Unlock the Secrets of Data Science

Why Choose Big Data?

The importance of Big Data in companies is immense. Hiding huge potential for company growth, its knowledge offers more excellent career prospects. Here it’s it’s essential: 

• Exponential Increase: Data is being generated at a rapid pace. While AI is being used to process it, there remain challenges in accuracy, thus requiring human minds to do the same. 
• Higher Adoption Rate: Big Data is widely and increasingly adopted across companies and industries, owing to its efficient role in decision-making. This opens numerous opportunities while also increasing the possibilities for jobs. 
• Demand for Professionals: Compared to the pace of data increase, the currently available ratio of professionals is small. This offers candidates a worthy opportunity and exhibits their high demand. Various job profiles are available, such as Big Data analyst, data analyst, data architect, etc. Further, the opportunities are available in a vast number of sectors. 
• Salary Growth: Career progress, marked by education and skill development, broader tool handling and increased experience, is available with effective salary growth. 
• Necessity for Analytics: Analytics is the method used to utilize data effectively. Companies need professionals to handle the data effectively, efficiently and appropriately as per the requirements. 
• Offers Quality Deliveries: Knowledge of Big Data is practical in numerous areas that require data handling. In-depth information helps increase productivity and fill the skill gap while providing diverse perspectives and abilities to work.

Ideal Big Data Syllabus

Here’s a detailed insight into the big data syllabus:

Course on Big Data Hadoop and Spark Development

This course equips you with expertise in the Hadoop framework, Big Data essentials, and tools within the Hadoop ecosystem, including HDFS, YARN, MapReduce, Hive, Impala, Pig, HBase, Spark, Flume, and Sqoop, among others.

Key Learning Outcomes

• Master navigating the Hadoop ecosystem and enhancing its functionality.
• Ingest data effectively using tools like Sqoop, Flume, and Kafka.
• Apply techniques such as partitioning and indexing in Hive.
• Utilize RDDs within Apache Spark.
• Handle real-time data streams.
• Execute SQL queries for DataFrame operations in Spark.
• Develop and implement UDFs and UDAFs in Spark.

Course Outline

• Lesson 01: Introduction to the Course
• Lesson 02: Basics of Big Data and Hadoop
• Lesson 03: Understanding Hadoop Architecture and Components
• Lesson 04: Techniques for Data Ingestion and ETL Processes
• Lesson 05: Exploring Distributed Processing with MapReduce and Pig
• Lesson 06: Detailed Study of Apache Hive
• Lesson 07: Exploring NoSQL Databases and HBase
• Lesson 08: Introduction to Functional Programming and Scala
• Lesson 09: In-depth Learning of Apache Spark
• Lesson 10: Core Processing with Spark RDDs
• Lesson 11: Data Handling with Spark SQL
• Lesson 12: Machine Learning with Spark MLLib
• Lesson 13: Stream Processing and Spark Streaming
• Lesson 14: Graph Processing with Spark GraphX

AWS Technical Essentials

Learn to navigate the AWS management console, grasp essential AWS security, storage, and database options, and gain proficiency in services like RDS and EBS. This course is designed to help you efficiently use AWS services.

Key Learning Outcomes

• UndeAWS’sd AWS’s core services and cloud computing fundamentals.
• Recognize AWS terminology, benefits, and suitable deployment options.
• Identify optimal deployment and networking solutions on AWS.

Course Outline

• Lesson 01: Introduction to Cloud Computing
• Lesson 02: Getting Started with AWS
• Lesson 03: Exploring Storage and Content Delivery
• Lesson 04: AWS Compute Services and Networking
• Lesson 05: Managed Services and Database Solutions in AWS
• Lesson 06: AWS Deployment and Management Strategies

Big Data Specialization on AWS

This course demystifies the use of AWS for big data, covering the AWS cloud platform, Kinesis Analytics, Big Data storage, processing, analysis, visualization, security services, and EMR, AWS Lambda, Glue, and machine learning algorithms.

Key Learning Outcomes

• Process data with Amazon EMR using the Hadoop ecosystem.
• Utilize Amazon Kinesis for real-time Big Data processing and analysis.
• Create visual representations and perform data queries with Amazon QuickSight.

Course Outline

• Lesson 01: Overview of Big Data on AWS
• Lesson 02: Introduction to Big Data Services on AWS
• Lesson 03: Big Data Collection with AWS
• Lesson 04: Storage Solutions for Big Data on AWS
• Lesson 05: Processing Big Data on AWS
• Lesson 06: Analyzing Big Data
• Lesson 07: Data Visualization Techniques
• Lesson 08: Security Practices for Big Data on AWS

Azure Fundamentals

Explore the foundational concepts of cloud computing as implemented in Microsoft Azure. Learn about Azure services, security, compliance, and deploying standard Azure services such as virtual machines and databases.

Key Learning Outcomes

• Comprehend Azure storage solutions and create Azure web applications.
• Deploy databases and manage cloud resources in Azure.
• Understand Azure AD and its integration with on-premises Active Directory.

