4.8 (11,800) 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
The development and construction of massive data collection, storage, and processing systems are known as data engineering. With applications in practically every industry, it is a broad field.
The first and most crucial stage in becoming a data engineer is to complete the necessary training in the subject. If you want to work in the industry, you must complete a certification course to gain a deep understanding of the data science and data engineering domain and to upskill your skills.
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.
If you want to work in the industry, you should enroll in the Data Engineer Course in Noida because it accredits you as a data science specialist. After completing our extensive curriculum, you'll possess the abilities necessary to be a successful data engineer as well as a portfolio that is ready for use in employment interviews.
Entry into this field requires a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. Most entry-level positions require real-world experience, such as internships, to even be considered for.
Depending on the level and kind of training you select, the Data Engineer Training Fee in Noida can be anywhere between 20,000 INR and 80,000 INR in India.
For in-depth instruction in courses in data engineering, data science, artificial intelligence, and other related topics, DataMites® is the ideal educational facility. To create and provide a comprehensive crafter training program, DataMites® works with eminent data engineering professionals.
Not all positions in data engineering are entry-level. In opposition to this, a lot of data engineers begin their careers as software engineers or business intelligence analysts. As your career progresses, you might take on administrative responsibilities or work as a machine learning engineer, data architect, or solutions architect.
Coding, data warehousing, database systems, data analysis, critical thinking, comprehending machine learning, and other abilities are among the fundamental skills of a data engineer.
Data scientists evaluate the data to identify patterns, gain business insights, and provide answers to issues that are pertinent to the organization. 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 linking many sources, small-scale ETL, API interaction, and automation. Python is well-liked for many different reasons. Its accessibility is one of the biggest benefits.
Overall, a career as a data engineer is a great fit for those who value accuracy, adherence to engineering standards, and building pipelines that turn raw data into actionable insights. Data engineering careers have a high-income potential and stable employment.
A profession as a data engineer is stable, financially lucrative, and physically demanding. Every firm needs a data engineer to help it utilize data to its fullest potential. According to a poll, it is one of the professions with the fastest global growth, with over 88.3% rise in job posts in 2019 and over 50% growth in the number of available positions.
In the hierarchy of data science requirements, it's also a crucial step since, without the architecture created by data engineers, analysts and scientists won't be able to access or use data. Consequently, businesses run the danger of losing access to one of their most priceless assets. According to the Dice 2020 Tech Career Report, data engineering is the field of technology with the biggest growth in 2019, with a 50% increase in open positions year over year.
Before applying for full-time data engineer 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 before 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.
The challenging work for data engineers is to balance short-term requirements with a longer-term view of where data demands will take the systems they oversee. There is a constant fear that you are stuck in a technical dead-end with every new architecture you design. Data is unquestionably crucial for growing your business and acquiring insightful knowledge. A data engineering course though challenging to learn can come in handy for proper expertise in the domain.
A poll conducted by DICE, an online platform that maintains one of the largest databases of technology specialists, found that the fastest-growing position in technology is data engineer, with a year-over-year increase of more than 50% in 2020. A recent survey revealed that demand for jobs in data engineering has significantly increased. To develop scalable solutions, you'll use your programming and problem-solving abilities.
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 Noida is 7,98,089 LPA.
There is a lot of space for improvement in the data engineering field in terms of capacity, remuneration, and learning. Aspirants can enroll in our Data Engineer Course Online in Noida at DataMites®, where we offer comprehensive instruction for their future careers.
The Noida-based DataMites® Data Engineer Courses have been thoughtfully designed to educate data engineering from inception. Henceforth, anyone may enroll in the course. The people looking for a career change, the data professionals looking to broaden their skill set for the next promotion, and the job-seeking college students should all consider this career route.
The Data Engineer Training in Noida lasts for 3 months and includes 120 hours of instruction. On weekdays and weekends, training sessions are conducted. You can select any option depending on your availability.
No, a postgraduate degree is not required, however having prior knowledge of mathematics, statistics, economics, or computer science can be very helpful.
The International Association of Business Analytics Certification (IABAC), NASSCOM, and Jain University have all granted accreditation to the international institute for data engineer training known as DataMites.
The cost of a data engineering course online in Noida is 42,000 INR, but thanks to a current discount, you may enroll for the course for just 31,395 INR.
Yes, DataMites® offers classroom courses for data engineers in Bangalore, Chennai, Pune, Hyderabad, and Kochi in India. Depending on the demand of the applicants and the availability of other candidates from the specific place, we would be happy to host one in another location.
We are adamant about giving you access to certified, highly trained trainers that have years of expertise in the field and are knowledgeable about the material.
We provide you with a variety of flexible learning alternatives, including live online training, self-paced courses, and classroom instruction. You can make your selection based on your schedule.
With our Flexi-Pass for Data Engineer training, you get three months to attend DataMites® sessions linked to any query or revision you want to clear.
We will provide you with certifications from IABAC®, NASSCOM Future Skills, and JAINx, which offer universal recognition for pertinent skills.
The results of the exam can be seen right away if you take it online at exam.iabac.org. IABAC rules state that e-certificate issuance takes 7 to 10 business days.
Naturally, we will give you a Data Engineer Course Completion Certificate once your course is over.
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.
Yes, you will be given a complimentary demo class to provide you with a quick overview of the training's procedures and contents.
To reserve your seat for the entire course and schedule your certification exams with IABAC, the course fee must be paid in full. Your DataMites® relationship manager can help you with part payment agreements if you have any special limitations.
Using your particular certification number, you can verify all certificates at DataMites®.com. A different option is to email care@DataMites®.com.
Of course, you must maximize your training sessions. If you require any additional clarity, you can of course request a support session.
We accept payments through the;
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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