4.8 (12,500) reviews 110,000
60,900*
NO COST EMI : 10,150 p.m for 6 Months
*Offer valid till 15th December 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
• Managing Big data
MODULE 3: DATA INTEGRITY AND PRIVACY
• Data integrity basics
• Various aspects of data privacy
• Various data privacy frameworks and standards
• Industry related norms in data integrity and privacy: data engineering perspective
MODULE 4: DATA ENGINEERING ROLE
• Who is a data engineer?
• Various roles of data engineer
• Skills required for data engineering
• Data Engineer Collaboration with Data Scientist and other roles.
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• String functions part
• String functions part
• Python Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement, IF-ELSE
• NESTED IF
• Python Loops Basics, WHILE Statement
• BREAK and CONTINUE statements
• FOR statements
MODULE 3: PYTHON PACKAGES
• Introduction to Packages in Python
• Datetime Package and Methods
MODULE 4: PYTHON DATA STRUCTURES
• Basic Data Structures in Python
• Basics of List
• List methods
• Tuple: Object and methods
• Sets: Object and methods
• Dictionary: Object and methods
MODULE 5: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
• Introduction to Statistics: Descriptive And Inferential Statistics
• a.Descriptive Statistics
• b.Inferential Statistis
• Basic Terms Of Statistics
• Types Of Data
MODULE 2 : HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multistage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3 : EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies, Measure of Spread
• 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 Minkowski Distance
• Covariance and Correlation
MODULE 4 : HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• Types of Hypothesis
• P- Value, Crtical Region
• Types of Hypothesis Testing: Parametric, Non-Parametric
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis Testing (Proposed)
MODULE 1: DATA WAREHOUSE FOUNDATION
• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• Data Lake house
• 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 architecture
• Data Warehouse vs Data Lake
MODULE 2: 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 3: DOCKER AND KUBERNETES FOUNDATION
• Docker Introduction
• Docker Vs.VM
• Hands-on: Running our first container
• Common commands (Running, editing,stopping,copying and managing images)YAML(Basics)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Docker swarm vs kubernetes
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 : AWS DATA SERVICES INTRODUCTION
• AWS Overview and Account Setup
• AWS IAM Users, Roles and Policies
• AWS S overview
• AWS EC overview
• AWS Lamdba overview
• AWS Glue overview
• AWS Kinesis overview
• AWS Dynamodb overview
• AWS Athena overview
• AWS Redshift overview
MODULE 2 : DATA PIPELINE WITH GLUE
• AWS Glue Crawler and setup
• ETL with AWS Glue
• Data Ingesting with AWS Glue
MODULE 3 : DATA PIPELINE WITH AWS KINESIS
• AWS Kinesis overview and setup
• Data Streams with AWS Kinesis
• Data Ingesting from AWS S 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 Blob Storage
MODULE 7: AZURE DATA FACTORY
• Azure Data Factory Introduction
• Data transformation with Data Factory
• Data Wrangling with Data Factory
MODULE 8 : AZURE DATABRICKS
• Azure databricks introduction
• Azure databricks architecture
• Data Transformation with databricks
MODULE 9 : AZURE RDS
• Creating a Relational Database
• Querying in and out of Relational Database
• ETL from RDS to databricks
MODULE 10 : AZURE RDS
• Hands-on Project Case-study
• Setup Project Development Env
• Organization of Data Sources
• AZURE/AWS services for Data Ingestion
• Data Extraction Transformation
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
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
• Key Terms: Output Format
• Partitioners Combiners Shuffle and Sort
• Hands-on Map Reduce task
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Resilient distributed datasets (RDD),Working with RDDs in PySpark, Spark Context , Aggregating Data with Pair RDDs
• Spark Databricks
• Spark Streaming
MODULE 1: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
MODULE 2: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
MODULE 3: KAFKA and Spark
• Creating an HDFS cluster with containers
• Creating pyspark cluster with containers
• Processing data on hdfs cluster with pyspark cluster
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI Basics
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3: DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
Large-scale data gathering, storage, and analysis systems are developed and built through the process of data engineering. It is a broad field with applications in practically all industries.
You can learn how to become a data engineer by enrolling in courses, which can run anywhere from three to twelve months. On the other hand, the course content differs depending on the degree or certification sought after. 3-month courses can give you valuable Data Engineer experience and internship opportunities, which can lead to entry-level careers at reputable companies.
Getting the right training in the field is the first and most crucial step to becoming a data engineer. For one to find employment in the sector, one needs to complete a certification course to gain a comprehensive understanding of the data science and data engineering domain and upskill one's skills.
Because it qualifies you as a specialist in the subject of data science, the Data Engineer Course in Coimbatore is the one to take if you want to work in the industry. After completing our extensive curriculum, you'll possess the abilities necessary to be a successful data engineer in addition to a portfolio that is ready for employment that you can use to impress potential employers.
For admittance into this field, one must possess a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. You'll need practical experience, like an internship, to even be considered for most entry-level positions.
The greatest institute for thorough instruction in courses in data engineering, data science, artificial intelligence, and other related topics is DataMites®. In order to develop and provide a comprehensive artisan training program, DataMites® works with recognized data engineering professionals.
Depending on the type of training you select and the level of the course, the cost of Data Engineer training in Coimbatore can be anywhere between 20,000 INR and 80,000 INR.
