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Top 7 AWS Data Science Course With Placements

Amazon Web Services provides courses on many different topics that are relevant to the field of data science. These courses include different data science topics, like machine learning, data analytics, and business analytics. All of these topics are related to the use of the AWS Data Science Course for various aspects of the topic. Amazon Web Services is a cloud platform provided by Amazon. AWS platform offers more than 200 services to customers on the internet. Amazon Web Services is a subsidiary of Amazon company that provides many cloud computing services to governments, individuals, and companies. 

List of Top 7 AWS Data Science Course

Main Services of Amazon Web Services 

Amazon web services provide many different services to customers. Some of them are.

1. Computer Services- Amazon web services (Amazon EC2- elastic compute cloud) Amazon computing services in the cloud to customers, companies, and agencies. This service is available for a large scale of work. Virtual services on demand can be launched.

AWS Lamba is also a service provided by Amazon Web Services. It helps to run code without managing the server or any provision. The only payment required is for the time of computing.

2. Storage Services – Amazon’s simple storage service (Amazon S3) is also available for uploading and storing a scalable amount of data that can be retrieved anytime.

Amazon EBS (elastic block store) also offers block-level storage volumes which are used along with Amazon elastic compute cloud.

3. Database Services- Amazon RDS-  Amazon RDS is a relational database service that is compatible with many database engines like MySQL, PostgreSQL, Oracle, etc.

4. Networking Services- Amazon VPC- Amazon VPC is a virtual cloud for private users. It provides an isolated section of the cloud where clients can store resources in a virtual network.

5. Security and Identity- Amazon web services identity and access management is used to securely control access to AWS services and resources for users. Amazon web services Shield is used to protect the services that protect applications running on Amazon web services.

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Applications of Amazon Web Services- 

  1. Amazon web services can be used for the warehousing of a large amount of data. Amazon Redshift can find data from many different courses and then this data can be analyzed to find various business insights.
  2. Amazon web services can be used for big data processing as it can run many different big data technologies like Hadoop and Apache Spark which enables it to process large amounts of data efficiently and efficiently process analysis.
  3. Amazon web services allow customers to get insights from real-time analysis. Amazon Kinesis Analytics and Amazon Kinesis streams provide real-time data analysis to users by processing streaming data in real-time.
  4. Amazon web services offer business intelligence to customers by allowing stakeholders to make intelligent and well-informed decisions based on data analytics.

Benefits of Using Amazon Web Services-

  1. There is no need to invest in a physical infrastructure because it provides a scalable platform to scale different applications up or down according to demand.
  2. It is very cost-effective. The user is only supposed to pay for what they use on the Amazon web services platform.
  3. Amazon web services provide a secure and safe platform for cloud computing. Amazon web services comply with many regulations and standards.
  4. Amazon web services provide global reach to its customers. Amazon web services have a large network of data centers which allows high availability for applications of users.

What is Data Science?

Data science is a multidisciplinary field that uses different tools and techniques to get information and insights from a bunch of raw data. Data science techniques are used for analyzing trends and patterns in the bunch of raw data which consists of records, and statistical and numerical figures.

 Analyzing this raw data can help to understand its behavior so that it can be leveraged to gain maximum benefits by making the right decisions.

Data science is an emerging field that is currently being used by a wide range of companies and organizations. Every business these days generates a huge amount of raw data.

They generate it by social media platforms, surveys, websites, ad campaigns and many other different ways. If they just ignore this data, it would be a huge mistake on their part.

This data can be very useful for the growth and improvement of the company. They hire data scientists for this purpose. Data scientists use different data science tools and methodologies to identify different patterns and trends within the dataset.

After analyzing the information, data scientists suggest actionable strategies to the companies that can help them improve their performance and help them expand.

Scope of Data Science in Different Industries:

Data science has its applications in every discipline. Especially in the present scenario, it’s highly unlikely that any company is not using data science to expand their business.

1. Telecommunication- In the field of telecommunication, data science tools are very important for enhancing the performance and reliability of networks by the use of different data science techniques and methodologies.

The records can be used as raw data to identify the customers who are at risk of leaving the services. This can help telecom companies to avoid losses. Data science is used for revenue assurance too.

This is done by ensuring the accuracy of billing and preventing revenue leakage. Information of customers can be used to tailor personalized plans for them thereby increasing customer satisfaction.

