CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN GANDHINAGAR

Live Virtual

Instructor Led Live Online

110,000
63,945

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
36,645

  • IABAC® & JAINx® Certification
  • One year access to Self Learning
  • 10 Capstone Projects
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Classroom

In - Person Classroom Training

110,000
69,195

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Classroom Training
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING DATA ENGINEER ONLINE CLASSES IN GANDHINAGAR

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN GANDHINAGAR

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools and platforms
• Core concepts of Data engineering

MODULE 2: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 3: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data wrangling with Pandas
• Managing large data sets with Pandas
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 4: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data Ingestion through Pandas methods
• Hands-on: Ingestion, Transform Data and Load data

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools, and platforms
• Core concepts of Data engineering

MODULE 2: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 3: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 4: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 5: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping, and managing images)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 6: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 7: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : AWS DATA SERVICES INTRODUCTION 

  • AWS Overview and Account Setup
  • AWS IAM Users, Roles and Policies
  • AWS Lamdba overview
  • AWS Glue overview
  • AWS Kinesis overview
  • AWS Dynamodb overview
  • AWS Anthena overview
  • AWS Redshift overview

MODULE 2 : DATA INGESTION USING AWS LAMDBA 

  • Setup AWS Lamdba  local development env
  • Deploy project to Lamdba console
  • Data pipeline setup with Lamdba
  • Validating data files incrementally
  • Deploying Lamdba function

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

  • AWS Kinesis overview and setup
  • Data Streams with AWS Kinesis
  • Data Ingesting from AWS S3 using AWS Kinesis

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

  • AWS Redshift Overview
  • Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
  • Develop Applications using AWS Redshift cluster
  • AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

  • Azure Synapse setup
  • Understanding Data control flow with ADF
  • Data Pipelines with Azure Synapse
  • Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

  • Create Azure storage account
  • Connect App to Azure Storage
  • Azure Blog Storage

MODULE 7: AZURE DATA FACTORY

  • Azure Data Factory Introduction
  • Data transformation with Data Factory
  • Data Wrangling with Data Factory

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

  • Hands-on Project Case-study
  • Setup Project Development Env
  • Organization of Data Sources
  • AZURE/AWS services for Data Ingestion
  • Data Extraction Transformation  

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 2: DOCKER FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping and managing images)
• Publishing containers to Docker Hub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 3: KUBERNETES CONTAINER ORCHESTRATION

• Kubernetes Introduction
• Setting up Kubernetes Clusters
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 4: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 5: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN GANDHINAGAR

The data engineering industry is experiencing significant momentum. A study by Research Dive projects that the global data engineering market will surpass $149.57 billion by 2027, growing at a CAGR of 26.8% from 2020 to 2027. The report emphasizes the escalating need for efficient data processing and storage solutions, along with the implementation of advanced data management practices, propelling the demand for data engineering professionals.

Embark on an enriching journey with DataMites' Data Engineer course in Gandhinagar. This comprehensive program spans over 6 months, encompassing more than 150 learning hours. Engage in 50+ hours of live online training, where you can interact with expert instructors in real-time. Immerse yourself in 10 capstone projects and 1 client project, gaining hands-on experience in solving real-world data engineering challenges. Additionally, enjoy the flexibility of a 365-day Flexi Pass, granting access to course materials and the Cloud Lab for continuous learning and practice.

DataMites also offers Data Engineer courses on demand in Gandhinagar, catering to those who prefer offline learning. Benefit from the freedom to learn at your own pace with pre-recorded lectures and comprehensive study materials.

DataMites stands out as the ideal choice for Data Engineer training in Gandhinagar for several compelling reasons. 

  • Led by Ashok Veda, a renowned expert in the field, and supported by a dedicated faculty team, DataMites ensures a top-notch learning experience. 

  • The course curriculum is thoughtfully designed to cover all essential topics and technologies necessary for success in data engineering. 

  • Upon completion, you will receive globally recognized certifications from esteemed organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX.

  • Flexibility is a cornerstone of DataMites' training approach, allowing you to balance your learning journey with other commitments through our data engineer training online in Gandhingar aswell as data engineer training offline in Gandhinagar. 

  • Engage in projects that involve working with real-world data, gaining practical experience that sets you apart. Unlock data engineer course with internship opportunities to further enhance your skills and industry exposure. 

  • DataMites provides comprehensive data engineer training with placement assistance and job references, giving you an edge in kickstarting your data engineering career. 

  • Enjoy the convenience of hardcopy learning materials and books, ensuring you have valuable resources at your fingertips. 

  • Join the DataMites Exclusive Learning Community, where you can collaborate and network with like-minded peers and industry professionals. 

  • With affordable pricing and scholarship options, DataMites makes quality education accessible to all.

Gandhinagar, the capital city of Gujarat, India, offers a vibrant and conducive environment for learning. The city's thriving technology sector and robust business landscape create numerous career opportunities in data engineering, both locally and globally. Gandhinagar's close proximity to Ahmedabad, a major economic and industrial hub, further expands the prospects for data engineers. Embrace the tranquility of Gandhinagar, with its wide roads, green spaces, and serene environment, providing a conducive setting for focused learning. Experience the rich culture, warm hospitality, and diverse cuisine that add to the city's charm.

Along with the data engineer courses, DataMites also provides data analytics, r programming, data analyst, data science, deep learning, IoT, artificial intelligence, AI expert, python training, data mining, tableau, mlops and machine learning courses in Ghandhinagar.

