CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN PANAJI

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 PANAJI

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 PANAJI

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 PANAJI

Data engineering is a thriving sector in the digital economy. As per a report by MarketsandMarkets, the global data engineering market is anticipated to reach $146.38 billion by 2026, at a CAGR of 15.9% during the forecast period. The report outlines the increasing importance of data lakes, data warehouses, and data pipelines in managing diverse data sources, contributing to the growth of the data engineering market.

DataMites offers a comprehensive Data Engineer course in Panaji, designed to provide students and professionals with the necessary skills and knowledge required for a successful career in data engineering. The course has a duration of 6 months, comprising over 150 learning hours. It includes 50+ hours of live online training, allowing participants to interact with expert instructors in real-time. The course also features 10 capstone projects and 1 client project, providing hands-on experience in solving real-world data engineering challenges. 

Additionally, learners receive a 365-day Flexi Pass, which grants them access to course materials and the Cloud Lab for continuous learning and practice. For individuals who prefer offline learning, DataMites also offers Data Engineer courses on demand in Panaji. These courses provide the flexibility to learn at one's own pace, with pre-recorded lectures and comprehensive study materials.

There are several reasons why choosing DataMites for Data Engineer Training in Panaji is a wise decision. 

  • Firstly, DataMites boasts experienced trainers, including renowned expert Ashok Veda, along with a skilled faculty team. 

  • The course curriculum is comprehensive, covering all the essential topics and technologies required to excel 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 online data engineer course in Panaji and data engineer training offline in Panaji, allowing individuals to balance their training with other commitments. 

  • The course includes projects that involve working with real-world data, enabling learners to gain practical experience. Data Engineer Internship opportunities are also provided to further enhance their skills and industry exposure. 

  • DataMites offers data engineer course with placement assistance and job references to help students kickstart their careers in data engineering. Participants receive hardcopy learning materials and books to support their studies. 

  • DataMites also provides an exclusive learning community where learners can collaborate and network with peers and industry professionals. The courses are affordably priced, and scholarships are available to deserving candidates.

Panaji, also known as Panjim, is the capital city of the Indian state of Goa. It is a charming and vibrant city with a mix of colonial Portuguese architecture, beautiful beaches, and a rich cultural heritage. The city's thriving tourism industry, technology sector, and emerging startups present ample prospects for data engineers. Additionally, Panaji's laid-back atmosphere, delicious cuisine, and picturesque surroundings make it an appealing location for professionals seeking a well-rounded work-life balance.

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

ABOUT DATA ENGINEER COURSE IN PANAJI

Data engineering involves the design, development, and management of systems and processes for collecting, storing, processing, and analyzing large volumes of data. It focuses on ensuring data reliability, efficiency, and availability for effective data-driven decision-making.

To pursue a career as a data engineer in Panaji, you can follow these steps:

a. Obtain a degree in computer science, engineering, or a related field.

b. Develop proficiency in programming languages such as Python, SQL, or Java.

c. Gain knowledge of database management systems and data processing frameworks like Hadoop and Spark.

d. Acquire practical experience through internships, projects, or working on data-related tasks.

e. Continuously update your skills by staying informed about emerging technologies and industry trends.

Yes, it is possible to transition from the mechanical domain to data engineering. While a background in computer science or a related field may provide a smoother transition, you can bridge the gap by acquiring relevant skills such as programming, database management, and data processing. Additional training or data engineer certification programs specific to data engineering can also be beneficial.

Emerging trends in data engineering include:

a. Adoption of cloud-based data platforms and services.

b. Integration of artificial intelligence and machine learning in data processing and analysis.

c. Increased focus on real-time data streaming and processing.

d. Implementation of data governance and data privacy regulations.

e. Utilization of automated data pipeline orchestration tools.

The future prospects for individuals pursuing a career as data engineers are promising. With the exponential growth of data and the increasing reliance on data-driven decision-making, there is a growing demand for skilled data engineers across industries. Companies need professionals who can handle complex data infrastructure, process large datasets, and extract meaningful insights, leading to a wide range of career opportunities.

The cost of Data Engineer Training in Panaji may vary depending on factors such as the institute, course duration, and training mode (online or classroom). Typically, the fees for data engineer training in Panaji can range from approximately 40,000 INR to INR 1,00,000. It is recommended to research different training providers in Panaji to determine the specific costs associated with their courses.

DataMites is considered one of the top choices for Data Engineer Training. With their comprehensive curriculum, industry-relevant projects, and experienced instructors, DataMites provides high-quality training in data engineering. We have a strong track record of delivering excellent education and equipping individuals with the skills and knowledge needed to succeed in the field of data engineering.

After completing Data Engineer Training, one can expect job roles such as Data Engineer, Database Administrator, ETL Developer, Big Data Engineer, Cloud Data Engineer, or Data Warehouse Engineer. These roles can be found in industries like technology, finance, healthcare, e-commerce, and more.

Essential skills for a successful data engineer include:

a. Proficiency in programming languages like Python, SQL, or Java.

b. Knowledge of database management systems and data modeling.

c. Experience with big data processing frameworks like Hadoop, Spark, or Apache Kafka.

d. Understanding of data integration and ETL (Extract, Transform, Load) processes.

e. Strong problem-solving and analytical skills.

f. Familiarity with cloud platforms and data warehousing concepts.

g. Effective communication and collaboration skills.

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

FAQ’S OF DATA ENGINEER COURSE IN PANAJI

Choosing DataMites for Data Engineer Training in Panaji offers several advantages. They provide comprehensive and industry-relevant training programs that cover essential data engineering concepts, tools, and techniques. With experienced instructors, practical projects, and hands-on learning, DataMites ensures that you gain the necessary skills and knowledge to excel in the field of data engineering.

The DataMites Certified Data Engineer Training program in Panaji covers a wide range of topics, including data engineering fundamentals, database management, data warehousing, ETL (Extract, Transform, Load) processes, big data processing frameworks, data visualization, and advanced analytics techniques.

The duration of the DataMites Data Engineer Course in Panaji varies based on the learning mode selected. Typically, online instructor-led training lasts for approximately 6 months, comprising more than 150 learning hours. However, the duration may differ for self-paced learning alternatives.

The cost of Data Engineer Training at DataMites in Panaji can vary depending on factors such as the program, training mode (online or classroom), and any additional features or resources included. The fees for the data engineer course at DataMites in Panaji range from approximately INR 26,548 to INR 68,000, depending on the specific program and any additional features included.

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.

The eligibility criteria to enroll in the Data Engineer Course at DataMites® in Panaji generally include a background in computer science, engineering, mathematics, or related fields.

Yes, upon completion of Data Engineer training from DataMites®, you will receive multiple certifications. DataMites® is affiliated with esteemed organizations such as the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations ensure that the training programs meet industry standards and provide recognized certifications.

If you miss a session during Data Engineer training at DataMites®, they typically provide options to access the recorded sessions or attend a makeup session at a later date. DataMites aims to ensure that learners have the opportunity to cover missed content and continue their learning journey.

DataMites® often provides the option to attend a demo class before making the course fee payment. This allows you to experience the teaching style, interact with instructors, and get a glimpse of the course content and structure. It helps in making an informed decision before committing to the training program.

DataMites® offers both online and classroom training options for Data Engineer courses in Panaji. Learners can choose the training mode that best suits their preferences and schedule. Both modes provide high-quality instruction and practical learning experiences to help you master data engineering skills.

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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