CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN IMPHAL

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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA ENGINEER ONLINE CLASSES IN IMPHAL

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 IMPHAL

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 IMPHAL

Data engineers are the unsung heroes behind the scenes of the data revolution. With the exponential growth of data, the demand for skilled professionals in this field has skyrocketed. According to industry reports, the job market for data engineers is expected to grow by 30% in the next five years, making it one of the fastest-growing professions.

DataMites offers a comprehensive Data Engineer Course in Imphal, designed to equip aspiring professionals with the skills and knowledge required to excel in the field. The course spans over 6 months and encompasses more than 150 hours of immersive learning. Participants will benefit from 50+ hours of live online training, providing interactive sessions with industry experts. To enhance practical skills, the course includes 10 capstone projects and 1 client project, allowing learners to apply their knowledge to real-world scenarios. With a 365-day flexi pass and access to a cloud lab, students have the flexibility to practice and experiment with data engineering tools and technologies.

For those preferring offline learning, DataMites also offers Data Engineer courses on demand in Imphal. These offline courses provide learners with the opportunity to study at their own pace, with the flexibility of scheduling their training sessions according to their convenience.

Here are 10 compelling reasons to choose DataMites for Data Engineer Training in Imphal:

Expert Faculty: DataMites boasts a team of experienced faculty members, including renowned data expert Ashok Veda, who provide comprehensive guidance and mentorship throughout the course.

Comprehensive Curriculum: The course curriculum is carefully designed to cover all essential concepts and skills required for data engineering, ensuring a well-rounded learning experience.

Global Certification: DataMites offers prestigious global certifications, including IABAC, NASSCOM FutureSkills Prime, and JainX, which validate the expertise and competency of learners in the field of data engineering.

Flexible Learning Options: DataMites provides flexible learning options, allowing participants to choose from live online data engineer training in Imphal or data engineer course offline in Imphal according to their preferences and convenience.

Real-World Projects: The training includes hands-on projects that involve working with real-world data, enabling learners to gain practical experience and develop problem-solving skills.

Internship Opportunity: DataMites provides data engineer course with internship opportunities to students, offering them a chance to apply their knowledge in a professional setting and gain valuable industry exposure.

Placement Assistance: The training program includes data engineer training with placement assistance and job references, helping students connect with potential employers and enhance their career prospects.

Learning Materials: Participants receive hardcopy learning materials and books, ensuring easy access to reference materials even after the completion of the course.

Exclusive Learning Community: DataMites fosters an exclusive learning community where learners can interact, collaborate, and share insights with fellow data enthusiasts, creating a supportive and engaging learning environment.

Affordable Pricing and Scholarships: DataMites strives to make quality education accessible by offering competitive pricing and scholarship opportunities to deserving candidates.

Imphal, the capital city of Manipur, is a vibrant and culturally rich location in Northeast India. Known for its scenic beauty and historical significance, Imphal offers a conducive environment for learning. The city is home to reputed educational institutions and is witnessing a growing interest in the field of data engineering. Students in Imphal can benefit from DataMites' data engineer course training in Imphal to acquire in-demand skills and tap into the emerging opportunities in the data engineering industry.

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

ABOUT DATA ENGINEER COURSE IN IMPHAL

Data engineering can be explained as the practice that involves the design, development, and management of systems and processes to handle and analyze large amounts of data effectively. It emphasizes the creation of reliable data pipelines, ensuring the quality and integrity of data, and enabling data-driven decision-making. Data engineering plays a crucial role in the collection, organization, and utilization of data for various applications and business insights.

Individuals aspiring to pursue a data engineering career in Imphal can take the following steps:

  • Lay a solid foundation in mathematics, statistics, and programming.

  • Gain proficiency in programming languages like Python or SQL.

  • Develop expertise in database management systems and data manipulation techniques.

  • Familiarize themselves with big data technologies such as Hadoop and Spark.

  • Enhance their skills through hands-on projects and practical experience.

Certainly, data engineering holds promising prospects for the future. The growing importance of data-driven decision-making and the exponential growth of data volumes have created a strong demand for skilled data engineers. The role of data engineering in handling and processing data efficiently to extract valuable insights and drive business outcomes positions it as a field with abundant opportunities and a bright future ahead.

Undergoing training in data engineering offers several advantages for individuals, including:

  • Developing valuable skills and knowledge in the field of data engineering.

  • Expanding career opportunities in diverse industries.

  • Gaining practical experience through project-based learning.

