CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN KOZHIKODE

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 KOZHIKODE

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 KOZHIKODE

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 KOZHIKODE

In the era of digital transformation, data engineering has emerged as a critical discipline. A report by Grand View Research predicts that the global data engineering market will reach $193.76 billion by 2028, growing at a CAGR of 23.6% from 2021 to 2028. This growth can be attributed to the rising demand for real-time data processing, the need for data-driven decision-making, and the proliferation of data-intensive technologies such as artificial intelligence and machine learning.

DataMites offers an immersive Data Engineer course in Kozhikode, designed to equip individuals with the necessary skills and knowledge to excel in the field. The course spans over 6 months, providing over 150 learning hours of comprehensive training. Participants will engage in 50+ hours of live online training, allowing them to interact with expert instructors in real-time. The course also includes 10 capstone projects and 1 client project, providing valuable hands-on experience in tackling real-world data engineering challenges. Moreover, learners receive a 365-day Flexi Pass, granting them access to course materials and the Cloud Lab for continuous learning and practice.

For those who prefer offline learning, DataMites also offers Data Engineer courses on demand in Kozhikode. These courses provide the flexibility to learn at one's own pace, with access to pre-recorded lectures and comprehensive study materials.

There are numerous reasons why DataMites is the preferred choice for Data Engineer training in Kozhikode. 

  • Led by industry expert Ashok Veda and a team of experienced faculty, DataMites ensures top-quality instruction. 

  • The course curriculum is comprehensive, covering all the essential topics and technologies needed to succeed as a data engineer. 

  • Upon successful completion of the course, participants receive globally recognized certifications from prestigious organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX.

  • DataMites offers flexible learning options including data engineer training online in Kozhikode and data engineer training offline in Kozhikode, allowing individuals to tailor their learning experience to their schedule. 

  • Engage in projects that involve working with real-world data, gaining practical skills and experience. Data Engineer Training with Internship opportunities are provided to further enhance industry exposure. 

  • DataMites offers data engineer course with placement assistance and job references, supporting participants in their career endeavors. 

  • Additionally, learners receive hardcopy learning materials and books to supplement their studies. 

  • Join the DataMites Exclusive Learning Community, fostering collaboration and networking among learners and industry professionals. 

  • The courses are priced affordably, and scholarships are available to deserving candidates.

Kozhikode, also known as Calicut, is a vibrant city located on the southwest coast of India in the state of Kerala. Known for its rich history, cultural heritage, and educational institutions, Kozhikode provides an excellent backdrop for pursuing a Data Engineer Certification. Kozhikode's thriving business ecosystem, which includes IT parks and startup incubators, offers abundant opportunities for data engineers. With its picturesque beaches, serene backwaters, and delectable cuisine, Kozhikode provides a conducive environment for learning and a high quality of life. Embrace the blend of traditional and modern influences that make Kozhikode a unique and dynamic city to pursue your data engineering aspirations.

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

ABOUT DATA ENGINEER COURSE IN KOZHIKODE

Yes, data engineering is considered part of the IT industry as it involves utilizing technology, software tools, and programming languages to process and manage data.

Data engineering offers several advantages, including efficient data processing and storage, optimized data pipelines, data integration from multiple sources, data quality assurance, and the ability to support advanced analytics and machine learning initiatives.

While DevOps and data engineering share some similarities in terms of their focus on automation and collaboration, they are distinct fields. DevOps primarily deals with software development and IT operations, while data engineering focuses on the management and processing of data.

The educational requirements for a career in data engineering typically include a bachelor's or master's degree in computer science, data science, information technology, or a related field. However, practical experience, data engineer certifications, and specialized training can also contribute to career opportunities in data engineering.

Data engineering involves designing, building, and optimizing the infrastructure necessary to handle big data and ensure its smooth flow from various sources. It includes tasks like data ingestion, transformation, storage, and data quality assurance. Data engineers play a crucial role in enabling data scientists and analysts to extract valuable insights, make informed decisions, and drive innovation across industries.

Yes, data engineering is considered a promising career option for the future due to the increasing reliance on data-driven decision-making and the growing demand for professionals who can effectively manage and analyze large volumes of data.

The eligibility criteria for enrolling in a Data Engineer Course in Kozhikode may vary depending on the training institute. Generally, a basic understanding of programming, databases, and data concepts, along with a passion for working with data, can be beneficial.

The cost of Data Engineer Training in Kozhikode can vary depending on factors such as the training provider, program duration, and delivery mode. Typically, the cost of data engineer training in Kozhikode can range from approximately 40,000 INR to INR 1,00,000, depending on the training provider, program duration, and course content.

