version takes only around a second to examine a scrambled cube and then produce a solution. Its solution will be significantly less than the 50 moves or so a human typically uses to solve a cube in competition, but it’s less likely to produce a solve that’s perfectly minimal. Meanwhile, the version of the AI that the researchers report in their paper is more powerful but slightly slower: it can produce the shortest possible solution 60 percent of the time, but the computational delay for that is around 20 seconds long, according to Baldi. Still, that’s much, much faster than it would take a human, a cube in their hands, to figure out a solve that involves a minimal number of moves.

In comparison, remember that humans can do this in around 6 seconds, but since they’re working in the real world, they have to physically twist and turn it. Speedcubers can actually solve the cube using fewer moves than 50, but the faster method by time is actually for them to not to do in the fewest possible twists.

The cube is an elegant puzzle, because while there are quintillions of different ways to mess it up, and many routes to take to solve it, there’s only one destination to get to: the solved cube.

Software engineers use games as a framework for building AI algorithms, but also keep an eye on the ways that software that can play games could also be applied to real-world situations. In this case, Baldi says there could be applications in the field of robotics. For example, he imagines a robot that cleans up your kitchen. Like the cube, a kitchen can be scrambled, or dirty, in many different ways, but there’s just one solved state: a clean cooking space, with everything in its place. Algorithms like the cube-solver could be applied to situations like this one. “If the robot was to move things randomly—take dirty dishes and move them randomly around in the kitchen—the kitchen would never get cleaned,” he says. “You [can] see the similarity between certain robotic tasks and what we did.”

Business Intelligence Course (18 Courses Bundle, Online Certification)

About Business Intelligence Certification Course

Course Name Online Business Intelligence Training Course Bundle

Deal You get access to both the 18courses, Projects. You do not need to purchase each course separately.

Hours 156+ Video Hours

Core Coverage Business Intelligence using Microsoft Excel, Business Intelligence with Tableau, Predictive Modeling using Minitab and SPPS, SAS – Predictive Modeling, Data Science, Big Data and Hadoop

Course Validity Lifetime Access

Eligibility Anyone who is serious about Business Intelligence and wants to make a career in this field.

Pre-Requisites Knowledge of R, SAS Programming Language would be useful

What do you get? Certificate of Completion for each of the 18courses, Projects

Certification Type Course Completion Certificates

Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced skills

Type of Training Video Course – Self Paced Learning

System Requirement 2 GB or above RAM

Other Requirement Speaker / Headphone

Online Business Intelligence Course Curriculum



Course Highlights

Project Highlights


This course focuses on how business intelligence uses business analytics tools that make it easy to combine data from multiple sources, analyze and visualize information. It helps trainees in making more informed and better decisions to guide the business. After the completion of the course trainee will be through with all the concepts of business intelligence.


The objective of this course is to assist the folks in running a business strategically. One of the main objectives of this training is to train you on all the concepts that are related to business intelligence. The purpose of the Business Intelligence training program is to support better business decision making. Topics like BI – Business Intelligence, Business Intelligence using Microsoft Excel, Business Intelligence with Tableau, BIP – Business Intelligence Publisher using Siebel Analytics using Tableau, Predictive Modeling Training and, SAS Features for Starters are covered in the training program.

Course Highlights Project Highlights

This training program includes seven training modules which will be covered briefly through the video modules. So the project will cover all the training modules and various sub-points Projects on R and Tableau – Customer Analytics is the first project where you will work on how customer analytics using R and Tableau is beneficial in unlocking the customers’ details. You will be focusing on all the topics that you would have covered in this course to draft a solution for this project. Projects on R and Tableau – Pricing Analytics is next in which you will be working on how pricing analytics can be done using R and Tableau. This project will increase your understanding of the course and how R and Tableau are used. Project – Predictive Modeling using Minitab is next to where you will be working on how predictive modeling can be performed using Minitab. The concepts will be explained through videos and all kinds of queries will also be solved by the mentor. Project – Predictive Modeling using SPSS will be the next project in which you will be Predictive Modeling skills across different business domains. It will be explained through a video lector how the project is supposed to be done. +6 More Courses and Projects10 More Projects and courses will be there in the course. After completing all of these projects, you will be able to work effectively in business intelligence and will be able to resolve all BA based problems practically.

