Data Science and Business Analytics Online Course
Data Science is amongst the most prominent attributes of every sector and business operations as it is helpful in getting relevant insights and making business decisions based on it. It is a multidisciplinary field primarily based on facts, trends, and statistics for data science. In the emerging scope of data.
There has been a drastic technologic shift in a couple of years which results in the creation of huge amounts of data every single day. In a 2018 article, the World Economic Forum claimed that by 2025, over 463 exabytes of data will be created globally every day, the equivalent of 212,765,957 DVDs per day.
Foundations of Data Science is more about giving targeted solutions rather than a quality solution applicable to all business operations. It mainly focuses on using the strategies to extract and dissect data specific to definite domains or sectors. We can observe its use and functions in various sectors like healthcare, education, finance, retail, etc. With the help of its techniques, healthcare has found better solutions to take care of the patients, the education sector is providing better opportunities to students, the banking industry is more focused on providing the best customer service and more.
Introduction to Data Science and Related Terms
Data science is a field of study which mainly focuses on extracting meaningful and relevant insights by studying the huge amount of data available and then it uses those insights to recommend action plans for potential growth. It is an interesting combination of technology, fact, and statistics along with a good knowledge of business operations.
It is important to understand the following terms to have clarity about the field:
1. Data Collection
As per a recent study, over 2.5 quintillion bytes of data is generated every single day. The number is expected to grow exponentially with more businesses opting to capture and collect user data. It is important for businesses to understand the type of data to be collected.
3. Data Cleaning
Collecting data is not complete until it goes through check processes to retard any unnecessary data. This step is important in terms of reducing the complexity and also making sure that the collected data is efficient for the analysis.
2. Business Requirements
It only makes more sense to collect data once you know where, how, and for whom it has to be used. Understanding business requirements is key as it helps companies to get critical information that can be used to fulfill their goals and objectives.
4. Data Exploration and Data Analysis
Sometimes, there’s a need to employ data science tools to get the right understanding of the patterns of data. This process is called Data exploration. You can perform data analysis effectively only when you understand the patterns properly.
6. Data Validation
This is the stage of validation when the predictive model built in the previous stage is tested rigorously. This stage is basically to test the model and find out all the existing errors, false predictions, inappropriate outcomes, and many more, only to work on them and fix the issues at the earliest. The model goes through several testing stages to improve the preciseness of outcomes. The certain predictions that are produced from the model are then compared with the previous data and outcomes to judge the efficiency of the model.
5. Data Modelling
The stage of Data Modelling is considered to be the most crucial one in Data Science. Based on all the insights and trends derived from data analysis and exploration, a predictive model is created. A lot of tests and training happens with this model to make sure it predicts precise outcomes from the data being input.
7. Deployment and Optimization
The final stage of data science mainly involves the deployment of the model to the client and then seek feedback. Based on the feedback, the data scientists rework on the errors and improvements after which it is good to go. That’s the whole process of data science.
Trending Applications of Data Science and Business Analytics
The importance of data science is quite prominent in current times. Companies do want to lose any chance to utilize insights from data and make strategic business decisions based on it to build useful applications and get solutions. Hence, Data Science is being used in so many cases for different applications such as:
With so much data floating here and there, cyber security is one of the biggest issues that companies face, and data science comes handy here as well. It helps in mitigating cybersecurity risks. In domains like research and development, employees need to know how to tackle cyberattacks and reduce risks. Data science has a lot of potential in contributing to security products.
In the healthcare industry, data science and analytics have revolutionized a lot of methods like medical image analysis, cancer detection, drug discovery, prediction and prevention of diseases, monitoring of patient’s health and so much more. Data science is being heavily used in the healthcare industry to improve the lifestyle of patients and also give them complete care with less hustle.
Data science plays a vital role in the talent acquisition industry to make informed decisions. During the process of hiring, big data helps in avoiding any glitches and reducing the cost of training of employees. It is comparatively a lot simpler to maintain databases with data science while still, more than 50% of companies make use of old traditional methods and tools to keep their data and update them timely.
Financial sectors have a huge demand for Data Science in managing the indefinite financial transactions happening every other minute. The need of detecting fraudulent activities is real in this sector and hence the use of Data Science is important in sectors like banking, credit cards, loans, and many more. It is the ultimate solution to secure financial transactions and predict fraud.