Essential skills for success as a data engineer include proficiency in programming languages (such as Python, SQL), data modeling, database management, ETL (Extract, Transform, Load) processes, knowledge of big data technologies, cloud platforms, problem-solving abilities, and strong communication skills.

After completing Data Engineer Training, job opportunities can include roles such as Data Engineer, Data Architect, Database Developer, ETL Developer, Data Analyst, or Big Data Engineer.

FAQ’S OF DATA ENGINEER COURSE IN KOZHIKODE

DataMites® offers comprehensive data engineering training programs designed to equip individuals with the skills and knowledge required to excel in the field. Their training covers essential topics such as data integration, ETL processes, data modeling, and big data technologies, providing a strong foundation in data engineering principles. With experienced instructors, practical hands-on exercises, and industry-relevant curriculum, DataMites® empowers students to become proficient data engineers capable of handling complex data challenges in real-world scenarios.

The DataMites Certified Data Engineer Training program in Kozhikode covers the following in its curriculum:

  • Fundamentals of data engineering: Introduction to data engineering concepts, processes, and technologies.

  • Data modeling and database management: Understanding different data modeling techniques and database management systems.

  • Data integration and ETL: Learning about data integration techniques and Extract, Transform, Load (ETL) processes.

  • Big data technologies: Exploring various big data technologies like Hadoop, Spark, and NoSQL databases.

  • Data warehousing and data pipelines: Understanding data warehousing principles and building efficient data pipelines for analytics and reporting.

Flexibility: Online data engineer training from DataMites® offers flexibility in terms of schedule, allowing you to learn at your own pace and from anywhere with an internet connection.

Accessibility: With online training, you can access the course material and resources anytime, anywhere, making it convenient for individuals with busy schedules or those living in remote areas.

Interactive Learning: DataMites® provides interactive online sessions with experienced instructors, allowing for real-time discussions, Q&A sessions, and collaborative learning with fellow participants.

Practical Experience: Online data engineer training from DataMites® includes hands-on projects and assignments that simulate real-world scenarios, providing practical experience and enhancing your skills.

Cost-Effective: Online training typically has lower costs compared to in-person training, making it a more affordable option for individuals seeking data engineer training.

Networking Opportunities: Online training platforms often provide opportunities to connect with professionals in the field, allowing you to expand your network and gain valuable industry contacts.

  • The Data Engineer Course at DataMites® in Kozhikode is open to professionals who are already working in the field of data engineering or related areas and wish to enhance their skills and knowledge in this domain.

  • Individuals with a background in computer science, information technology, or related fields are eligible to participate in the Data Engineer Course at DataMites® in Kozhikode.

  • Graduates or postgraduates in engineering, computer science, mathematics, statistics, or other relevant disciplines are eligible to enroll in the Data Engineer Course at DataMites® in Kozhikode.

  • IT professionals with experience in data analysis, database management, or programming who are interested in transitioning their careers to data engineering can also participate in the course.

  • Aspiring data engineers who have a strong passion for working with data, possess analytical and problem-solving skills, and are willing to learn new technologies and tools are eligible to join the Data Engineer Course at DataMites® in Kozhikode.

DataMites offers a flexible pricing structure for Data Engineer Training in Kozhikode, with fees ranging from INR 26,548 to INR 68,000, ensuring accessibility for a wide range of learners.

The instructor for the Data Engineer Course in Siliguri at DataMites® is a highly experienced and knowledgeable professional with expertise in data engineering. They possess in-depth industry knowledge and are skilled in delivering effective training sessions to help you gain the necessary skills and knowledge in data engineering.

Depending on the chosen learning mode, the DataMites Data Engineer Course in Kozhikode can have a variable duration. Online instructor-led training generally takes around 6 months and includes over 150 learning hours.

Flexi-Pass is a unique feature by DataMites® that allows learners to customize their training experience. It provides the flexibility to choose and attend multiple courses according to their interests and learning goals.

Absolutely! When you successfully complete the Data Engineer training program at DataMites®, you will receive certifications from reputable organizations such as the International Association of Business Analytics Certifications (IABAC), Jain (Deemed-to-be University), and NASSCOM FutureSkills Prime. These certifications validate your proficiency in data engineering and add credibility to your resume, boosting your chances of securing rewarding job opportunities.

In Kozhikode, DataMites® conducts Data Engineer training in a physical classroom setup ON DEMAND, allowing students to attend face-to-face sessions and benefit from direct interaction with trainers and fellow participants.

DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient modes of payment. It is best to inquire with them for specific details.

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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