2. Agriculture- in the field of agriculture, data science is being used for the optimization of agricultural practices by increasing the overall yield of crops. Soil health monitoring requires many applications of data science for the analysis of soil data to improve crop health.

The overall process of planting, watering, and harvesting can be optimized by the use of data science. Data science can help to predict the yield of crops. Even in supply chain optimization, data science is very useful.

3. Sports- Data science has revolutionized the standards of the sport by providing in-depth analysis of the performance of athletes and helping them by offering actionable strategies.

Data science can be used to assess the overall performance of a team and then make plans for performance improvement.

Past match records can be used for developing winning strategies. By providing personalized content to fans, engagement can be enhanced by the use of data science Techniques.

4. Entertainment- Data science applications are nowadays widely used in the entertainment industry. Data science applications are used to tailor content recommendation systems that are personalized for each customer.

Habits and behaviors of the audience can be understood at a deeper level which would help to make strategies. By applications of data analysis, movie makers can predict the future performance of a movie by analyzing historical data.

5. Finance- Data science has some of the most important applications in the finance sector. It’s mainly used for understanding the needs and behaviors of customers and tailoring personalized financial services.

Data science helps in understanding customer behavior and to implement retention strategies. Developing trading algorithms can be useful for analyzing the market and making investment decisions by analyzing past data.

By predictive analysis, companies can predict and mitigate the risks by taking appropriate actions.

6. Healthcare- In the healthcare industry, data science is used for tailoring personalized treatments for patients by analyzing their genetic profile and previous complications.

Recognition algorithms can be used to enhance the accuracy of diagnoses. Data science is a highly useful tool in the development of medicines by use of statistical analysis. Patient data can be used for the prediction of the outbreak of a disease.

7. Transportation and Logistics- In transportation and logistics, data science has many applications. It is used to improve the delivery routes by analyzing the previous travel records and therefore reducing the travel time and consumption of fuel.

Data-driven maintenance schedules can be used for the prevention of vehicle breakdowns. Data science can be used to increase the visibility of the supply chain by monitoring and providing real-time insights.

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Why Choose Data Science as a Career-

1. High Demand- With rapidly increasing demand, data science is one of the most popular career options in a contemporary environment. In this data-driven world, companies, institutions, and organizations generate a large amount of data every day.

They collect it from social media platforms, surveys, and sales records. They need a professional who can transform this raw data set into actionable insights. This skill is highly demanded and rewarded in businesses.

2. High Payment- Data science is one of the most highly-paying career options. Data science is a highly demanded skill, for which companies are ready to pay a good amount.

All companies want to attract the top talent in the field of data science and that is why they are ready to pay high salaries to hire new talents and retain the already hired. This makes this job a highly rewarded job.

3. Versatility- Data science skills and tools are applicable in a wide range of industries. Many disciplines like healthcare, retail, education, government, and more, use data science as one of the most essential tools of data science.

This versatility allows data science professionals to work in different disciplines according to their interests and preferences. They can work remotely or freelance according to their comfort and schedule.

4. Future-Proof Career- Data science offers a future-proof career to data science professionals. A career in data science provides job security as it is a career that is high in demand and this demand is going to grow at an even higher rate.

One can easily find a job in their locality which requires data science skills and expertise. Even if that’s not the case, a data science professional can easily find a freelance gig or remote job to work from their home on their schedule.

5. Interdisciplinary Nature- The interdisciplinary nature of data science makes it a fun and exciting career. It encompasses different elements of various disciplines like mathematics, statistics, computer science, etc.

Mixed use of technology, analytics, and industry-specific knowledge makes it an engaging and meaningful career option. The use of skills belonging to different disciplines makes it a very interesting career option as there is very little chance of monotony.

6. Flexible Career- Data science is a very flexible career option as it offers the opportunity to work from home. It offers a chance to work for multinational companies and clients through the use of freelancing platforms or remote job opportunities.

Professionals can work from anywhere they want with their schedule. A freelance career is a great option for individuals seeking autonomy in their careers.

7. Problem-Solving and Analytical Skills- Data science as a career offers a chance to learn the skills of solving complex problems by analyzing and making the best possible decisions.

This skill is highly valued in companies and organizations because they want employees to be efficient and intelligent. This skill helps individuals in their personal lives by helping them analyse different situations and solve problems by making the best possible decisions.

