What is the course syllabus for data analytics?

Students can opt for a data analytics course after completing class 12 at diploma, undergraduate and postgraduate levels. Today both offline and online versions of data analytics certification courses are available in the market. The terms and conditions set out by each institution or college for Data analytics course admission are different and are based on the type of study and the mode of study. Candidates interested in this course program must meet certain requirements that the college or university sets. Some of the typical requirements that are required to take admission in a data analytics course are as follows.

• Candidates who want to opt for data analytics courses at the undergraduate level need to complete class 12 in commerce or science stream with mathematics
• To be admitted to data analytics courses at the postgraduate level, students must have a necessary bachelor’s degree or any equivalent degree in the field of computer science, mathematics, economics, finance, etc.

What are the subject covered by the data analytics course?

Data analytics graduates are highly in demand in almost every industry. This is why data analytics is a popular choice among students who want to make a massive shift in their careers and earn lucrative salaries. This field entails the application of big data analytics, statistics, machine learning, as well as other related data concepts. A data analytics course program is a good career option since, after completing the program, you can adopt strong numerical and analytical abilities and grasp computer applications like Python, SQL, Matlab, SAS, etc. Companies and organizations seek data analytics and data science professionals. A data analyst earns an average salary of INR 8-20 LPA. Completing a bachelor’s graduate degree in this field will enable you to earn Rs4,00,000 per annum salary. The core subjects that are covered in the data analytics course are data mining, data analytics foundation, important programming languages like R and Python, machine learning, natural language processing, big data, data warehousing, artificial intelligence, the Internet of Things, etc.

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Data analytics detailed syllabus 

All the disciplines taught in data analytics programs may differ from one college to another. The popular subjects that are covered in the course of data analytics as well as its detailed syllabus, are 

• Data analytics Principles

The principle of data analytics is a subject that helps to train data analytics professionals about the basics of the field and its application areas so that they can comprehend new data analytics technologies and underlying key principles used in the field. This subject includes

• Using data analysis techniques in different applications
• Fundamentals of data analytics
• Ethical implication of data sharing, privacy, algorithmic decision making
• Recognizing machine learning algorithms

• Programming concepts with Python And R

Data analytics courses consist of essential programming concepts like R and Python that data scientists and data analysts require to carry out data analysis work. These programming languages help in displaying and analyzing data correctly.

• Mathematical foundation

Data analytics and data science theory rely mostly on a mathematical foundation like linear algebra. The prime purpose of a data analytics course is to expose students to fundamental mathematical concepts like linear algebra and how these mathematical concepts are used in different areas of data analytics and data science.

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• Database Technologies 

The Analytics course covers database technologies to help students gain a foundational understanding of database principles and hone their practical experience working with these technologies. This subject includes database principles as well as terminology that are used to build database tables as well as write queries easily and successfully.

• Data Mining 

The data analytics syllabus covers data mining which comprises preprocessing as well as analyzing data, choosing appropriate machine learning algorithms and models for a particular application, and fostering interest among individuals in making advancements in data mining.

• Artificial Intelligence 

The data analytics syllabus covers artificial intelligence concepts and techniques so that students can understand the areas where artificial intelligence is needed and what are the different AI methods. This subject covers important algorithms so students can gain experience evaluating and analyzing complex data systems.

• Regression Modeling

In this subject, data analytics aspirants learn about evaluating the relationship between different variables in a data set and how to use data analytics software to fit multiple and basic linear regression models.

• Big Data Analytics 

The data analytics course aims to teach students about the importance of big data in diverse applications and how big data impacts different industries in real time. This subject explains Hadoop architecture and Hadoop distributed file systems. This subject explains how big data is used in emerging technologies today.

• Machine Learning 

The data analytics course covers machine learning approaches and methods. This subject helps to understand training models and other different modeling processes that data analysts require in practical work. Students get to validate the model, give predictions about new observations and gain an understanding of supervised learning algorithms.

• Natural Language Processing 

Natural language processing is an important syllabus of data analytics courses that involves syntax processing, the application of natural language processing in different areas, and pragmatic and discourse processing.

1. Data Visualization 

Data visualization is a core subject of data analytics that helps understand the graphical representation of data and information. In this subject, data analytics aspirants will learn different data visualization tools that help examine and comprehend patterns, trends, and outliers in data. 

• Deep learning and neural networks

The data analytics course program covers neutral networks and deep learning in order to familiarize students with deep learning and neural networks as fundamental data analytics concepts. This subject includes training, designing, and connecting deep neural networks and teaching them how to implement vectorized or efficient neural networks successfully. 

• Project work 

The data analytics syllabus covers project work to teach candidates about the repercussions, problems, and capabilities of deep learning and to equip them with cutting-edge data analytics technology. By participating in the project work, students acquire the ability to apply their knowledge base in the data analytics field and advance their careers. 

Conclusion 

The data analytics course syllabus is designed to provide comprehensive instruction about data extraction, data manipulation, and data analysis. The exact course syllabus may vary from one program to another. However, some important subjects almost covered by every data analyst program are statistics, probability, data structures and algorithms, business fundamentals, text analytics, data collection, data visualization, etcetera. The following guide will cover the course syllabus and subjects covered in the data analytics program. After pursuing data analytics as a career option, you will be able to transition yourself as a big data analyst, data engineer, business intelligence professional, data scientist, etcetera.

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