All You Need To Know About Big Data Analytics Course? Exploring The Significance

The global Big Data Analytics market share is approximately $ 307.52 billion in 2023, expected to grow to $745.15 billion by 2030 at a CAGR of 13.5%. The Covid-19 pandemic blow made industries shift to the digital platform, in a bid to capture better market opportunities. With this major shift, the demand for Big Data Analytic experts is also growing by leaps and bounds. And so has the number of institutes offering Big Data Analytics Course. In this article, we attempt to assist you with knowledge of what are the components of a good course. So Read On!!

Significance of Big Data Analytics

The Big Data term was introduced in the market in the 1990s, yet the full utilization of the technique was not done till after 2000. The biggest impact on the Big Data Analytics industry occurred during the Covid-19 pandemic turmoil. The pandemic caused a major jump in Internet of Things(IoT) devices. As per International Data Corporation (IDC) by 2025, there will be approximately 152,200 IoT devices getting connected to the internet per minute. KPMG conducted a survey in 10 countries across 12 industries and found that during the COVID-19 pandemic, 67% of the companies studied had shifted to digital transformation and 63% of the companies had increased their budget for shifting to digital processes. MicroStrategy a leading business intelligence company claimed that 94% of its client data professionals claim that Big Data Analytics is important for their organization to advance in the digital transformation program.

Sectors like Banking Financial Services and Insurance(BFSI), healthcare, retail, agriculture, telecom, media, and social media generate massive amounts of data as they are the leading sectors in adopting smart digital applications. These digital applications termed Business Intelligence Solutions, workforce analytics, and customer relationship management have supported businesses with great development. These solutions have rendered real-time insights, visualization, and forecasting, which all have resulted in advanced and informed decision-making capabilities.

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What is Big Data Analytics

The relevance of describing any Big Data Analytics Course can be ascertained once we understand the definition of “Big Data Analytics”. Big Data Analytics is a multitude of complex processes involved in analyzing Big Data to detect information about hidden patterns, and correlations between different variables, market trends, customer behavior, and customer preferences. The data to be analyzed is so large that advanced computing techniques and tools are required to cope with the speed of the input originating from different sources like structured, unstructured, and semi-structured data. The source of such data may be varied like website traffic, comments on social media, surveys, data from Internet of Things(IoT) devices, sales volumes, and services. The results are used as tools for making informed decisions.

Characteristics of Big Data Analytics

This section is the opening topic for any Big Data Analytics Course. Any vast amount of data cannot be scrutinized with Big Data Analytics, as it has unique characteristics to identify it. These characteristics are as follows:

• Volume: In Big Data Analytics very high volumes of data, that can be structured or unstructured are processed. The data curries an indefinite value.

• Velocity: The data that is received is very fast ranging in zettabytes even per minute. Big Data Analytics is characterized by the velocity of the data being processed, which is required for giving results in real-time required for analysis of the performance.

• Variety: Since Big Data is received in various forms, Big Data Analytics can incorporate processes to include a diverse range of structured and unstructured data. The data in the form of text messages, video, and audio require different methods of processing.

• Value: The value of the data being processed matches the core objective or goal of the company. This is the core characteristic of Big Data Analytics.

• Veracity: Veracity relates to the authenticity of the data being analyzed through Big Data Analytics. The error factor of the data has to be low to arrive at correct results. The precision of data collection is important to avoid a high error factor, which adversely affects the final analysis.

• Validity: The validity here refers to the legality of the data. General Data Protection Regulation(GDPR) has set some standard rules and regulations applicable to data protection and privacy, which are applicable during data collection and processing to keep these methods transparent and legal.

• Volatility: As the rate of receiving the data is at a phenomenal speed, so is the rate of change in the value of data. The shelf life of data is very short as the variables keep changing fast too. Analysis of old data will impact the decisions being not in line with the current trends.

• Variability: As the sources for data are different and not consistent there can be inconsistencies in the data collected owning to different speeds at which data is received, different sources, and different types of data.