Course Outline

• Lesson 01: Understanding Cloud Concepts
• Lesson 02: Core Services in Azure
• Lesson 03: Azure Security, Privacy, and Compliance
• Lesson 04: Understanding Azure Pricing and Support

Azure Data Engineer Pathway

This section will focus on data-related implementations in Azure, covering data storage services, data ingestion, transformation, security, and performance optimization.

Key Learning Outcomes

• Implement comprehensive data storage solutions.
• Develop efficient batch and streaming data processes.
• Optimize and monitor data solutions in Azure.

Course Outline

• Designing and Implementing Data Storage
• Developing and Designing Data Processing
• Ensuring Data Security
• Monitoring and Optimizing Data Systems

Data Engineering Capstone Project

Apply what you’ve gained in a real-world, industry-aligned data engineering project with guidance from expert mentors. This project is your gateway to demonstrating your data engineering proficiency to potential employers.

Elective Courses Overview

• Python for Data Science: Leverage Python in data science applications.
• PySpark: Integrate Apache Spark with Python for big data processing.
• Apache Kafka: Master real-time messaging with Kafka.
• MongoDB Developer and Administrator: Gain expertise in NoSQL databases.
• GCP Fundamentals: Explore infrastructure components on Google Cloud.
• Java Training: From basics to advanced Java programming concepts

How to Get Started With Big Data?

While gaining practical exposure to handling big data is crucial for the current job market, you must start from the basics. Here’s how you can begin to cover Big Data course syllabus:

Focus on Fundamentals 

The fundamentals and core concepts never lose their essence, regardless of which learning journey you begin. In the Big Data syllabus, the data types, characteristics, 3Vs, shell scripting, operating systems, programming languages like Python or Java, SQL, and tools like Hadoop, Apache Spark, Hive, and others form the basics. Familiarity and ability to work with these are to be learned. Additionally, it would be best to know about data analytics and associated complexities. 

Gain Familiarity With Analytics 

It is an essential application of Big Data. Working on analytics requires proficiency with a programming language and databases and knowledge of data warehousing, processing, visualization, and storytelling principles.  

Navigate to Experience 

Individuals wishing to progress in their careers should aim to face real-world problems. Since companies require this, the perfect course will compromise the practical element. At Simplilearn, you can work on real-life, hands-on projects. Further, with learning experience, we emphasize the necessity of developing relevant hard and soft skills required for the job.

Head on to Certifications 

Certifications are non-negotiable proof of your capabilities, skills and experience. They help you stand out from the crowd of simple learners. Numerous Big Data cloud certifications can be gained by simply taking their exam—for instance, GCP, AWS, or Azure.

Gain Work Experience  

After getting good credibility, you can head on to independently work in companies. You can begin by applying for internship and entry-level roles, followed by more experience-based positions.

Learn Continuously 

The field of data and, hence, the syllabus of Big Data is dynamic. With numerous advancements, the launch of new and updated tools and technologies, and changes in AI incorporation techniques, one must remain updated with trends to be a competitive candidate. Be aware of trends and keep on building projects or contributing to them for better familiarity and competitiveness.

Simplilearn’s Post Graduate Program in Data Engineering, aligned with AWS and Azure certifications, will help all master crucial Data Engineering skills. Explore now to know more about the program.

Conclusion 

With brand names like Spotify, Amazon, Netflix, Microsoft, and many more depending on Big Data for various operations, there lies much potential in learning its intricacies. Though numerous options are available to lure candidates with knowledge from industry experts, do you get the following benefits there? 

• JobAssist Program
• Alignment with trending concepts
• Industry-based hands-on projects

The Post Graduate Program in Data Engineering by Simplilearn deals with Big Data and advanced data skills. Available in association with Purdue University and IBM, the enrolled candidates can witness Master Class and Ask Me Anything sessions. Furthermore, 8x higher live interaction and particular emphasis on AI are sure to keep the candidates updated with industry demands. Click now to learn and interact with like-minded people.  

FAQs

1. How long does it take to complete a Big Data course?

Around six months is an appropriate time for learning fundamental aspects of Big Data. 

2. What are the prerequisites for enrolling in a Big Data course?

Basic knowledge of programming and familiarity with databases are minimal prerequisites in enrolling to cover the Big Data course syllabus. 

3. Are there any online resources included in a Big Data syllabus?

Yes, the Big Data syllabus includes multiple freely available online resources. These resources offer superficial knowledge enough to offer insights into fundamentals. 

4. What are the learning outcomes of a typical Big Data syllabus?

After completing the Big Data syllabus, you can handle data, analyze and interpret it, choose appropriate analytical models, use tools and techniques, apply technical concepts and much more. 

5. What are the most challenging parts of a Big Data syllabus?

The challenges of dealing with the Big Data course syllabus are data security and privacy, processivity, analysis, visualization and interpretation. To overcome these challenges, experience, technical knowledge, expertise, and problem-solving skills are required.