4.8 (14,000) reviews 110,000
77,726*
NO COST EMI : 12,954 p.m for 6 Months
*Offer valid till 24th November 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 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 Gurgaon. 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 Gurgaon 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 Gurgaon might be anywhere between 20,000 INR and 80,000 INR.
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.
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.
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.
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.
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.
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.
The national average salary for a Data Engineer in India earns an average amount of INR 10,49,170 per year! And the salary for a data engineer in Gurgaon is 9,71,025 LPA. (Indeed)
The three-month Data Engineer Course in Gurgaon includes 120 hours of instruction. On weekdays and weekends, training sessions are held. Depending on your availability, you can pick any.
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 Gurgaon.
The DataMites® Data Engineer Courses in Gurgaon 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 price of the online data engineering course in Gurgaon is 42,000 Indian Rupees, but thanks to the current discount, you can enroll for just 31,395 INR.
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 Gurgaon 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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