Trending December 2023 # Want A Business Flare? Follow These Top Data Analytics Trends # Suggested January 2024 # Top 16 Popular

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In order to get the maximum out of technology, businesses are adopting data analytics trends

The power of

Top Data Analytics Trends for Business Moving to Scalable AI

Post the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chain.

Decision Intelligence as the Powerhouse of Decision Making

In modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable data.

Augmented Data Management to Shorten Data Delivery Time

The next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their architecture.

Edge Data and Analytics at the Core of Operations

The inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.

The Stronghold of the Cloud Continues

Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2023. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track trials.

No more Big Data, Let’s go to Small and Wide Data

For almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.

Automation at its Best

The power of data and analytics is no longer hidden. Today businesses of all sizes, starting from small to medium and big are availing data analytics in their routine to streamline operations. Without data analytics, companies are blind and deaf. Data analytics allows businesses to understand the market and their customers’ preferences and suggests solutions that could yield big profits. A rough estimation suggests that data analytics in business will increase five-fold by 2024 because of the rapid rise in technology adoption. Once upon a time, data analytics was confined to the tech industry. Only IT professionals, data engineers, and top-level enterprise executives got their hands on the technology. But things changed when laymen started embracing artificial intelligence. Today, big data, machine learning, cloud computing, data analytics, and many more technologies are a part of our everyday life. Many companies unveil data analytics in business to optimize business processes, cut costs, increase revenue, improve competitiveness, and accelerate innovation. In order to get the maximum out of technology, businesses should adopt recent data analytics trends. Data analytics trends such as decision intelligence, edge computing, data storytelling, etc are unraveling a world where businesses can understand their customers and address their needs like never before. In this article, Analytics Insight takes you through some of the top data analytics trends that businesses should follow in chúng tôi the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chúng tôi modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable chúng tôi next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their chúng tôi inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2023. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track chúng tôi almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.Business outcomes rely on data. But over the past few years, big data is getting more complex. For example, the inflow of data is in various forms like videos, images, documents, texts, files, etc. Besides, there are also two other categories called structured and unstructured data, which makes data processing even more hectic. The only way out of this is by automating the process of data discovery, preparation, and blending of disparate data. Besides, automating the data discovery and analysis process helps analysts focus on high-value-added activities.

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Top 10 Certification In Business Data Analytics (Cbda) Courses

Take a deeper dive into the domain of business data analytics with these top 10 certification courses

In the data-driven economy, providing value means being able to translate data rapidly into value-added information. Business

data analytics

is a practice by which a specific set of techniques, competencies, and procedures are applied to perform the continuous exploration, iteration, and investigation of past and current business data, to obtain insights about a business that can lead to improved decision-making. The role of a Business

data analytics

professional is strategic. They do not only focus their work on detecting insights and analyzing hypotheses. Besides projecting scenarios, this professional supports decisions focusing on the business goals.

When it comes to buzzwords used by businesses,

analytics

has got to be at the top of the most-used list.

Data Analytics

for, businesses are all about changing uncooked statistics and studying them with the purpose to set destiny traits and styles, which makes it positive for enterprise officers to have interaction with their enterprise operations innovatively. Here we have listed the top 10

certifications in business data analytics courses

The program led by the IIT Madras faculty aims at helping learners develop strong skillset including descriptive statistics, probability distributions, predictive modeling, Time Series forecasting, Data Architecture strategies, Business Analytics, and other skills to excel in this field.

In this course, Certification in Business Data Analytics (IIBA®- CBDA), you’ll learn what business data analytics is. First, you’ll explore the importance, value, and perspectives of business data analytics. You’ll learn how to differentiate business data analytics from business analysis and how the two are complementary skill sets. When you’re finished with this course, you’ll have the skills and knowledge of business data analytics needed to begin your business data analytics work.

This course will explain how to prepare for a data analytics project, how to organize the analytics process, and what to consider to make sure the results of your analysis will drive noticeable business change. You need to have the technical skills to collect and analyze it, but you also need to have the organizational skills to know when to do it and how to interpret the results to drive better decisions.

This new Certification in Business Data Analytics (IIBA – CBDA) provides an endorsement of your ability to accomplish end-to-end business analytics initiatives using data for informed decision-making. This certification will empower you not only to perform the Business Analyst’s role but also to work on end-to-end projects with data scientist professionals in Data Analytics/Business Intelligence Projects.

Earning this certification informs employers of your passion and competencies in performing business analysis on analytics initiatives. The certification helps identify skilled business data analytics professionals for organizations seeking these in-demand skills.