Travel apps are a whole different world full of endless choices that are suitable to different audiences according to their budgets and requirements. From services like flight booking, hotels, travel packages to rental cars, and more, the complete online experience is now backed by machine learning and AI algorithms. These apps transform their customized offerings through insights from data about customer habits and needs and give them a more personalized experience.
Now comes the most technologically advanced sector which is that of self-driving cars. It is a classic example of Data Science where intelligent vehicles collect data in real-time through sensors, radars, cameras, etc. and then use that data along with machine learning algorithms to make driving decisions that are accurate for an amazing driving experience.
The current trending virtual voice assistants are all a result of the processes of Data Science. Gadgets like Google Home, Siri, Alexa and many more, which have brought so much ease to our lives are all a result of correct outcomes of data. In fact, it has even replaced customer executive roles these days with a chatbot which is extremely quick and responsive at the same time.
Hence, it has become quite important to learn and data and its techniques as it might replace many existing jobs in the coming times.
Job Roles for Data Science Learners
There’s a huge demand for data scientists and analytics professionals. All the job roles being offered in this domain are in high-demand all over the world. Hence, many people are looking out for various Data Science Programs Online.
1. MIS Reporting Executive
2. Data Scientist
3. Data Analyst
4. Data Engineer
6. Data Architect
7. Machine Learning Engineer
What is Business Analytics?
Business Analytics is a broader domain that combines Artificial Intelligence, Machine Learning, and Big Data Analytics.
Data Science and Business Analytics are fairly interrelated to each other. Both the technologies are at the peak and have a huge scope in the 21st century. The process of applying data and quantitative analytical techniques for deriving useful insights and making decisions based on them is known as Business Analytics.
Under the domain of Business Analytics, there are mainly three types of analytics that are applied to get the desired results.
Descriptive Analytics as the name says derives insights by analyzing the historical data. It is one of the most important analytical techniques to perform advanced and complex analysis of data.
Predictive Analytics is highly useful to derive reliable conclusions based on predictive models that are built to identify risks and connect data with effective actions.
Prescriptive analytics is the stage after predictive analytics. It is built on predictive capabilities. It mainly includes the application of logical and statistical techniques to derive the most preferred insights.
Best Data Science and Business Analytics Online Courses
With the increased scope of data analytics and its tools and techniques, professionals and students want to opt for online business analytics courses to get proper business analytics training but it is not mandatory that they provide them with some learning outcomes. It is a crucial decision to go and choose a course that fulfills all your learning requirements and thus keeping in mind the objective and delivery of the course is really important. There are a lot of things that learners should look at while finalizing a course in data science and business analytics such as mentored learning models, interaction with industry experts, hands-on projects, and real-life case studies, all these are necessary points to note while making a decision.
Working professionals looking to transform their career in Data Science and Business Analytics through the best data science courses should look at mentored learning models, where a learner can interact with industry experts in a small batch and discuss real-life case studies for easy understanding of topics.
Rated among the best Data Science online courses is this business analytics certification course, PGP-DSBA by UT Austin offers interactive mentored learning that provides collaborative as well as personalized learning.
Why Choose Great Learning for PGP-DSBA Online Course?
The comprehensive program is designed to build expertise in the most widely-used analytics tools and technologies. The curriculum will help you learn data science concepts with real-world case studies by the faculty of UT Austin and the experienced industry leaders. This enables learners to get hands-on training and experiences in leading tools like R, Python for data science, Tableau, Machine Learning, and many more.
The live mentored sessions taken by the Industry Professionals of Data Scientists and Business Analytics are a major part of the curriculum that helps learners get a complete understanding of concepts.
The PG Program in Data Science and Business Analytics provides access to learners to learn at their comfort. With Great Learning’s proprietary learning management system (LMS) Olympus, learners can access course content with one single login that works on all the platforms. For professionals with a busy schedule, they can watch recordings of missed lectures at their convenience. The mobile app provides notifications on quizzes and assignments to keep learners updated and motivated throughout the program journey. The mentored learning sessions are kept at a time convenient to the cohort.
Learn Data Science and Business Analytics through this data science certification course from the top faculty of The University of Austin at Texas. The course is designed by the leading faculty from UT Austin, ensuring widely-used analytics tools and technologies are a part of the curriculum. The mentors for the program are leading industry experts with a strong practical understanding of core concepts of data science.
With dedicated career support, the online data science and business analytics online certification course offers personalized 1:1 Career coaching and preparation for interviews. The career support team also provides assistance in building a resume to help you get the desired job. Learners can use their industry-ready portfolio, e-Portfolio to showcase their projects, skill, and tools.