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Key Features of the Data Science Syllabus for Business Analysts

Data Science Syllabus

The data syllabus has changed significantly in the post-COVID era where a greater focus is on new concepts related to low coding analytics, predictive intelligence, automated machine learning, data visualization, and edge/ fog computing. The demand for certified professionals has also grown exponentially in the last 2 to 3 years which is putting the onus on the best data science courses to come up with world class curricula and teaching patterns for people who can join business organizations and IT companies providing solutions and services in AI. Machine Learning, Cloud computing, and SaaS markets.

If you are looking for a data science course to meet your career goals, you have come to the right place. In this article, we will focus on the core areas of top data science courses and the specialized content specifically meant for jobs that hire from the best data science courses in India.

What is Data Science’s definition in 2022?

In 2022, data science is a billion dollar economy but the basics have remained firm and unchanged. For those in their formative years in the data science industry, this definition is considered to be the best in terms of its scope of application and specialization required.

According to a leading data science company, data science is defined as the extension of computer applications, data analysis, mathematics, statistics, and logical reasoning as applied to complex problems and generate meaningful insights using data in the form of signals (Boolean/ meta) text, images, voice/ speech, video, audio and so on. Companies leverage data science to not only stay competitive in the market of the product but also ensure that they are able to hire and retain the best talent who can use data science to come up with innovative solutions for latent and upcoming problems. This way, such companies are able to optimize their operations and core business functions, and when the time comes, like during a pandemic, distinguish themselves and their people from the rest of the world by diversifying into newer markets by introducing useful products, and solutions. The greatest example of this data science application is that shown by companies that make video conferencing and meeting planning platforms. 

Today, it is impossible to think of a company that hasn’t heard or used a video conferencing tool for their business objectives, more so, these companies have been able to go fully remote by diversifying their technology use into the data science field. This segment alone taps into billions of dollars in the software industry and employs close to a million engineers, analysts, marketers, and back of the office executives for pre-sales, post sales, and customer support teams.

What does Data Science Curriculum Focus on?

Best data science teaching courses give a lot of attention to instructional course designing and the e-learning platform assets and resources. For these course providers, the structure of content and students’ experiences is everything. The quality of data science resources outshines the competition which is a great differentiator if you are looking for a successful data science career. 

Here are the biggest cutting-edge differentiators that only the most advanced and best data courses offer:

Programming Languages

In the last 10-15 years, at least 50 different types of programming languages have been introduced to specifically manage big data and data analysis projects. While the popular ones are obviously the domains related to Python, R, Julia, Scala, Java, C / C++, JavaScript, and so on. But, did you know there are others in the league too that remain competitive for a data science company to excel with their projects. These are Microsoft Excel, PHP, Assembly Language, Visual Basic/ Classic Visual Basic, FORTRAN, SAS, PL/ SQL, and TypeScript. 

Best courses in data science encourage students to learn as many programming languages as possible, without losing focus of what’s important — the Python, R, and Java.

Also, Check – Why Are Financial Translations For The Businesses Imperative 2022?

Business intelligence

What’s the whole point of learning data science?

To apply the academic learning to real life practical problems associated with businesses. For example, how to predict customer churn for an e-commerce site in a month after a mega sale festival is planned? This complex business challenge can be answered by using all the website and marketing data collected from different first party sources and offline media communications. While it is humanly impossible to muster the courage and brain to break down every single detail in the data management to a single team, having an experienced data manager with certified experience with machine learning tools and data analysis platforms really helps. The whole idea of learning data science is to adapt to the fast changing business decision making domains that rely more and more on machine intelligence and less on human intelligence.

By pursuing a data science career, learners can connect with relevant data points that directly influence how decisions are made at the top level. This is a door to bigger opportunities in strategic positions such as marketing and sales, product design, finance, and IT specializations. 

Introduction to large scale data processing

Large Scale Data Processing is not a science – it’s an approach that has gained massive prominence recently due to the emergence of AI, Machine Learning, IoT, and Distributed systems ( DBMS). Why Large Scale Data Processing has become so important? Well, big data can help solve only a part of the problem. Because, there are two major challenges in big data analysis – firstly, there is labeled versus unlabeled data; or structured and unstructured data. Secondly, there is simplified data and dark data – the hidden data that no one can extract unless large troves of these are passed through highly advanced machine learning systems specifically built for unstructured data management. Both take time, human effort, and above all, cost of application, storage, and compliance. Why would companies risk it all on large scale data processing if the human executives are not trained to handle this aspect of the business? Some leading data science courses have understood this pain point and therefore, they have begun to provide special attention to every aspect of large scale data processing – hardware, no code analysis, auto ML, AI operations, ETL / ELT extensions, Event processing, and Stream processing, and so on.

In the best data science curriculum, trainers help in establishing the relationship between existing principles of processing data and applying AI ML concepts for a faster outcome that benefits businesses and project management teams in the immediate and long run.

How to be successful with data science?

In data science, the risk of failure is omnipresent. And, business leaders are aware of this all the time. In data science courses, the best thing you will learn is that here is learning from failures and recovering from these setbacks than anywhere else – because data is the king, and when you work with the costliest asset in the world, you are bound to be extra attentive and extra careful with your repertoire of programming expertise, understanding of data behavior, and find the “one in a million” way to handle a massive volume of data to generate the best business results and reduce the workload deficiencies with each iteration. When you master these two aspects, you are set out for a lucrative well-paying job in 2022.

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