Professional Course in Data Engineering
- 120 Hours Blended - Online Interactive
- 80+ Hours of Assignments and practicals
- 1+ Capstone projects
- Lifetime Learning Management System access
513 Reviews
3117 Learners
Academic Partners & International Accreditations
360DigiTMG's Professional Course on Data Engineering introduces and explores the various tools needed for Data Engineers to solve modern-day issues. It expands learner's understanding of the numerous skills involved in knowing tools like Python, SQL, Big data tools, Spark, Kafka, Airflow, Databricks, Azure data factory, data lake, Redshift, BigQuery, Synapse, AWS Glue, etc. Participants get a chance to extract raw data from various data sources in multiple formats and transform them into actionable insights, and ingest data into a single, easy-to-query database. They learn to handle huge data sets and build data pipelines to optimize processes for big data analytics. Participants get a chance to dive deeper into advanced Data Engineering projects that will help in gaining practical experience.
Professional Data Engineering Course Overview
The professional course in Data Engineering lets you explore various tools that help you expand your understanding of the various skills involved and the tools needed to ace the job. The students will be trained to extract raw data from various data sources in multiple formats and then transform them into actionable insights, and deploy them into a single, easy-to-query database. Learn to handle huge data sets and build data pipelines to optimize processes for Big Data. Dive deeper into advanced Data Engineering projects which will help you gain practical experience and skills.
What is Data Engineering?
A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He/She has to pay attention to security and compliance; reliability and fidelity; scalability and efficiency; and flexibility and portability while designing, operationalizing, and monitoring data processing systems.
360DigiTMG Advantages
Learning Management System (LMS): - Students will be provided with LMS access, which included class recordings, self–paced videos, assignment course works, and reference materials. Data sets, algorithms, etc.
Training faculty with 10+ years of average experience and trained 20,000+ professionals and 10,000+ students from 8-12 countries. Corporate clients include many Fortune 500 companies.
Carries a legacy of training 20,000+ professionals and 10,000+ students from across the globe. Our program has been approved by 3 leading international universities /accreditation bodies.
The curriculum has been meticulously designed by industry experts by considering student communities as well as working professionals.
Career Mentorship & Placement assistance:- A coordinator will be assigned to you until you complete the program for smooth delivery of your training journey with 360DigiTMG
Professional Data Engineering Training Learning Outcomes
These modules will lay out a detailed exposure for Data Engineering tools and techniques. The core of Data Engineering involves an understanding of various techniques like data modeling, building Data Engineering pipelines, etc. Participants will get a keen understanding of how to handle data. As the course progresses, they get to learn how to design, build as well as maintain the data pipelines and work with big data of diverse complexity and production level infrastructures. Participants will also learn to extract and gather data from multiple sources, build data processing systems, optimize processes for big data, orchestrate the pipelines and much more. Also learn to
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Who Should Sign Up?
- Science, Maths, and Computer Graduates
- IT professionals who want to Specialize in Digital Tech
- SQL and related developers or software developers
- Students/IT professionals have an interest in Data and Databases
- Professionals working in the space of Data Analytics
- Academicians and Researchers working with data
- Cloud and BigData enthusiasts
Professional Data Engineering Course Modules
- Introduction to Python Programming
- Installation of Python & Associated Packages
- Graphical User Interface
- Installation of Anaconda Python
- Setting Up Python Environment
- Data Types
- Operators in Python
- Arithmetic operators
- Relational operators
- Logical operators
- Assignment operators
- Bitwise operators
- Membership operators
- Identity operators
- Check out the Top Python Programming Interview Questions and Answers here.