With this course, you will learn Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Predictive Modeling, Business Problem Solving, etc. On successful completion of the Program, you receive a verified certificate from the University of Maryland’s Robert H. Smith School of Business.

This Data Analytics program is ideal for all working professionals and prior programming knowledge is not required. It covers job-critical topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

The PMI Professional in Business Analysis (PBA) certification is designed for business analysts who work with projects or programs, or project and program managers who work with analytics. It’s offered through the Project Management Institute, which specializes in widely recognized project management certifications, such as the PMP. The certification focuses on business analysis training through hands-on projects and testing on business analysis principles, tools, and fundamentals.

Top 5 Data Collection Trends For Data

Data collection is becoming common practice for many businesses. Whether for implementing deep tech or conducting analytics, business leaders are continuously involved in gathering or using data to improve their operations.

As people realize the power of harnessing data, the regulations and practices of gathering and using it change. Considering that, business leaders must stay up to date regarding data collection and usage trends to maintain a consistent and useful flow of data across their business value chain.

This article explores the top 5 data collection trends to keep your data-driven business growing and to keep you informed of the latest developments.

Development in AI/ML models

As businesses try to automate more business operations, AI/ML models become more sophisticated and capable. For instance, a deep learning model can figure out its own parameters and learn how to improve itself. However, this means that not only do these models require a significantly larger amount of data to learn from, but they also have a much longer learning curve.

For instance, Facebook’s facial recognition system was trained with 4 million labeled images from 4000 people. This was back in 2014. Current facial recognition models require even larger datasets. The increase in dataset size is a trend that will continue to be observed.

You can check our data-driven list of data collection/harvesting services to find the best option that suits your project.

Development in rules and regulations

Data, a double-edged sword, can both be a powerful asset and a harmful liability. And to keep data usage and collection in check, there are regulatory measures being enforced.

Many countries are regulating data usage and sharing, making the rules more strict and comprehensive. The developments in regulations related to data collection, sharing, and usage will be another trend that will continue to be observed. Therefore, local companies need to thoroughly go through country-specific rules and policies that they operate in regarding data collection and usage before initiating any practices.

Rise of unstructured data

To understand this trend, let’s first have a look at structured and unstructured data.

Structure data

Structured data is normally stored in relational databases. It can be easily searched for by humans or software and can be placed into organized, designated fields. Examples include addresses, credit card or phone numbers. Unstructured data

Unstructured data is the opposite of structured data. It does not fit into predefined data models. And it can’t be stored in a relational database. Due to the various formats, conventional software can not process and analyze this data.

In other words:

In the past, structured data was the king. However, that has changed now, and unstructured data is more commonly used. This is because unstructured data is much more diverse than structured data and can provide more in-depth insights into things. Thanks to new technology such as AI, ML, computer vision, etc., unstructured data can now be analyzed and used in various ways to benefit a business.

Studies show that the volume of unstructured data was 33 zettabytes in 2023 and is projected to grow to 175 zettabytes (175 billion terabytes) by 2025. With the surge in the adoption of AI/ML-based solutions, the use of software to organize unstructured data rises as well, and companies continue to gather unstructured data.

Data stored in different tiers

Since the volume of data being generated and used continues to increase, business leaders are refocusing their efforts on data management strategies, including data storage and protection technology. Another trending practice to better manage data is data tiering. Organizations with strong digital maturity are tiering their data based on:

Data volume: How much they have, and the growth rate.

Data variety: The type of data they have, data storage details, and the accessibility of the data.

Data velocity: The speed at which data is generated.

Data priority: The impact of the data on the business operations.

Based on these considerations, data is stored in different tiers.

Data diversity

Bias in AI is becoming an increasing concern among businesses. For instance, studies show that AI-enabled facial recognition systems show more erroneous results for darker skin women, men, and children as compared to people of lighter color.

This bias can be reduced through reevaluating training of AI/ML models and diversifying training datasets. Diversifying the data collected for training AI/ML models is another trend that is being observed. For instance, IBM and Microsoft are taking steps to optimize their facial recognition system toward racial and gender neutrality.

For more in-depth knowledge on data collection, feel free to download our comprehensive whitepaper:

Top 10 Big Data Analytics Trends And Predictions To Watch For In 2023

Data Analysis Automation

Recently the automation has turned out to be highly favoured technology almost across every industry to enhance business potentials. Not much to the surprise, we can expect 40 percent of database work to get automated by next year. Hopefully, automation will also assist business leaders to efficiently see further ahead to assist in propelling their organization with the appropriate analytics to drive decisions.