It's not always an entry-level position for data engineering. Many data engineers, however, begin their careers as software engineers or business intelligence analysts. As your career progresses, you might take on administrative responsibilities or work as a data architect, solutions architect, or machine learning engineer.
Coding, data warehousing, database management, data analysis, critical thinking, and an understanding of machine learning are some of the fundamental skills of a data engineer.
While data scientists analyze the data to identify trends, generate business insights, and provide answers to pertinent organizational questions, data engineers create and manage the systems and structures that store, retrieve, and organize data.
Python for Data Engineering includes all aspects of data wrangling, including reshaping, collecting, and tying together many sources of data, small-scale ETL, API interaction, and automation. Many factors contribute to Python's popularity. Its accessibility is one of its most important benefits.
Overall, a career as a data engineer is a great fit for those who value accuracy, following engineering specifications and building pipelines that turn raw data into actionable insights. Data engineers have an excellent chance of making a good living and having stable employment.
Before applying for full-time data engineering work, it's a good idea to start with an internship. Because data engineering involves practice, internships are essential to gaining experience and increasing practical knowledge prior to landing a full-time job. People with no prior work experience are more likely to be offered internships by businesses. When you have finished an internship, it will be considerably simpler for you to land an entry-level job with the company.
Data engineering is a steady, financially lucrative, and physically demanding profession. Realizing data's full potential in any organization requires the expertise of a data engineer. With over 88.3% more job postings in 2019 and more than 50% more open positions year over year, a poll found that it is one of the professions with the strongest global growth rates.
It is also a critical step in the hierarchy of data science requirements since analysts and scientists cannot access or interact with data without the architecture created by data engineers. Businesses run the danger of losing access to one of their most priceless assets as a result. According to the Dice 2020 Tech Career Report, with a 50% increase in accessible positions year over year, data engineering is the position in technology that is growing the quickest in 2019.
Balancing immediate needs with a longer-term perspective of where data demands will take the systems they oversee is difficult for data engineers to do. With each new architecture you create, you constantly worry that you'll run into a technical wall. Data is unquestionably essential for developing your company and learning insightful information. Despite being difficult to learn, a data engineering course can be useful for gaining the necessary expertise in the field.
According to a survey conducted by DICE, an online platform that manages one of the largest databases of technology specialists, the position of Data Engineer will experience the fastest growth in the field of technology in 2020, with a growth rate of over 50% over the previous year. An extensive increase in demand for jobs in data engineering has been detected, according to a recent survey. To develop scalable solutions, you'll draw on your programming and analytical abilities.
Data Engineering is taught from inception in DataMites® Data Engineer Courses in Coimbatore. Anybody can now enroll in the course. This career path is for people looking for a change in their career, data professionals looking to broaden their skill set for the next promotion, and college students looking for employment.
A data engineer in India earns an average amount of INR 10,00,000 per year! (Glassdoor) the average salary for a Data Engineer in Coimbatore is 5,97,647 LPA.
There is a lot of room for growth in the data engineering field in terms of knowledge, capability, and income. Aspirants can enroll in the DataMites online Data Engineer Course in Coimbatore, where we offer comprehensive instruction for their future job.
Three months and a total of 120 hours of instruction make up the Data Engineer Course in Coimbatore. Weekdays and weekends both have training sessions. Any option is there for you to select.
No, a postgraduate degree is not required, although having prior experience in mathematics, statistics, economics, or computer science can be very helpful.
With the current discount, you can enroll in the data engineering course online for just 31,395 INR instead of the 42,000 INR that it would normally cost in Coimbatore.
In the Indian states of Bangalore, Chennai, Pune, Hyderabad, and Kochi, DataMites® does indeed provide Data Engineer Classroom Courses. Depending on the availability of additional candidates from the exact place, we would be happy to host one in other locations upon the applicants' DEMAND.
With decades of experience in the field and a strong understanding of the material, we're adamant about giving you access to certified, highly experienced trainers.
We provide you with a variety of flexible learning alternatives, such as live online training, self-paced courses, and classroom instruction. Depending on your schedule, you can make a decision.
You can attend DataMites® classes for three months that are connected to any query or revision you want to clear thanks to our Flexi-Pass for Data Engineer training.
The results are immediately available if you take the exam online at exam.iabac.org. IABAC regulations state that issuing an e-certificate takes 7 to 10 business days.
We will grant you certifications from IABAC®, NASSCOM Future Skills, and JAINx, which guarantee your skills' global recognition.
Of course, we'll provide you with a Data Engineer Course Completion Certificate once your training is finished.
Yes. A National ID card, a driver's license, or another form of photo ID is necessary to book the certification exam and provide the participation certificate.
Concerning it is unnecessary. To plan a lesson that fits within your schedule, just contact your professors about the issue.
For Data Engineer Training Online in Coimbatore, every session will be recorded and published so you can simply catch up on what you missed at your own pace and ease.
Yes, a free trial class will be offered to you so that you can have a taste of what the training entails and how it will be conducted.
You must pay the entire course fee to reserve your seat in the entire program and to schedule your certification exams with IABAC. Your DataMites® relationship manager can help with part payment agreements if you have any special limitations.
At DataMites®.com, you can use your specific certification number to verify all certificates. You can also email care@DataMites®.com as an alternative.
It goes without saying that you need to maximize your training sessions. If you need more clarity, you may request a support session, of course.
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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