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Big Data Analytics Course Syllabus- A General Overview

As evident from the introduction of this article, Big Data Analytics is currently high in demand, as companies are shifting more toward digital technology. This growth also signifies the rise in demand for experts in this area. The best way to step into this field, gain up-to-date experts, and aim to get an edge over the other aspirants, a good Big Data Analytics Course is recommended for all the aspiring candidates and professionals already working in this industry. In this section, we focus on giving you a general overview of the topics that are covered in any Big Data Analytics Course.  These are as follows:

Introduction to Big Data Analytics: The section covers the definition of Big Data and Big Data Analytics. It introduces students to the 5 important characteristics of Big Data Analytics that are Volume, Velocity, Veracity, Variety, and Value.

Data Management and Storage: The following tools which are required for data storage are taught at the beginning of Big Data Analytics Course: 

• Hadoop- HDFS, Apache, Cloudera, Horton Networks, and Daemon
• YARN
• HIVE
• MAPREDUCE
• SQL
• MongoDB
• Cassandra
• HBase

Data Preprocessing: This topic covers the processes involved in improving the quality of data to make it more viable for further processing. The following sub-points are covered in this section:

• Understanding the processes of data collection. Differentiation between primary and secondary data collection processes, for example, surveys, questionnaires, focus groups, research groups, etc.
• Processes involved in data cleaning, modifications, and normalization.
• Handling missing data and outliers.
• Data Integration and data quality assessment.

Data Mining: Data Mining is the process of analyzing large database sets to identify parameters set in alignment with the objective of the analytics. Parameters are identified and a correlation is identified between these parameters and other variables. The different types of Data Mining are

Predictive Data Mining: Data is analyzed to help forecast events in a business. Predictive Data Mining methods are divided into the following types:

Classification Analysis: In this process, while retrieving information from the data, it is classified under different categories to ease the process of analysis.

Regression Analysis: This technique is applied to analyze and forecast the changes occurring in the dependent variable when it is kept constant and the independent variable is changed.

Prediction Analysis:  The relationship between dependent and independent variables is analyzed and the results are forecasted about the different correlations. The relation-independent variables with each other are also analyzed under this technique.

Descriptive Data Mining: In this category the historical data is analyzed to explain the reasons for the events which occurred in the past. The Descriptive Data Mining technique is further sub-classified under the following heads: 

Clustering Analysis: In this method, the data are grouped in clusters as the analysis progresses. The norms for the cluster group are not predefined.

• Summarization Analysis: The data is stored in a compressed and compact manner for ease of analysis.

Association Rule Analysis: Here data is mined to find out hidden variables and the patterns which they formed. These may have not been discovered earlier but were affecting the final results. 

Sequence Discovery Analysis: In this data is received in a sequence to a main event and it is analyzed to figure out variables and the patterns and trends they are responsible for. 

Tools Required for Data Mining: Here is a list of the tools required for Data Mining. Most of the tools are taught in any Big Data Analytics Course, These are:

• RapidMiner
• Orange
• Rattle
• Python
• DataMelt
• Alteryx
• MonkeyLearn
• KNIME
• SAS
• Oracle Data Mining
• Apache Spark
• Olik
• Anaconda
• Weka
• Apache Mahout SPSS Modeler
• ELKI 
• Kaggle
• Sisense

Machine Learning: Machine Learning is an important part of a good Big Data Analytics Course. The following topics are emphasized on in this section: 

Introduction to Machine Learning: This section gives a general introduction to the definition of Machine Learning, which is the collection and analysis of complex and large datasets with the help of tools connected to cloud computing. The following types of Machine Learning are basic:

Supervised Learning: The data is cleaned and labeled and the algorithms for the final analysis are already mapped and input by the analysts, then it is known as supervised learning.

Unsupervised Learning: The data set received is analyzed by the machine to analyze patterns and trends without any human intervention. The machine configures the algorithms while processing the data to arrive at results that align with the company’s objective.

