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: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA SCIENCE
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures: Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: R LANGUAGE ESSENTIALS
• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures, control statements
MODULE 6: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T-Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 7: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic 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: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create a new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5:SQL JOINS
• Inner join
• Outer Join
• Left join
• Right join
• Cross join
• Self join
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management
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: 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
• Bitbucket Git account
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
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU : BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
MODULE 1: AWS DATA SERVICES INTRODUCTION
• AWS Overview and Account Setup
• AWS IAM Users, Roles and Policies
• AWS Lamdba overview
• AWS Glue overview
• AWS Kinesis overview
• AWS Dynamodb overview
• AWS Anthena overview
• AWS Redshift overview
MODULE 2: DATA INGESTION USING AWS LAMDBA
• Setup AWS Lamdba local development env
• Deploy project to Lamdba console
• Data pipeline setup with Lamdba
• Validating data files incrementally
• Deploying Lamdba function
MODULE 3: DATA PREPARATION WITH AWS GLUE
• AWS Glue Components
• Spark with Glue jobs
• AWS Glue Catalog and Glue Job APIs
• AWS Glue Job Bookmarks
MODULE 4: SPARK APP USING AWS EMR
• PySpark Introduction
• AWS EMR Overview and setup
• Deploying Spark app using AWS EMR
MODULE 5: DATA PIPELINE WITH AWS KINESIS
• AWS Kinesis overview and setup
• Data Streams with AWS Kinesis
• Data Ingesting from AWS S3 using AWS Kinesis
MODULE 6: 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 7: DATA ENGINEERING PROJECT
• Hands-on Project Case-study
• Setup Project Development Env
• Organization of Data Sources
• Setup AWS services for Data Ingestion
• Data Extraction Transformation with AWS
• Data Streams with AWS Kinesis
MODULE 1: AZURE DATA SERVICES INTRODUCTION
• Azure Overview and Account Setup
• Azure Storage
• Azure Data Lake
• Azure Cosmos DB
• Azure SQL Database
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure HDInsight
• Azure Data Services
MODULE 2: STORAGE IN AZURE
• Create Azure storage account
• Connect App to Azure Storage
• Azure Blog Storage
MODULE 3: AZURE DATA FACTORY
• Azure Data Factory Introduction
• Data transformation with Data Factory
• Data Wrangling with Data Factory
MODULE 4: 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 5: DATA ENGINEERING PROJECT WITH AZURE
• Hands-on Project Case-study
• Setup Project Development Env
• Organization of Data Sources
• Setup AZURE services for Data Ingestion
• Data Extraction Transformation with Azure Data Factory and Azure Synapse
Data engineering is the backbone of data science, providing the infrastructure and tools needed to manage and process vast amounts of data. Without data engineering, data scientists and analysts would struggle to access and analyze the data essential for making informed decisions and gaining valuable insights. As organizations increasingly rely on data to drive their strategies, data engineering remains a critical field that empowers businesses to harness the power of data for success.
A data engineer course curriculum typically includes data modeling, database design, data integration, ETL (Extract, Transform, Load) processes, data warehousing, data pipelines, distributed computing, cloud technologies, and big data frameworks. Students acquire skills to design and construct data systems that efficiently handle large data volumes for analysis and decision-making purposes.
The curriculum of a data engineer course usually covers topics such as database systems, data modeling, data integration, data pipelines, cloud computing, big data technologies, data warehousing, and data quality.
Yes, data engineering is considered an IT-related profession as it involves the use of technology, programming, and software tools to manage and process data.
To pursue a career as a data engineer in Siliguri, you can start by obtaining a relevant degree or certification in data engineering or a related field. You can also gain practical experience through internships or projects, develop your skills in programming and database management, and stay updated with the latest trends and technologies in the field.
The prerequisites for enrolling in a Data Engineer Course in Siliguri may vary depending on the training provider. However, a background in computer science, data analysis, or a related field, along with basic programming and database knowledge, is typically beneficial.
After completing Data Engineer Training, you can explore job opportunities such as Data Engineer, Data Analyst, Database Administrator, ETL Developer, Data Architect, or Big Data Engineer in various industries such as technology, finance, healthcare, e-commerce, and more.
The essential skills needed for a successful data engineer include programming skills (Python, SQL), data modeling and database knowledge, ETL (Extract, Transform, Load) processes expertise, familiarity with big data technologies, proficiency in cloud platforms, data pipeline and workflow tools, data warehousing understanding, problem-solving and analytical skills, communication and collaboration abilities, and a mindset of continuous learning.