  • Keeping abreast of industry trends and technological advancements.

Joining a data engineer course in Imphal typically requires meeting certain criteria that may vary depending on the course and institution. Generally, it is beneficial to have a basic understanding of mathematics, statistics, and programming. Familiarity with databases, SQL, and programming languages such as Python or Java can also be advantageous for a more seamless learning journey.

Prerequisites for enrolling in a data engineer course in Imphal may vary depending on the specific program and institution. However, a basic understanding of mathematics, statistics, and programming concepts is generally beneficial. Knowledge of databases, SQL, and programming languages like Python or Java can also be advantageous.

Individuals considering data engineer training in Imphal should anticipate costs that may vary based on factors like the training institution, program duration, and training mode (online or classroom). Generally, the fees for data engineer training in Imphal can be expected to range from approximately 40,000 INR to 1,00,000 INR.

For data engineer training, DataMites comes highly recommended as an institute of choice. They provide a comprehensive curriculum, industry-relevant projects, and experienced instructors who equip students with the essential skills and knowledge required in data engineering.

After undergoing data engineer training, individuals can embark on various career paths, including opportunities as Data Engineers, Database Administrators, ETL Developers, Big Data Engineers, and Cloud Data Engineers. These career options span industries such as technology, finance, healthcare, and e-commerce.

Data engineer job positions are not exclusively limited to individuals with prior experience. Entry-level roles or positions targeted at individuals with limited experience are available in the data engineering field. By participating in internships or gaining practical experience through projects, individuals without experience can showcase their skills and competence to secure data engineer job positions.

Data engineer training adds substantial value by equipping individuals with the skills and knowledge required for success in the field. It encompasses crucial concepts, tools, and techniques involved in developing robust data pipelines, managing databases, and ensuring efficient data processing and analysis. This proficiency is highly coveted in the modern business landscape, where data plays a central role in driving growth, innovation, and competitiveness.

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FAQ’S OF DATA ENGINEER COURSE IN IMPHAL

If you're looking to obtain data engineering training in Imphal, one option is to enroll at DataMites. They offer comprehensive courses that cover essential data engineering concepts, tools, and techniques. With a focus on practical learning, hands-on experience, and expert guidance, DataMites provides a strong foundation in data engineering.

The DataMites Certified Data Engineer Training program in Imphal provides instruction in a variety of subjects. These include data engineering concepts, tools, and technologies like Hadoop, Spark, SQL, and data pipeline development. The program also incorporates practical exercises and hands-on projects to ensure a comprehensive understanding and practical application of these topics.

The requirements for enrolling in the Data Engineer Course at DataMites® in Imphal can vary. Typically, individuals with a background in computer science, mathematics, or related fields, as well as professionals aspiring to work in data engineering roles, are eligible to enroll.

Yes, DataMites® conducts classroom training for Data Engineer Courses in Imphal, in addition to their online training offerings. They ensure flexibility by providing individuals the option to select the training mode that suits their needs and preferences.

The instructors for the Data Engineer Course in Imphal at DataMites® have a strong background in data engineering. They possess expertise in data engineering concepts, tools, and industry practices, allowing them to provide valuable instruction and guidance to participants throughout the training program.

DataMites® offers a variety of learning formats for data engineering courses, including instructor-led online training, classroom training, and self-paced learning. These options allow individuals to select the learning format that best meets their preferences and time constraints.

Yes, at DataMites®, individuals are given the opportunity to attend demo classes before they are required to pay the course fee. This allows them to get a preview of the teaching style, course content, and interact with instructors, empowering them to make an educated choice.

Yes, at DataMites®, individuals have the option to pay the course fee in installments. This flexible payment arrangement considers the financial constraints that learners may face, allowing them to manage the course fee while pursuing their data engineering training.

Certainly, DataMites® offers placement support for individuals enrolled in Data Engineer Training in Imphal. They extend valuable assistance in terms of career guidance, resume preparation, interview readiness, and facilitating job placements to enhance participants' career prospects.

DataMites® offers flexible payment options for their training programs, providing learners with convenience and ensuring secure transactions. Accepted payment methods include credit cards, debit cards, net banking, and other digital payment options. Through their online channels, learners can choose the payment method that aligns with their preferences, facilitating a smooth and seamless transaction process while maintaining security.

The expected length of the DataMites Data Engineer Course in Imphal depends on the chosen learning mode. On average, online instructor-led training spans approximately 6 months, with over 150 learning hours. However, the duration may differ for self-paced learning options.

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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