- Data structures
- Vectors
- Matrix
- Arrays
- Lists
- Tuple
- Sets
- String Representation
- Arithmetic Operators
- Boolean Values
- Dictionary
- Conditional Statements
- if statement
- if - else statement
- if - elif statement
- Nest if-else
- Multiple if
- Switch
- Loops
- While loop
- For loop
- Range()
- Iterator and generator Introduction
- For – else
- Break
- Functions
- Purpose of a function
- Defining a function
- Calling a function
- Function parameter passing
- Formal arguments
- Actual arguments
- Positional arguments
- Keyword arguments
- Variable arguments
- Variable keyword arguments
- Use-Case *args, **kwargs
- Function call stack
- Locals()
- Globals()
- Stackframe
- Modules
- Python Code Files
- Importing functions from another file
- __name__: Preventing unwanted code execution
- Importing from a folder
- Folders Vs Packages
- __init__.py
- Namespace
- __all__
- Import *
- Recursive imports
- File Handling
- Exception Handling
- Regular expressions
- Oops concepts
- Classes and Objects
- Inheritance and Polymorphism
- Multi-Threading
- MySQL Integration
- INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations
- Introduction to Big Data Analytics
- Data and its uses – a case study (Grocery store)
- Interactive marketing using data & IoT – A case study
- Course outline, road map, and takeaways from the course
- Stages of Analytics - Descriptive, Diagnostics, Predictive, Prescriptive
- CRISP ML(Q)
- Business Understanding
- Data Understanding
- Typecasting
- Handling Duplicates
- Outlier Analysis/Treatment
- Winsorization
- Trimming
- Local Outlier Factor
- Isolation Forests
- Zero or Near Zero Variance Features
- Missing Values
- Imputation (Mean, Median, Mode, Hot Deck)
- Time Series Imputation Techniques
- 1) Last Observation Carried Forward (LOCF)
- 2) Next Observation Carried Backward (NOCB)
- 3) Rolling Statistics
- 4) Interpolation
- Discretization / Binning / Grouping
- Encoding: Dummy Variable Creation
- Transformation
- Transformation - Box-Cox, Yeo-Johnson
- Scaling: Standardization / Normalization
- Imbalanced Handling
- SMOTE
- MSMOTE
- Undersampling
- Oversampling
- Data Collection - Surveys and Design of Experiments
- Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
- Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
- Balanced versus Imbalanced datasets
- Cross Sectional versus Time Series vs Panel / Longitudinal Data
- Time Series - Resampling
- Batch Processing vs Real Time Processing
- Structured versus Unstructured vs Semi-Structured Data
- Big vs Not-Big Data
- Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
- Sampling techniques for handling Balanced vs. Imbalanced Datasets
- What is the Sampling Funnel and its application and its components?
- Inferential Statistics
- Population
- Sampling frame
- Simple random sampling
- Measures of Central Tendency and Dispersion
- Mean/Average, Median, Mode
- Variance, Standard Deviation, Range
- What is a Database
- Types of Databases
- DBMS vs RDBMS
- DBMS Architecture
- Normalisation & Denormalization
- Install PostgreSQL
- Install MySQL
- Data Models
- DBMS Language
- ACID Properties in DBMS
- What is SQL
- SQL Data Types
- SQL commands
- SQL Operators
- SQL Keys
- SQL Joins
- GROUP BY, HAVING, ORDER BY
- Subqueries with select, insert, update, and delete statements
- Views in SQL
- SQL Set Operations and Types
- SQL functions
- SQL Triggers
- Introduction to NoSQL Concepts
- SQL vs NoSQL
- Database connection SQL to Python
- Data Ingestion from NoSQL databases with Python
- Data Science vs Data Engineering
- Data Engineering Infrastructure and Data Pipelines
- Concepts of Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
- Data Architectures
- Lambda
- Kappa
- Streaming Big Data Architectures Monitoring pipelines
- Working with Databases and various File formats (Data Lakes)
- SQL
- MySQL
- PostgreSQL
- NoSQL
- MongoDB
- Neo4j
- HBase
- Cloud Sources
- Microsoft Azure SQL Database
- Amazon Relational Database Service
- Google Cloud SQL
- Apache Hadoop
- Distributed Framework
- HDFS
- MapReduce
