4.8 (12,500) reviews 110,000
60,900*
NO COST EMI : 10,150 p.m for 6 Months
*Offer valid till 21st April 2024
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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.
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
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND REGRESSION
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
MODULE 2 : DATA INGESTION USING AWS LAMDBA
MODULE 3 : DATA PIPELINE WITH AWS KINESIS
MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT
MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE
MODULE 6 : STORAGE IN AZURE
MODULE 7: AZURE DATA FACTORY
MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS
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
MODULE 2 : SQL BASICS
MODULE 3 : DATA TYPES AND CONSTRAINTS
MODULE 4 : DATABASES AND TABLES (MySQL)
MODULE 5 : SQL JOINS
MODULE 6 : SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
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
Large-scale data gathering, storage, and analysis systems are created through a process called data engineering. There are uses for it in practically every business, and it is a broad field.
The first and most crucial stage in the process of becoming a data engineer is to receive the necessary training. Finding work in the industry requires taking a certification course to gain a complete understanding of the data science and data engineering domain and to upskill one's talents.
You can become a data engineer by enrolling in one of the three- to twelve-month-long Data Engineer Courses in Kochi. The targeted degree or certification, on the other hand, determines the course content. You can gain valuable Data Engineer experience and open up internship opportunities through 3-month courses, which will help you land entry-level jobs at reputable companies.
If you want to work in the industry, the Data Engineer Course in Kochi is the one you should enroll in because it accredits you as a data science specialist. You'll have the abilities required to be a successful data engineer after completing our in-depth curriculum, as well as a portfolio that's ready for a job interview.
To enter this area, one must hold a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a closely related field. You will require practical experience, such as an internship, to even be considered for the majority of entry-level positions.
Depending on the level of instruction and the training program you select, the Data Engineer Training Fee in Kochi might be anywhere between 20,000 INR and 80,000 INR.
For thorough instruction in programs in data engineering, data science, artificial intelligence, and other related topics, DataMites® is the ideal institution. To create and provide a comprehensive artisan training program, DataMites® works with eminent data engineering experts.
An entry-level position isn't always available in data engineering. Rather, a lot of data engineers start off as software engineers or business intelligence analysts. You might take on administrative responsibilities as your career develops, or you might work as a machine learning engineer, data architect, or solutions architect.
Data analysis, coding, data warehousing, database management, critical thinking, and an understanding of machine learning are just a few of the fundamental skills needed for a data engineer.
Data scientists study the data to identify patterns, generate business insights, and provide appropriate answers to queries for the organization, whereas data engineers design and manage the systems and structures that store, retrieve, and organize data.
Python for Data Engineering includes data wrangling activities like reshaping, aggregating, and linking many sources, small-scale ETL, API interaction, and automation. There are several reasons why Python is popular. Its accessibility is one of the main advantages.
Overall, working as a data engineer is a great career choice for those who value attention to detail, following engineering specifications, and building pipelines that turn raw data into insightful information. A career in data engineering offers significant earning potential as well as work stability.
Being a data engineer is a physically demanding yet financially rewarding occupation. Realizing the full potential of data in every organization requires the contribution of a data engineer. It is one of the professions with the strongest global growth rates, with over 88.3 percent more job posts in 2019 and over 50% more open opportunities year over year.
Before submitting an application for a full-time data engineer job, it's a good idea to start with an internship. Internships are essential for getting experience and increasing practical knowledge before landing a full-time job in data engineering because this field requires practice. People who have never worked before are more likely to be offered internships by companies. Once your internship is over, it will be much simpler for you to land an entry-level job with the company.
Furthermore, it has a significant place in the hierarchy of prerequisites for data science because analysts and scientists cannot access or operate with data without the infrastructure created by data engineers. And as a result, businesses run the danger of losing access to one of their most valuable assets. According to the Dice 2020 Tech Career Report, data engineering will have the biggest growth in employment in 2019, with a 50% increase in open positions.
Data engineers' difficult task is to strike a balance between immediate needs and a longer-term perspective of where data demands will take the systems they supervise. With every new architecture you create, there is a persistent worry that you will encounter a technological brick wall. Without a doubt, data is essential for developing your company and learning new information. Even if it is difficult to learn, a data engineering course might be useful for gaining the necessary domain knowledge.
According to a poll conducted by DICE, an online platform that manages one of the largest databases of technology specialists, Data Engineer will have a growth rate of over 50% year over year by the year 2020, making it the fastest-growing career in technology. A recent survey revealed that there has been a significant increase in demand for jobs in data engineering. To design scalable solutions, you'll use your programming knowledge and analytical abilities.
There is a tonne of opportunity for development in the field of data engineering, both in terms of knowledge and ability as well as compensation. For a thorough training program for your future job, applicants can enroll in the DataMites Online Data Engineer Course in Kochi.
The three-month Data Engineer Course in Kochi includes 120 hours of instruction. On weekdays and weekends, training sessions are held. Depending on your availability, you can pick any.
The price of the online data engineering course in Kochi is 42,000 Indian Rupees, but thanks to the current discount, you can enroll for just 31,395 INR.
The DataMites® Data Engineer Courses in Kochi have been thoughtfully designed to educate data engineering from inception. Everyone is now able to enroll in the course. This career path is intended for persons looking for a change in career, data professionals looking to advance their skill set, and college students looking for employment.
Having prior knowledge of mathematics, statistics, economics, or computer science can be very helpful, but a PG degree is not required.
The national average salary for a Data Engineer in India is INR 8,65,008 per year! (Payscale) A data engineer in Kochi receives an annual salary of 8,57,557 INR! (Indeed.com)
Bangalore, Chennai, Pune, Hyderabad, and Kochi do indeed provide Data Engineer Classroom Courses through DataMites®. On the applicants' request and based on the availability of additional candidates from the specific place, we would be happy to host one in other locations.
We provide you with customizable learning alternatives that range from live online training to self-paced courses and classroom instruction. You may select according to your schedule.
We are committed to offering you trainers who are highly qualified, certified, have a lot of experience in the field, and are knowledgeable about the material.
You can attend sessions from DataMites® for any query or revision you want to clear for three months with our Flexi-Pass for Data Engineer training.
We will provide you certifications for your pertinent abilities from IABAC®, NASSCOM Future Skills, and JAINx, which are recognized internationally.
The exam results are immediately accessible if you take them online at exam.iabac.org. E-certificate issuance takes 7 to 10 business days, as per IABAC requirements.
You will receive a Data Engineer Course Completion Certificate once your course is over, of course.
Yes. The participation certificate must be issued and the certification exam must be scheduled using photo IDs such as a national ID card, driver's license, etc.
You shouldn't be concerned about it. Simply get in touch with your instructors and arrange a lesson time that works for you. Data Engineer Training Online in Kochi will record and publish each session so that you can simply catch up on what you missed at your own pace and comfort.
You will receive a complimentary demo session to provide you with a quick overview of the training's objectives and methodology.
To reserve your seat for the whole course and to work with IABAC to schedule your certification exams, the course fee must be paid in full. Your DataMites® relationship manager can help you with part payment agreements if you have any special constraints.
Using your specific certification number, all certificates can be validated at DataMites®.com. You could also send a message to care@DataMites®.com.
Yes, you must undoubtedly maximize your training sessions. If you require any additional clarity, you can, of course, request a support session.
Payment can be made by using;
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: -
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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