ABOUT DATA ENGINEER COURSE IN GANDHINAGAR

Data engineering refers to the discipline that involves the design, development, and management of systems and processes to acquire, store, organize, process, and deliver data. It focuses on building and maintaining the infrastructure and architecture required for efficient and reliable data processing, ensuring data quality, integration, and accessibility. Data engineering plays a crucial role in enabling data-driven decision-making and supporting various data-intensive applications and analytics initiatives.

The timeframe for becoming a data engineer can vary depending on several factors, including the individual's prior experience, educational background, dedication to learning, and the intensity of training. Generally, it takes several months to a couple of years to acquire the necessary skills and knowledge to work as a data engineer. This timeframe involves gaining proficiency in areas such as data modeling, database management, ETL (Extract, Transform, Load) processes, big data frameworks, data warehousing, and other relevant technologies and tools.

The fees for Data Engineer Course in Gandhinagar can vary based on factors such as the training provider, duration of the course, and the extent of the curriculum. Generally, the cost of data engineer training in Gandhinagar falls within the range of approximately 40,000 INR to INR 1,00,000. It is advisable to conduct thorough research on various training providers in Gandhinagar to obtain accurate and detailed information regarding the specific costs of their courses.

Data engineering and data analytics are different but complementary fields. It is not a matter of one being better than the other, but rather depends on your interests, skills, and career goals. Data engineering focuses on designing and managing data infrastructure, while data analytics involves analyzing and interpreting data to derive insights. Both fields play crucial roles in the data ecosystem, and the choice between them depends on your specific interests and career aspirations.

The average salary range for Data Engineers in Gandhinagar can vary based on factors such as experience, skills, and the organization's size. Generally, Data Engineers in Gandhinagar can expect salaries ranging from INR 4,00,000 to INR 10,00,000 per year.

Data Science and Data Engineering are considered as distinct fields. While they are closely related and often work together, Data Science focuses on extracting insights and building predictive models from data, whereas Data Engineering primarily deals with the collection, storage, processing, and management of data infrastructure.

The requirements for enrolling in a Data Engineer Course in Gandhinagar can vary depending on the training provider and the specific program. However, a background in computer science, engineering, mathematics, or a related field is generally beneficial. Some courses may have prerequisites in programming, database management, or statistics.

While experience can enhance job prospects, individuals with no prior experience can still secure entry-level Data Engineer positions by demonstrating relevant skills, completing data engineering training programs, and showcasing practical projects or certifications that validate their knowledge and capabilities.

DevOps and data engineering are distinct fields but share some overlapping concepts. DevOps focuses on collaboration between software development and operations teams, aiming to streamline software development processes, while data engineering focuses on the management and processing of data infrastructure to support data-driven operations and analytics.

The curriculum of a data engineer course typically covers essential topics such as database management, data modeling, ETL (Extract, Transform, Load) processes, big data processing frameworks, data warehousing, data governance, and data integration. It may also include practical hands-on projects to develop proficiency in relevant tools and technologies used in the industry.

FAQ’S OF DATA ENGINEER COURSE IN GANDHINAGAR

To obtain data engineering training in Gandhinagar, you can enroll in the Data Engineer Course offered by DataMites. DataMites is a reputable institute that provides comprehensive data engineering training programs. Our experienced instructors and industry-aligned curriculum will equip you with the necessary skills and knowledge to excel in the field of data engineering.

The key components of the DataMites Certified Data Engineer Training in Gandhinagar typically include comprehensive coverage of data engineering concepts, tools, and technologies. It may cover areas such as data modeling, database management, ETL processes, big data frameworks, data warehousing, data governance, and data integration. The program often includes practical projects and hands-on exercises to reinforce learning.

Eligibility criteria for enrolling in the Data Engineer Course at DataMites® in Gandhinagar may vary depending on the specific program. Generally, individuals with a background in computer science, engineering, mathematics, or related fields are eligible.

The duration of the DataMites Data Engineer Course in Gandhinagar is flexible and depends on the learning mode chosen by the participant. For online instructor-led training, the typical duration is around 6 months with more than 150 learning hours. However, the duration may vary for self-paced learning options.

DataMites® follows a certification process to validate course completion. Upon successfully fulfilling the requirements of the Data Engineer training program, you will receive a certificate from DataMites®. The certification demonstrates your proficiency and completion of the course.

DataMites® offers Data Engineer Courses with placement assistance in Gandhinagar. They aim to provide support to participants in securing suitable job opportunities in the field of data engineering. The specific details of the placement assistance can be obtained from DataMites® directly.

The Flexi-Pass concept offered by DataMites® provides learners with the flexibility to attend multiple batches of the same course within a specified timeframe. This allows learners to review the course content, revise concepts, and reinforce their learning. It provides an opportunity to revisit the course material and gain a deeper understanding of the subject.

Upon successfully completing Data Engineer training from DataMites®, you will be awarded multiple certifications. DataMites® is affiliated with renowned organizations such as the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations guarantee that the training programs meet industry standards and offer recognized certifications, validating your expertise in data engineering.

The documentation requirements for training sessions at DataMites may differ depending on the specific course and program. Generally, it is recommended that participants bring a valid identification proof, such as a government-issued ID card, along with any specific documents mentioned in the communication received from DataMites.

DataMites® usually has a policy in place to address missed sessions during Data Engineer training. They may offer options to access recorded sessions or provide opportunities to attend makeup sessions to cover the missed content.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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