- YARN
- Hands-on with Data Proc (GCP)
- Apache Pig features
- Apache Hive features
- Apache Spark
- Spark Components
- Spark Executions – Session
- RDD
- Spark DataFrames
- Spark Datasets
- Spark SQL
- Spark MLlibs
- Spark Streaming
- Big Data and Apache Kafka
- Producers and Consumers
- Clusters Architectures
- Kafka Streams
- Kafka pipeline transformations
- Building pipelines in Apache Airflow
- Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Amazon web services (AWS)
- Features
- Services
- Microsoft Azure Services
- Features
- Services
- Google Cloud Platform (GCP)
- Features
- Services
- OLTP vs OLAP
- Databases vs Data Lakes vs Data Warehouses
- Data Lakehouse
- Data Fabric, Data Mesh, Data Mart, Delta Lake
- Choosing the right storage option
- Data Lake Cloud offerings
- Cloud Data Warehouse Services
- Intro to AWS Data Warehouses, Data Marts, Data Lakes, and ETL/ELT pipelines
- Configuring the AWS Command Line Interface tool
- Creating an S3 bucket
- Working with Databases and various File formats (Data Lakes)
- Amazon Database Migration Service (DMS) for ingesting data
- Amazon Kinesis and Amazon MSK for streaming data
- AWS Lambda for transforming data
- AWS Glue for orchestrating big data pipelines
- Consuming data - Amazon Redshift & Amazon Athena for SQL queries
- Amazon QuickSight for visualizing data
- Hands-on - AWS Lambda function when a new file arrives in an S3 bucket
- Azure Data Lake - Managing Data
- Securing and Monitoring Data
- Introduction to Azure Data Factory(ADF)
- Building Data Ingestion Pipelines Using Azure Data Factory
- Azure Data Factory Integration Runtime
- Configuring Azure SQL Database
- Introduction to Azure Synapse Analytics
- Data Transformations with Azure Synapse Dataflows
- Azure Synapse SQL Pool
- Monitoring And Maintaining Azure Data Engineering Pipelines
- Getting Started with Data Engineering with GCP
- Bigdata Solutions with GCP Components
- Data Warehouse - BigQuery
- Batch Data Loading using Cloud Composer
- Building A Data Lake using Dataproc
- Processing Streaming Data with Pub/Sub and Dataflow
- Visualizing Data with Data Studio
- Architecting Data Pipelines
- CI/CD On Google Cloud Platform for Data Engineers
- Storage Accounts
- Designing Data Storage Structures
- Data Partitioning
- Designing the Serving Layer
- Physical Data Storage Structures
- Logical Data Structures
- The Serving Layer
- Data Policies & Standards
- Securing Data Access
- Securing Data
- Data Lake Storage
- Data Flow Transformations
- Databricks
- Databrick Processing
- Stream Analytics
- Synapse Analytics
- Data Storage Monitoring
- Data Process Monitoring
- Data Solution Optimization
- Google Cloud Platform Fundamentals
- Google Cloud Platform Storage and Analytics
- Deeper through GCP Analytics and Scaling
- GCP Network Data Processing Models
- Google Cloud Dataproc
- Dataproc Architecture
- Continued Dataproc Operations
- Implementations with BigQuery for Big Data
- Fundamentals of Big Query
- APIs and Machine Learning
- Dataflow Autoscaling Pipelines
- Machine Learning with TensorFlow and Cloud ML
- GCP Engineering and Streaming Architecture
- Streaming Pipelines and Analytics
- GCP Big Data and Security
Tools Covered
How we prepare you
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Additional assignments of over 80+ hours
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Live Free Webinars
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Resume and LinkedIn Review Sessions
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Lifetime LMS Access
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24/7 support
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Job placements in Data Engineering fields
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Complimentary Courses
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Unlimited Mock Interview and Quiz Session
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Hands-on experience in a live project
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Offline Hiring Events
Call us Today!
