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 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND 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 : AWS DATA SERVICES INTRODUCTION
MODULE 2 : DATA INGESTION USING AWS LAMDBA
MODULE 3 : DATA PIPELINE WITH AWS KINESIS
MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT
MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE
MODULE 6 : STORAGE IN AZURE
MODULE 7: AZURE DATA FACTORY
MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS
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 : 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
• 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
Data engineering encompasses the design, development, and maintenance of systems and infrastructure to collect, process, store, and manage large volumes of data. It involves tasks such as data ingestion, data transformation, database management, and data pipeline creation.
The requirements for enrolling in a Data Engineer Course in Dispur may vary depending on the institute and program. Generally, a basic understanding of programming, databases, and data concepts is beneficial. Some courses may have prerequisites like knowledge of SQL, Python, or familiarity with data manipulation techniques.
The cost of Data Engineer Training in Dispur can vary depending on factors such as the institute, program duration, delivery mode (online or classroom), and additional features. Typically, the fees for data engineer training in Dispur range from 40,000 INR to INR 1,00,000.
The choice of the best institute depends on factors like the curriculum, faculty expertise, industry connections, alumni reviews, and training delivery modes. Datamites is considered one of the best institutes for data engineering training. With comprehensive curriculum, industry-relevant projects, and experienced instructors, the institute provides a strong foundation in data engineering concepts, tools, and techniques.
Data Science and Data Engineering are related fields but have distinct focuses. Data Science primarily deals with extracting insights and knowledge from data through statistical analysis and machine learning techniques. Data Engineering, on the other hand, focuses on building and maintaining the infrastructure and pipelines to process, store, and prepare data for analysis.
Data Engineering is a recommended career path for individuals interested in working with data and technology. The demand for skilled data engineers is growing rapidly as organizations increasingly rely on data-driven decision-making. It offers diverse opportunities, competitive salaries, and the chance to work on cutting-edge technologies.
The field of Data Engineering has promising prospects. With the exponential growth of data and the increasing need for efficient data processing and analysis, data engineers play a crucial role in organizations across various industries. There is a high demand for professionals who can design and manage data infrastructure and pipelines.
While prior experience can be advantageous, individuals with no prior experience can still secure entry-level Data Engineer job positions. Starting as a junior data engineer or intern and gradually gaining experience and skills through practical projects and on-the-job learning can pave the way for career growth in the field.
Python is widely used in the context of Data Engineering due to its versatility, extensive libraries, and ease of use. It is commonly used for data manipulation, transformation, and scripting tasks. Python frameworks like Apache Spark and libraries like Pandas provide powerful tools for data processing, making it a valuable language for data engineers.
To establish a career as a data engineer in Dispur, you can follow these steps: gain a strong foundation in programming languages like Python and SQL, develop skills in data manipulation and database management, familiarize yourself with big data technologies like Hadoop and Spark, and consider pursuing relevant certifications or joining data engineering training programs to enhance your knowledge and increase your chances of securing job opportunities in the field. Networking and gaining practical experience through internships or projects can also be beneficial.
To acquire training in data engineering in Dispur, you can enroll in reputable institutes like DataMites®, which offer comprehensive data engineering courses through online or classroom modes. They provide hands-on training, practical projects, and expert guidance to develop your skills in data engineering.
The DataMites Certified Data Engineer Training in Dispur covers a wide range of topics including data engineering concepts, tools, and technologies such as Hadoop, Spark, and SQL. The training program includes interactive sessions, practical assignments, and real-world case studies to enhance your understanding and proficiency in data engineering.
Eligibility criteria for enrolling in the Data Engineer Course in Dispur at DataMites® may vary. Generally, individuals with a background in computer science, mathematics, or related fields, as well as professionals aspiring to work in data engineering roles, are eligible to enroll.
The duration of the DataMites Data Engineer Course in Dispur can vary depending on the chosen learning mode. Generally, for online instructor-led training, it is typically around 6 months with 150+ learning hours, while the duration may differ for self-paced learning options.
The data engineer course fee at DataMites in Dispur can vary from around INR 26,548 to INR 68,000.
The duration to become certified by the IABAC (International Association of Business Analytics Certifications) can vary depending on the specific certification and the individual's preparation. It typically requires rigorous study, practical experience, and successfully passing the certification examination.
Yes, upon successful completion of the Data Engineer Course from DataMites®, you will receive a Data Engineer Course Completion Certificate. This certificate acknowledges your successful completion of the course and serves as evidence of your proficiency in data engineering concepts and techniques.
The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to carry a valid ID proof, such as a government-issued ID card, and any specific documents mentioned in the communication received from DataMites.
DataMites® accepts various payment methods for their training programs, including online payment through credit cards, debit cards, net banking, and other digital payment options.
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.