Amazon Web Services AWS Data Science Training Program and Certification Courses

1. Amazon Web Services Certified Data Analytics Speciality Certification Course-

AWS data science training program is designed for individuals who are interested in the field of data science and want to advance in their careers by performing advanced big data analyses. AWS data science training program validates designing, developing and maintaining analytics solutions on Amazon web services.

Topics- Data collection and storage-

Data Ingestion: It refers to the use of AWS services such as Amazon Kinesis, AWS Data Pipeline, AWS Glue, or even AWS IoT Core for data collection and processing.

Data Transformation: This includes cleansing and transforming data using some other AWS services like AWS Lambda, among others, plus AWS glue.

Streaming Data: It gives a clear understanding of real-time data acquisition and processing with the help of Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, and also Amazon Kinesis Data Firehose.

Data Lake Implementation: Building up data lakes using technologies like AWS S3 and AWS Lake Formation.

Data Warehousing: It consists of designing and managing databases through Amazon Redshift.

Database Solutions: The topic gives an insight into how different cloud-based database solutions are utilized in storing information such as Amazon Aurora, Amazon DynamoDB as well as Amazon RDS.

Data Processing and Analysis:

Batch Processing: This one provides insights into batch processing large datasets using tools such as Amazon EMR, AWS Batch, and even the likes of AWS Glue.

Stream Processing: The course focuses on real-time data processing through the use of API Gateway, Lambda functions and also Athena integrated with Kinesis Firehose Delivery Streams

Data Pipeline Orchestration: Through this section, learners will be able to understand better how to coordinate workflows for big data systems by relying on Step Functions as well as Glue.

Data Visualization:

Data Querying and Analysis: The AWS data science course provides insights into some data science tools such as Amazon Athena, Amazon Redshift Spectrum, and AWS Glue Databrew for performing data analysis.

Business Intelligence: Building different dashboards and reports through the usage of Amazon QuickSight.

Machine Learning: It is all about Amazon SageMaker.

  • Machine learning integration
  • Security and Compliance

Data Encryption: The encryption is done using AWS Key Management Service (KMS)

  • AWS CloudHSM to encrypt data.
  • Access Control
  • Compliance and Auditing

2. Amazon Web Services Certified Machine Learning Course-

AWS Data Science training program has been developed for individuals aiming at getting expertise in machine learning for deployment on the AWS cloud combined with those who build, design, and run ML workloads using the unique technology from AWS.

Topics- This AWS data science training program covers the following topics.

  • Machine learning concepts-
  • Model deployment
  • Model monitoring
  • Scalability
  • Security
  • Data preparation and feature engineering-
  • Data ingestion
  • Data transformation
  • Feature engineering
  • Model training and evaluation
  • Supervised learning
  • Unsupervised learning
  • Model deployment and management

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3. Introduction to Data Analysis (AWS Data Science course Digital Classroom Training Program)

The AWS data science program is a foundational course designed to explore the utilization of data analysis within the Amazon Web Services platform. This AWS data science course will cover a variety of topics, including data collection, data storage, data processing and transformation, and data visualization.

1. AWS Analytic Services

  • Define data analysis and its importance
  • Data Life Cycle
  • Types of data analysis

2. Data Collection

  •     Amazon Kinesis
  •     AWS IoT Core
  •     AWS Data Pipeline

3. Data Storage and Management

  •     Amazon S3
  •     Scalable storage with Amazon S3
  •     AWS Glue
  •     Amazon Redshift

4. Data Processing and Transformation

  •     AWS Lambda
  •     Amazon EMR
  •     AWS Glue

5. Data Analysis

  •     Amazon Athena
  •     Amazon Redshift Spectrum
  •     AWS Glue Databrew

6. Data Visualization

  •     Amazon QuickSight
  •     Visualization techniques

7. Security and Compliance

  •     Data Encryption
  •     Access Control
  •     Compliance

4. Data Analytics on Amazon Web Services (AWS Data Science Course Digital Classroom Training Program)

Through this comprehensive AWS data science course, students will gain foundational knowledge in Big Data Technologies and their applications within the Amazon Web Services platform. This includes EMR, Kinesis, Glue, and Redshift.