Semi-Supervised Learning: As the title suggests this type of machine learning has a combination of human and machine intervention. In this type, the analysis is carried out on both structured and unstructured data. Few algorithms are preferred in the machine and as the analysis is taken forward the machine configures its own algorithms to arrive at predictions.

Reinforcement Learning: In this type, the algorithms for analysis have to run through several courses of trial and error and changes. Depending on the feedback the performance is improved.

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Tools Required for Machine Learning: The following programs are completely or partially taught in any Big Data Analytics Course, depending on the institution:

•  TensorFlow

• Apache Mahout
• Weka
• Apache Spark
• Microsoft Cognitive Toolkit
• Theano
• Numpy
• Matplotib
• Scikit – Learn
• Keras
• Shogun
• KNIME
• Google Cloud Platform
• H2O
• Pytorch
• Pandas
• Open NN
• Caffe
• RapidMiner
• Accord.net
• DataRobot.In
• Amazon SageMaker
• Torch

Data Visualization: Data Visualization is an effective platform in a Big Data Analytics Course for students to work on their technical, creative thinking, presentation, leadership, and communication skills. Post the analysis of the data an analyst has to present the meaning of the results with the help of data visualization tools to make it comprehensible and cognitive. This objective of Data Visualization focuses on presenting complex technical results with the help of tools in an easy catchy and graphical way so that even a non-technical person can grasp the main objective. There are certain methods of Data Visualization that are as follows:

Comparison Visualization: When there is a comparison between values to be represented bar charts, column charts, population Pyramids, and Radial charts are used.

Pattern Visualization: The representation for showing patterns and trends in the data line graphs, Aron charts, scatter plots, dendrogram, histogram, violin plot, and stream graphs are used.

Relationship Visualization: To show the link between variables heat maps, radar charts, tree diagram, network diagram, and SWOT analysis is employed.

Proportions Visualization: To demonstrate the proportion of differences and/or similarities between variables bubble charts, pie charts, donut charts, progress bars, proportional area charts, treemaps, wordcloud, and waterfall charts are generally used.

Range Visualization: Representation to depict the upper and lower range of the effect of variations and data error bars, bullet graphs, and Gantt charts are some of the popularly known methods.

Geography Visualization: In this method, the analysis is related to data obtained from geographical activities. In this, the most commonly used methods are Choropleth Maps and Cartograms.

Data Visualization Tools: There are many Data Visualization tools available which are listed below, but the inclusion of the tools in Big Data Analytics Courses depends upon the most prevalent and widely used tools in the industry. Here is a list of Data Visualization tools:

• Tableau
• JupyteR
• Power BI
• Dundas BI
• Zoho reports
• Visual.ly
• Google Charts
• RAW
• Sisense
• IBM Watson
• Plotly
• Data Wrapper
• Fusion Charts
• High Chartys
• Infogram
• QlikView
• ChartBlocks
• D3.js
• Grafana
• Polymaps
• Sigmaj.s

Big Data Analytics for Business: Big Data Analytics has specific use when applied in different domains of business. Business Analysts are trained in the following types of Big Data Analytics Courses: 

Descriptive Analytics: In this field with the help of a combination of summary statistics, segmentation, and clustering the reasons for past events are analyzed

Prescriptive Analytics: Analysts have to have knowledge of the application of a combination of machine learning, mathematics, statistics, business rules, and big data to suggest solutions for predicted risks or ways to work on future opportunities to enhance profitability.

Predictive Analytics: In this a combination of machine learning, statistical modeling, and data mining is applied to predict future events based on the data obtained from the past events.

Diagnostic Analytics: Analysts apply a combination of data mining, data discovery, and correlation methods to suggest solutions for events that have occurred in the past.

Ethics & Security: As collection and analysis of data is done with human intervention there are bound to be biases and corruption in the methods in all the processes. To protect the rights of the sources and prevention of illegal methods every Big data Analytics Course has this section as a must, which covers the following topics:

 Information Consent: Ways to obtain consent taken from the source without any use of force, or bribes.