IoT Merged with Data Analytics

By the year 2023, we can expect to witness 20 billion active IoT devices which will subsequently collect more data for analysis. In big tech organization where IoT devices have already been embraced in big operations, the leaders are seeing beyond it to also implement the assisting technology to run capable data analytics. Therefore, we are likely to acknowledge more analytics solutions for IoT devices to provide relevant data along with transparency. Additionally, around 75 percent of companies might suffer while accomplishing matured benefits of IoT due to lack of data science professionals.

In-Memory Computing

In 2023, in-memory computing is likely to get highly influential since the reduction in the cost of memory resulted in turning IMC more mainstream. Being a mainstream technology, IMC can be a great solution for a varied range of benefits in the analysis. The latest persistent-memory technologies have led to a reduction in cost and complexity of IMC. Persistent-memory tech is a new memory tier well situated between NAND flash memory and dynamic access memory. As the wide scale implementation of IMC solution is manageable, several industries are adopting in-memory computing to help improve application performance while providing a great opportunity for future scalability.

Data-As- A-Service

Expectedly, up to 90 percent of big organizations will be generating some kind of revenue from DaaS (Data-As-A-Service) in 2023. It is a cloud-based technology that enables customers to access digital files using the internet. With high accessibility, the globalization of this technology will also support bridging gaps between departments within the larger organizations who require sharing data but currently can’t do so. Sharing data in real-time will be quicker and easier through DaaS. It will also improve productivity within the organization.

Augmented Analytics

Augmented analytics is about to become dominant in the coming years. The technology has shaken up the industry by merging AI and ML techniques to create fresh ways of creating, developing, sharing and consuming analytics. It is no at all surprising that

Smart Cities Development

IoT is creating new opportunities for data science and analytics. The development of Smart Cities has mandated the need for data collection as well as data processing and dissemination. Possibly, smart cities data will assist with medical nursing and proactive health care. It has been predicted that by 2023, 30 percent of the smart cities will have introduced robotics and smart machines at the medical facility. The technology can be leveraged to provide a good user experience to residents.

Consumer Device Developments

The current trends with personal devices, mobile and web use showcase the possibility that by 2023 more than 50 percent of consumer mobile interactions will be experiences comprehended at contextualized and hyperpersonal that is determined by the user’s past and real-time mobile behavior. As mobile devices are being used in a variety of settings from at home to at work and many other places, and the development of all kinds of new products like IoT, wearables and immersive technologies like virtual reality.

Enterprise Content Management

The disruptive technologies are gradually taking over the tasks of humans with 95 percent of image and video content which expected to be audited by machines by 2023. The ECM market is expected to hit $59.87 billion by 2023. Also, the 95 percent of content reviewed by machines is likely to never be viewed by humans rather the machines vetting content will provide detailed analyses in the capacity of supporting organizations’ digital initiatives. Subsequently, IT departments can leverage such analyses to enhance productivity and welcome new opportunities in mobile, social and cloud technologies.

ML And Cloud

As cloud storage has already become quite a popular means of safely storing digital files, currently, 30 percent of cloud vendors are using third-party solutions in the form of infrastructure as a service (IaaS) in place of running their infrastructure. The process is predicted to rise to 60 percent in the next 3 years. Also, projections for 2023 state that the hyper-scale cloud providers including Microsoft, Apple and Google will be making use of cloud-based machine learning to gain a 20 percent share of the market in platforms for data science.

Conversational Analytics and NLP

The futuristic trends for 2023 say that up to 50 percent of analytical queries will be either automatically generated or generated using voice or NLP technology provided that analytics tools should be easy to use and access. This development will allow anyone in a company to analyze complex data combinations using a widely adopted and user-friendly analytics platform.