Certificate of Completion

What is Business Intelligence

Business Intelligence can be defined as a set of methods or techniques which can be deployed to handle a large amount of data and extract meaningful information out of it. BI is used by organizations across the globe for the day today as well as the critical decision-making process. It helps organizations to leverage the technology into their core businesses which in turn helps them focus on the merits and demerits of their business. They get important insights into what is working well and what needs improvement.

The important tangible skill that users will learn from this Business Intelligence training certification are as below: –

Hands-on coding skills with many languages and frameworks such as R and python.

Practice and use of tools such as Tableau, Minitab, SPSS and SAS.

Exposure to Excel and Macros.

Exposure to Hadoop and its fundamentals.

Data visualization using Tableau.

SQL skills and basics of Oracle.

For BI perspective, SQL, Tableau, SAS, and Excel is a must which each BI profession should know. Rest is more towards the role of data science and hence can be defocused if someone gets overwhelmed with the content quantity.

Each of these skills is quite comprehensive and it is impossible to learn them all at a time. This Business Intelligence training course tries to shed light on important aspects of languages, frameworks, and tools mentioned above but still, there is huge room for further learning and upskilling. This Business intelligence certification course covers only the introductory and fundamental parts of the above techniques. This acts as an initial push to make the candidate comfortable with certain tools and technologies and once a candidate picks it up, they can further learn all topics or specialize in any one of them by themselves without any guidance or hand-holding.


This Business Intelligence certification course is suitable for people with a technical background. Graduates in engineering, technology or basic science with sufficient exposure to programming can take this course. Following are the pre-requisite for a complete understanding of this course: –

Familiarity with any one programming language such as C or C++.

Familiarity with Windows or Linux operating system.

Strong understanding of high school level mathematics.

Ability to expresses ideas and explain findings to people, however, this skill can be learned in this business intelligence certification course as well.

Ability to learn new skills quickly and adapt to new technologies and skills.

Target Audience

This Business Intelligence training is suitable for a wide range of people who come from varied backgrounds and skill-set. Thus the target audience for this Business intelligence certification  course could be one of the following: –

Fresh Graduates in engineering such as B. Tech or M. Tech, computer programs such as BCA or MCA and similar people with similar degrees who are looking for a job in a competitive market.

People working as database developers, backend developers or junior analysts who want to switch to more exciting career options with better pay and cutting-edge opportunities.

People working as an architect, product owner, product manager, scrum master, team lead, etc. who want to exploit the benefits of business intelligence and data science in their projects.

Already working BI developers who want to upskill themselves and learn new technologies that came into market science they last touched the book.

FAQ’s- General Questions

In this Business Intelligence certification course, we post questions that our students ask frequently before the make the decision to enroll in this course: –

What is going to be the demand for this Business Intelligence skill five years from now?

This Business Intelligence certification course teaches BI and predictive analytics tools. The natural succession of these skills is machine learning and artificial intelligence which are going to be the big thing in the coming decade. Hence the foundational understanding of what is being taught in this Business intelligence certification course is a must to flourish in the era of big data, machine learning and AI.

Why should I enroll in this Business Intelligence course and not similar to another course in the market?

This Business Intelligence training is probably the only one where users get to learn a wide variety of tools under a single curriculum. No other course probably covers so much. Students can learn R, Python, Excel, Hadoop, Big Data, SPSS, SAS, Minitab, Tableau, Siebel, etc.

Does this course provide a certification which shall help me in finding a job?

Yes. This business intelligence training certification provides a verifiable certificate in which employers can check for its authenticity and hence supports learns not only in learning but also invalidating the skillset.

Career Benefits

Career Benefits of this Business Intelligence training course are many. To start with, users learn new skills which are very important for the software industry. Those who do not upskill themselves, perish in the long run. Apart from upskilling, it makes the candidate ready for the job market is a challenging and growing field of business intelligence, predictive modeling, data analysis, and data science. Students learn practical skills with hands-on coding practices and case studies solving skill which removes any kind of hesitation that candidates can have before applying for new jobs and fill them with confidence. Other benefits include career progression, promotion in the current job by taking new responsibilities and learning to solve critical business problems. Managers and people in leadership roles can learn skills from this Business intelligence certification course to provide guidance and mentorship to junior developers who are going to code in Business intelligence-related projects or solve predictive analytics problems. Corporate training, hence, is also given in this field.




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