1. Introduction to Big Data and Analytics

  •     Define Big Data and common characteristics
  •     Big Data Use Cases
  •     Challenges of Big Data

2. Big Data Ingestion and Storage

  • Amazon S3
  • AWS Glue
  • Amazon Kinesis
  • Amazon Dynamo DB
  • AWS data pipeline

3. Big Data Processing 

  • Amazon EMR
  • AWS Lambda
  • Amazon Kinesis data analytics
  • AWS Glue

4. Big Data Analytics 

  • Amazon Athena
  • Amazon Redshift
  • Amazon Quicksight

5. Big Data Machine Learning 

  • Amazon SageMaker
  • Amazon EMR with Spark MLlib
  • AWS Glue and Amazon SageMaker integration

6. Security and Compliance 

  • Data encryption
  • Access control
  • Compliance

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5. Machine Learning on Amazon Web Services (Digital Classroom AWS Data Science Training Program) –

This AWS data science course offers comprehensive knowledge about different machine learning models using AWS Sagemaker and AWS ML and their applications on the Amazon web services platform.

Topics Covered-

1. Introduction to Machine Learning

  • Definition of machine learning
  • Machine learning use cases
  • Machine learning workflow

2. Machine Learning with AWS 

  • Amazon SageMaker
  • AWS Glue
  • Amazon S3
  • AWS Lambda
  • Amazon Redshift

3. Data Preparation and Feature Engineering 

  • Data cleaning and Transformation
  • Features selection and engineering
  • AWS Data Wrangler

4. Model Training and Evaluation

  • Model selection
  • Model training
  • Hyperparameter tuning
  • Distributed training

5. Deployment

  • Model deployment
  • Model monitoring
  • A/B testing
  • AWS CloudWatch

6. Business Analytics Course by Amazon Web Services-

AWS data science course is designed for business professionals and technical decision-makers. AWS data science course includes knowledge of machine learning and how it can be used to make effective and well-informed decisions.

Topics Covered-

  • Machine learning concepts
  • AWS machine learning services
  • case studies

7. Amazon Web Services Machine Learning Ramp-up Guide

All the different topics are included in the AWS data science training program by Amazon web services which is an online training programme.

The following are among the most important topics which should be understood to gain knowledge on various techniques of machine learning. This is useful In the deployment environment on Amazon web services.

  • AWS services for ML
  • data analytics and AI
  • SageMaker
  • Polly
  • Recognition

Here Are Some of the Following Features of the Aws Data Science Course:

AWS data science course covers topic areas which are relevant to data science and has flexibility in terms of scheduling. Big data, Predictive Analysis, Data Visualisation, Business Intelligence etc.

Advanced AWS data science course have workshops, case studies, and exercises. Dependence: It aids the participants in practically using concepts learnt in any working environment. Participants are taken through some of the features of the Amazon web services platform.

They get to learn a variety of services offered by AWS’s data science training program, such as Redshift, Amazon Athena, AWS Glue and so on. Amazon courses are provided by professional instructors with AWS certifications and industrious professionals.

Frequently Asked Questions-

1. That Brings Us to the Next Point of Order: What Measures Must One Take Before Enrolling in Aws Data Science Course?

Answer- For the introduction of a new topic, general knowledge is required about data science. This is because the participants would benefit from some prior knowledge of the Amazon Web Services platform.

However, it is not necessary to have any kind of prior knowledge regarding the facts and the relationships between them.

For the advanced and intermediate levels, many people may be expected to have a certain level of understanding. It would also be good to have experience in programming languages and ML models if you are going to work with them.

2. Regarding the Format of the AWS Data Science Course Offered, These Are the Available Formats.

Answer- Some of the available AWS data science course delivery modes comprise digital training and instructor-led training.

3. I Am Interested in the AWS Data Science Courses but I Would Like to Know Where I Am Most Likely to Take Them.

Answer- AWS data science courses can be obtained from the training certification website of AWS.

Conclusion

AWS provide numerous courses on the diversification of subjects and provides a healthy amount of information on them. AWS data science courses are related to data science and also to Machine learning. That makes it suitable for anyone who possesses the desire to get certified. This allows them to affirm their capacity and gain actual working experience through exercising.

Hello there, I am a graduate in Dairy Science and Food Technology. I have a passion for writing informative articles on topics such as health and nutrition, and I also possess a knack for storytelling. Currently, I am working as an intern at IIM Skills and looking forward to making meaningful contributions.

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