Big Data Analytics is astonishingly transforming the industries and organization today. The technology has made a huge shift where businesses are adapting it to go beyond the traditional ways of analysis. The strength of data analytics is positively embraced by enterprises across the globe. It is making some remarkable changes in the decision-making landscape for branding and recruitment. Till now, we have seen big data analytics making a massive shift in how business is being done but it would be exciting to see what the technology holds for us in the coming year. Therefore, let’s have a look at top data analytics trends and predictions to watch for 2023.Recently the automation has turned out to be highly favoured technology almost across every industry to enhance business potentials. Not much to the surprise, we can expect 40 percent of database work to get automated by next year. Hopefully, automation will also assist business leaders to efficiently see further ahead to assist in propelling their organization with the appropriate analytics to drive chúng tôi the year 2023, we can expect to witness 20 billion active IoT devices which will subsequently collect more data for analysis. In big tech organization where IoT devices have already been embraced in big operations, the leaders are seeing beyond it to also implement the assisting technology to run capable data analytics. Therefore, we are likely to acknowledge more analytics solutions for IoT devices to provide relevant data along with transparency. Additionally, around 75 percent of companies might suffer while accomplishing matured benefits of IoT due to lack of data science chúng tôi 2023, in-memory computing is likely to get highly influential since the reduction in the cost of memory resulted in turning IMC more mainstream. Being a mainstream technology, IMC can be a great solution for a varied range of benefits in the analysis. The latest persistent-memory technologies have led to a reduction in cost and complexity of IMC. Persistent-memory tech is a new memory tier well situated between NAND flash memory and dynamic access memory. As the wide scale implementation of IMC solution is manageable, several industries are adopting in-memory computing to help improve application performance while providing a great opportunity for future scalability.Expectedly, up to 90 percent of big organizations will be generating some kind of revenue from DaaS (Data-As-A-Service) in 2023. It is a cloud-based technology that enables customers to access digital files using the internet. With high accessibility, the globalization of this technology will also support bridging gaps between departments within the larger organizations who require sharing data but currently can’t do so. Sharing data in real-time will be quicker and easier through DaaS. It will also improve productivity within the organization.Augmented analytics is about to become dominant in the coming years. The technology has shaken up the industry by merging AI and ML techniques to create fresh ways of creating, developing, sharing and consuming analytics. It is no at all surprising that augmented analytics have already become the most popular technology to use for business analytics. The benefits of augmented analytics include– 1. ability to automate many analytics capabilities like preparation, analysis 2. building of models, as well as the insights generated, will be much easier with which to chúng tôi is creating new opportunities for data science and analytics. The development of Smart Cities has mandated the need for data collection as well as data processing and dissemination. Possibly, smart cities data will assist with medical nursing and proactive health care. It has been predicted that by 2023, 30 percent of the smart cities will have introduced robotics and smart machines at the medical facility. The technology can be leveraged to provide a good user experience to chúng tôi current trends with personal devices, mobile and web use showcase the possibility that by 2023 more than 50 percent of consumer mobile interactions will be experiences comprehended at contextualized and hyperpersonal that is determined by the user’s past and real-time mobile behavior. As mobile devices are being used in a variety of settings from at home to at work and many other places, and the development of all kinds of new products like IoT, wearables and immersive technologies like virtual chúng tôi disruptive technologies are gradually taking over the tasks of humans with 95 percent of image and video content which expected to be audited by machines by 2023. The ECM market is expected to hit $59.87 billion by 2023. Also, the 95 percent of content reviewed by machines is likely to never be viewed by humans rather the machines vetting content will provide detailed analyses in the capacity of supporting organizations’ digital initiatives. Subsequently, IT departments can leverage such analyses to enhance productivity and welcome new opportunities in mobile, social and cloud chúng tôi cloud storage has already become quite a popular means of safely storing digital files, currently, 30 percent of cloud vendors are using third-party solutions in the form of infrastructure as a service (IaaS) in place of running their infrastructure. The process is predicted to rise to 60 percent in the next 3 years. Also, projections for 2023 state that the hyper-scale cloud providers including Microsoft, Apple and Google will be making use of cloud-based machine learning to gain a 20 percent share of the market in platforms for data chúng tôi futuristic trends for 2023 say that up to 50 percent of analytical queries will be either automatically generated or generated using voice or NLP technology provided that analytics tools should be easy to use and access. This development will allow anyone in a company to analyze complex data combinations using a widely adopted and user-friendly analytics platform. Well, the predictions and futuristic trends for 2023 are leading the development of the Big Data Analytics world. Data and analytics platforms’ offerings are extremely influenced by such predictions and technology providers of these solutions will be leveraging changes based on the current forecasts.

Top 10 Business Analytics Master’s Degree For 2023

Here is the list of top 10 business analytics master’s degree courses for you for 2023 1. MBA by Massachusetts Institute of Technology (Sloan)

Duration: 1 Year

Fee: US$137,265

Enrollment Type: Full-time

In just 12 months, the MIT Sloan Master of Business Analytics program prepares students for careers that apply and manage modern data science to solve critical business challenges. By the end of the 12-month program, you will have completed anywhere from 111 to 141 units of classwork. MBA students can take up to 66 units per term, with a maximum of 54 units from MIT Sloan courses. The program answers the industry’s demand for a skilled pool of graduates who can apply data science to solve business challenges. MIT offers an analytics degree at the undergraduate, graduate, and doctoral levels.

Enroll Here.