+91 9989994319Professional Data Engineering Course Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0
- Over 18+ years of professional experience
- Trained over 2,500 professionals from eight countries
- Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Sharat Chandra Kumar
- Areas of expertise: Data sciences, Machine learning, Business intelligence and Data
- Trained over 1,500 professional across 12 countries
- Worked as a Data scientist for 18+ years across several industry domains
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
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Bhargavi Kandukuri
- Areas of expertise: Business analytics, Quality management, Data
visualisation with Tableau, COBOL, CICS, DB2 and JCL - Electronics and communications engineer with over 19+ years of industry experience
- Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
- Trained over 750+ professionals across the globe in three years
- Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Certificate
Win recognition for your expert skills with the Professional Data Engineering Certification. Stand out in this emerging yet competitive field with our certification.
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Alumni Speak
"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
Data Scientist
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
Venu Panjarla
AVP Technology
Our Alumni Work At
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FAQs for Professional Data Engineering Course Training
The Data Engineering course aims to provide aspirants with an in-depth understanding of all the essential tools and skills used by Data Engineers. The course provides hands-on learning on tools like Python, SQL, Spark, Kafka, and many more.
The training will be conducted in hybrid mode i.e., through the live instructor-led virtual sessions. The timings for both the sessions will be the same.
After the successful completion of 80% of your assignments, you are assigned to a live project where you will work with a group of students to bring the project to closure. After that, you will make a project presentation.
After the successful completion of the program, you will be awarded the Data Engineering certificate, powered by IBM.
This course is designed for students as well as working professionals. The basic requirement to undertake this course includes a degree in engineering, computer applications, or mathematics.
No, there are no extra charges for the certification. The cost is included in the package.
Not to worry, if you miss out on a session you can access the recorded session from the online Learning Management System (LMS).
We do not guarantee placements nevertheless, our placement cell supports you with resume building sessions, mock interviews, mentorship, and interview preparation. Our team also helps you launch your career by providing interview opportunities.
Jobs in Data Engineering In India
A Data Engineer is responsible for developing computer algorithms to identify trends in large data sets. The most common career paths for Data Engineer include Data Scientist, Data Architecture, Data Analyst, and Software Engineers.
Salary for Data Engineers
The demand for Big Data Engineers with strong analytic skills to handle data generated from various platforms with proficiency in SQL database design gets an average salary of Rs 8,17,911 LPA.
Projects in the field of Data Engineering
Data engineering is the most critical skill for a Data Scientist and the various projects students could take up include Analyzing sentiments, Detecting credit card fraud, Detection of color, and many more.
Role of Open Source Tools in Data Engineering
The various tools we will be exploring in this course are Apache Hadoop, Apache Spark, Apache Hive, Apache Kafka, NoSQL, and many more.
Modes Of Training For Data Engineering
The course in Data Engineering is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of online interactive live learning. We also support e-learning as part of our curriculum.
Industry Applications of Data Engineering
Data Engineers dominate many industries including Banking, Media, Education, Healthcare, manufacturing, etc.
Companies That Trust Us
360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.
Student Voices
Sharvin Rao
2 years agoVery good exposure. Satisfied with this program. Teaching materials are complete and does not require any programming background to learn this course
Priya Gopal
2 years agoVery experienced trainer and have patience to deal with every query raised in the classroom.
Lavaniya Rajesveran
2 years agoGreat place to learn about Data Science . Trainers are knowlegable and shared lots of new terms which was easily understandable.
Nivethitha Sivaraj
2 years agoThe course I selected was Data science using Python and R. Beginning it feels quite easy but days pass by we can feel the depth of the subject. Trainers doing their awesome job. I would seriously recommend for the people who are looking for a career uplift and even for freshers. Apart from trainers it’s our job to practice and practice to make it more perfect.
Gaurav Joshi
3 years agoData Science is aptly designed to cover most of the aspects of Data Science using R and Python. Anyone who is unsure of how to step in and explore the world of Data Science should join this course. Kudos to 360DigiTMG team to come up with such an elaborated program and helping students to put a confident foot forward in Data Science space.
Anita Nook Naidu
3 years agoVery good lecturers. Good training. Plenty of guidance provided. worth the money.