Privacy: Description of conditions of to rights of privacy and actions taken during the invasion of privacy and loss.

Ownership: Description of rights of ownership to distribution, modification, and earning rights of data.

Code Bias: Rules and Regulations for setting algorithms and codes without any bias towards caste, creed, or gender. 

GDPR Data Processing Agreement: Rules and Regulations related to legal basis processing, consent, and exit rights.

Skills Acquired in Big Data Analytics Course

A student when enrolled in a course does not only gain expertise in the technical domain but there are other aspects on which they are trained and these are industry-relevant. The additional acquired skills are:

• Problem Solving: The courses have projects and assignments incorporated at every level which are taken as examples from actual business scenarios.These help in enhancing the problem-solving and critical abilities of students. 

• Domain Knowledge: A student can choose either a course specializing in the industry domain of his/her choice or electives in their area of interest. This helps them gain knowledge about the industry in the course before they start searching for job opportunities.

• Communication Skills: Presentations, group activities, and Data Visualization groom the students for good communication skills and better insights toward the effective delivery of thoughts and ideas.

• Project Management: Class Projects and Capstone Projects help students the nitty gritty of effective project management skills.

• Team Work: Group activities, and networking with alumni and industry peers help in inculcating team spirit in students.

Career Prospects

On completing a course in Big Data Analytics a candidate can be employed in various positions::

• Data Analysts
• Big data Architect
• Data Manager
• Business Analyst
• Operations Analyst
• Metrics and Analytics Specialist
• Big Data Analytics Consultant
• Solution Architect
• Analytics Associate
• and Business Consultant

 The industries that have a high demand for Big data Analysts are:

• Banking Financial Services and Insurance
• Healthcare
• Retail
• Oil and Gas refineries
• Telecommunication
• Manufacturing units
• Media and Entertainment industry
• Government Agencies
• and many more

As per a general survey, the starting salary package of a Data Analyst is approx. Rs. 4 lacs/annum.

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Conclusion

In conclusion, Big data Analytics is very high in demand as the rate of Internet of Things (IoT) devices is increasing at a phenomenal speed generating humongous amounts of data. Most industries now are shifting to digital management to stay relevant in the competitive business landscape. There is a growing demand for experts who can lead companies toward cost and operational efficiencies and lead them toward a greater number of innovations. 

Enrolling in a good comprehensive course opens doors to countless opportunities. Students learn how to convert raw data to actionable insights with ease under proper guidance. They learn expertise in the technical domain which is relevant to the current business practices. In addition, they are groomed for effective communication skills, soft skills, problem-solving, critical thinking, project management, and teamwork. These students can very well help the companies they work with towards better innovations and optimized business profits.

As the demand for tech-savvy experts is high, investing in the right course is a lifelong wise decision. 

FAQs

Q. What is the definition of Big Data Analytics and its importance in todays world?

Big Data Analytics is the process of examining and unraveling insights from complex datasets which is not possible with traditional methods of computing. The analysis is carried out using advanced computing techniques, statistical algorithms, and artificial intelligence. Big Data Analytics has major importance in the business world. It helps in making informed decision making, better understanding of customer behavior, enhanced operational efficiency, better risk management, ideas for innovations, and planning, which all leads to better competitive advantage for the company to strive and stay pertinent.

Q. What are the eligibility criteria for enrolling in any Data Analytics Course?

Most institutions and colleges require the student to have at least a bachelor’s degree with 50% passing marks in the stream of either mathematics, statistics, computers, or finance. A few colleges require work experience in the same domain.

Q. What are the career prospects after completing a course in Big Data Analytics?

Big Data Analytics has great importance in healthcare, Banking Financial Services and Insurance, retail, telecommunication, oil and gas refineries, manufacturing, entertainment and media, and many other domains. After completing a course in Big Data Analytics from a good institution one can be placed in the position of a Data Scientist, Business Analyst, Data Engineer, Quantitative Data Analyst, Financial Analyst, and Marketing Analyst.