2. chúng tôi in Business Analytics by Carnegie Mellon University (Tepper)

Duration: 9-Months

Fee: US$34,706

Enrollment Type: Full-time

The Tepper Full-Time Master of Science in Business Analytics (MSBA) is a STEM-designated program created for recent college graduates who want to deepen their analytical skills and move into sought-after business analyst positions. Through this new 9-month program on Carnegie Mellon’s future-focused campus, Tepper MSBA students learn how to solve tough business challenges by harnessing emerging technologies and using data creatively to inspire better solutions. Equipped with a full range of state-of-the-art business analytics techniques, Tepper MSBA graduates tell stories through and extract insights from data.

Enroll Here.

3. chúng tôi in Analytics by Georgia Institute of Technology (Scheller)

Duration: 1 Year

Fee: Georgia Residents: US$41,800

Students Out of State: US$56,000

Enrollment Type: Full-time

The Master of Science in Analytics is an interdisciplinary analytics and data science program that leverages the strengths of Georgia Tech in statistics, operations research, computing, and business by combining the world-class expertise of the Scheller College of Business, the College of Computing, and the College of Engineering. By blending the strengths of these nationally ranked programs, graduates will learn to integrate skills in a unique and interdisciplinary way that yields deep insights into analytics problems.

Enroll Here.

4. MBA from the University of Pennsylvania (Wharton)

Duration: 2 Years

Fee: US$83,230

Enrollment Type: Full-time

This Business Analytics MBA major is designed to build deep competency in the skills needed to implement and oversee data-driven business decisions, including collecting, managing, and describing datasets, forming inferences and predictions from data, and making optimal and robust decisions. Business analytics makes extensive use of statistical analysis and the course teaches about the applications of business analytics spanning a variety of functional areas.

Enroll Here.

5. chúng tôi in Business Analytics by New York University (Stern)

Duration: 1 Year

Fee: US$85,500

Enrollment Type: Full-time

Graduates of this program will be equipped to influence decision-making and strategy, and they will ultimately drive better business results by gaining the ability to transform data into a powerful and predictive strategic asset. Business Analytics is critical in preparing organizations to solve 21st-century business challenges and participants of this program will have exposure to innovative methodologies that support data-driven decision-making.

Enroll Here.

6. MSc in Business Analytics by Pepperdine University

Duration: 1 Year

Fee: US$30,695 per term

Enrollment Type: Full-time

Starting every fall and spring term, the one-year, full-time Pepperdine Master’s in Business Analytics (MSBA) program will tackle some of the most pressing challenges facing businesses today from both the decision-sciences and information-systems perspective. With a focus on predictive analytics, risk management, optimization modeling, and information technology, this program is designed for students seeking an analytical career in evidence-based organizations that rely on big data.

Enroll Here.

7. MSc in Business Analytics by Syracuse University

Duration: 2 Years

Fee: US$1,734 per credit hour

Enrollment Type: Full-time

Utilizing an action-oriented approach, the STEM-designated Whitman School M.S. in business analytics is designed to prepare you with the skills to become a data-driven business leader and decision-maker. You will complete 36 credit hours of courses that develop an interdisciplinary understanding of the applications of analytics to the fields of accounting, finance, marketing, and supply chain management by using techniques for data collection, data visualization, statistical and pattern analysis, and data mining.

Enroll Here.

8. Master of Business Analytics by the University of Dayton

Duration: 12 Months

Fee: US$1,430 per credit hour

Enrollment Type: Full-time

The Master of Business Analytics (MBAN) program at the University of Dayton is for students interested in moving into this high-demand career area. Business Analytics represents a global growing need with employment opportunities readily available. Businesses and governments depend on the effective use of data through sophisticated analytical techniques. The program can be completed in as little as 12 months. The curriculum provides intensive analytics coursework as well as a broad view of the analytics professional role.

Enroll Here.

9. MSc in Business Analytics by American University

Duration: 12-24 Months

Fee: US$61,578 (Depends on the Enrollment Type)

Enrollment Type: Both Part-time and Full-time Courses are Available

Enroll Here.

10. MBA by the University of California, Berkeley (Haas)

Duration: 15 Months

Fee: US$66,709

Enrollment Type: Full-time

This STEM-certified MSBA program is a 15-month on-campus experience that will give the students a deep understanding of the tools to harness the power of data to gain insights and tell data stories that drive strategic business decisions. After completion of this program, the students will become invaluable assets to the industry, quite early in their careers. And since these MSBA graduates will have such a critical influence on business strategy and decisions in the future, the university also makes sure they are trained to achieve ethical outcomes.

Top 10 Certification In Business Data Analytics (Cbda) Courses

Top 5 Data Collection Trends For Data

Top 10 Big Data Analytics Trends And Predictions To Watch For In 2023

Top 10 Business Analytics Master’s Degree For 2023

Top Data Analytics Interview Questions & Answers Updated For 2023