Python is widely used in data engineering for tasks such as data manipulation, data cleaning, scripting, and building data pipelines. Its simplicity, versatility, and rich ecosystem of libraries make it a popular choice among data engineers.
Pursuing a career in Data Engineering can be a promising choice, given the increasing demand for professionals who can manage and analyze large volumes of data. It offers excellent growth opportunities, competitive salaries, and the chance to work with cutting-edge technologies. However, it is important to assess your interests, skills, and career goals to determine if data engineering aligns with your aspirations.
If you're looking to acquire data engineering training in Siliguri, DataMites is the top choice. With their comprehensive courses, industry-relevant curriculum, and experienced instructors, they provide the ideal platform to enhance your skills and knowledge in data engineering. Their training programs are designed to equip you with the expertise needed to excel in this dynamic field.
The DataMites Certified Data Engineer Training program in Siliguri covers a comprehensive curriculum that includes:
Introduction to data engineering and its role in the industry.
Data extraction, transformation, and loading (ETL) techniques.
Database management systems and data modeling concepts.
Big data processing frameworks like Hadoop and Spark.
Data warehousing and business intelligence concepts.
These topics provide a solid foundation in data engineering principles and techniques, preparing participants for real-world data engineering challenges.
For the DataMites Data Engineer Course in Siliguri, the duration can be customized based on the learning mode. Online instructor-led training usually spans 6 months and encompasses over 150 learning hours.
Opting for online data engineer training from DataMites® offers several advantages:
Flexibility: Online training allows you to learn at your own pace and convenience, fitting into your schedule without the need to commute to a physical location.
Accessibility: With online training, you can access course materials and resources from anywhere, as long as you have an internet connection. This enables learning from the comfort of your home or any other preferred location.
Interactive Learning: Online training often includes interactive features such as live instructor-led sessions, virtual labs, and discussion forums, fostering engagement and collaboration with instructors and fellow learners.
Updated Content: Online training providers like DataMites® frequently update their course content to keep up with the latest industry trends and advancements, ensuring that you gain knowledge and skills relevant to the current data engineering landscape.
Cost-Effective: Online training typically has lower costs compared to in-person training, as it eliminates expenses related to travel, accommodation, and physical classroom facilities. It allows you to access quality training at a more affordable price point.
Continuous Support: Online data engineer training from DataMites® often includes dedicated support channels, such as online chat or email, where you can seek assistance from instructors and mentors throughout your learning journey.
The Data Engineer Course at DataMites® in Siliguri is open to individuals with a background in computer science, IT, engineering, mathematics, or related fields.
Professionals working in the field of data analysis, database management, or data processing can also participate in the Data Engineer Course at DataMites® in Siliguri to enhance their skills and expand their career opportunities.
Graduates or postgraduates looking to enter the data engineering field and acquire the necessary skills can enroll in the Data Engineer Course at DataMites® in Siliguri.
Individuals with a keen interest in data engineering and a willingness to learn and explore the field are eligible to participate in the Data Engineer Course at DataMites® in Siliguri.
Data engineering enthusiasts who want to gain a comprehensive understanding of data management, ETL (Extract, Transform, Load) processes, data integration, and related topics can join the Data Engineer Course at DataMites® in Siliguri.
DataMites provides cost-effective Data Engineer Training in Siliguri, with course fees ranging from INR 26,548 to INR 68,000, making it accessible to aspiring data engineers with varying financial capacities.
In Siliguri, DataMites® facilitates ON DEMAND classroom-based Data Engineer training, enabling students to learn in a physical classroom environment under the guidance of qualified instructors.
With the Flexi-Pass from DataMites®, learners have the flexibility to create their own learning schedule by selecting courses based on their preferences and availability. It offers convenience and freedom in learning.
Yes, upon successfully completing the Data Engineer training program from DataMites®, you will receive certifications from renowned organizations such as the International Association of Business Analytics Certifications (IABAC), Jain (Deemed-to-be University), and NASSCOM FutureSkills Prime. These certifications validate your skills and knowledge in the field of data engineering, enhancing your credibility and career prospects.
At DataMites®, the Data Engineer Course in Siliguri is led by a seasoned instructor who possesses extensive industry experience and domain expertise in data engineering. They are dedicated to delivering comprehensive and practical training to equip you with the skills required in the field.
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.