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 created through the process of data engineering. It is a broad field with applications in practically every sector.
The first and most crucial step in becoming a data engineer is to receive the necessary training. To upskill one's skills and gain a complete understanding of the data science and data engineering domain, one must enroll in a certification program.
You can learn more about how to become a data engineer by enrolling in courses that can range from three to twelve months. Contrarily, depending on the degree or certification sought after, the course program differs. Three-month courses can give you crucial Data Engineer experience and internship opportunities, which can lead to entry-level jobs at prestigious companies.
If you want to work in the industry, the Data Engineer Course is the one to enroll in since it accredits you as a data science specialist. After completing our extensive program, you'll possess the abilities required to be a successful data engineer as well as a portfolio that is ready for use in job interviews.
Entry into this field requires a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a closely related field. You'll need practical experience, like an internship, to even be considered for the majority of entry-level positions.
Depending on the level and kind of training you select, Data Engineer Training Fees in Trivandrum can range anywhere between 20,000 INR and 80,000 INR in India.
DataMites® is the greatest institute for complete training in courses in data engineering, data science, artificial intelligence, and other related topics. DataMites® develops and makes available a comprehensive crafter training program in partnership with eminent data engineering experts.
Data engineering is not always an entry-level position. Instead, a lot of data engineers begin their careers as software engineers or business intelligence analysts. You might transition into administrative positions as your career progresses, or you might work as a machine learning engineer, data architect, or solutions architect.
Coding, data warehousing, database management, data analysis, critical thinking, comprehension of machine learning, and other abilities are among the fundamental data engineering skills.
Data scientists evaluate the data to identify trends, gain business insights, and provide answers to issues that are important 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 diverse sources, small-scale ETL, API interaction, and automation. There are several reasons why Python is well-liked. Its accessibility is one of the main benefits.
Overall, a career as a data engineer is a great fit for those who value accuracy, adherence to engineering standards, and the development of pipelines that turn raw data into actionable insights. Data engineering careers have excellent income potential and stable employment.
A profession as a data engineer is stable, physically demanding, and financially rewarding. Every firm needs a data engineer to help it realize the full potential of its data. It is one of the professions with the fastest global growth rates, with an over 88.3% rise in job posts in 2019 and over 50% growth in the number of vacant positions.
Before submitting a full-time data engineer job application, it's a good idea to start with an internship. Internships are essential for getting experience and increasing practical knowledge prior to full-time employment since data engineering takes practise. People who have never worked previously are more likely to receive internship offers from businesses. After completing an internship, it will be considerably simpler for you to land an entry-level position with the company.
In the hierarchy of data science requirements, it's also a crucial step because, without the architecture created by data engineers, analysts and scientists won't be able to access or work with data. And as a result, businesses run the danger of losing access to one of their most priceless assets. 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 with the biggest growth in 2019.
The difficult task for data engineers is to strike a compromise between immediate needs and a longer-term perspective of where data demands will take the systems they oversee. With each new architecture you create, there is a persistent worry that you will reach a technical impasse. Data is certainly essential for expanding your organization and learning useful information. Despite being difficult to understand, a data engineering course can be useful for gaining the necessary domain knowledge.
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 over 50% in 2020. A recent survey found that demand for jobs in data engineering has significantly increased. Scalable solutions will be developed using your programming and problem-solving 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 Trivandrum is 6,85,000 LPA. (Indeed)
The DataMites® Data Engineer Courses in Trivandrum are specifically designed to educate data engineering from scratch. Anyone can now enroll in the course. This career path is for those looking for a change in career, data professionals looking to broaden their skill set for the next promotion, and college students looking for employment.
There is a tonne of space for improvement in the data engineering field in terms of capacity, remuneration, and learning. Aspirants can enroll in the DataMites Data Engineer Course Online in Trivandrum, where we offer comprehensive instruction for their future job.
The Data Engineer Course in Trivandrum lasts for three months and includes 120 hours of instruction. Weekdays and weekends are both used for training sessions. You can select any option based on your availability.
No, a graduate degree is not required, however, it can be very helpful to have prior knowledge of mathematics, statistics, economics, or computer science.
The price of a data engineering course online in Trivandrum is 42,000 INR, but thanks to a current discount, you may enroll for just 31,395 INR.
Yes, DataMites® offers Data Engineer Classroom Courses in the Indian states of Bangalore, Chennai, Pune, Hyderabad, and Kochi. Depending on the demand of the applicants and the availability of additional candidates from the precise place, we would be happy to host one in another location.
We are adamant about giving you access to certified, highly skilled trainers with years of experience in the field and a solid understanding of the material.
We provide a variety of flexible learning choices, including live online training, self-paced courses, and classroom instruction. You can make a decision based on your schedule.
We will grant you IABAC®, NASSCOM Future Skills, and JAINx certificates, which offer widespread acknowledgment of necessary skills.
For three months, you will be able to attend sessions from DataMites® relating to any query or revision you wish to clear thanks to our Flexi-Pass for Data Engineer training.
The results are immediately accessible if you take the exam online at exam.iabac.org. IABAC recommendations state that e-certificate issuance takes 7 to 10 business days.
Of course, we will give you a Data Engineer Course Completion Certificate once your course is over.
Yes. For the purpose of awarding the participation certificate and scheduling the certification exam as necessary, photo ID proofs such as a national ID card, driver's license, etc.
Yes, you will be given a free sample class to provide you with a quick overview of the training's procedures and contents.
To reserve your seat for the entire course and to 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 constraints.
Using your specific certification number, you can verify all certificates at DataMites®.com. Alternatively, you can email care@DataMites®.com.
You must, of course, maximize your training sessions. Of course, if you require any additional clarification, you can request a support session.
We take payments via;
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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