Data Analytics
The process of analyzing, cleaning, manipulating, and analyzing data to identify useful information, influencing conclusions, and assisting judgment is known as data analysis. Data analysis has several dimensions and methodologies, including a wide range of techniques under many titles and being applied in several businesses, scientific, and social science sectors.
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About Data Analytics
Data analysis is important in today's business environment since it helps firms make more scientific choices and run more efficiently. The definition of data analytics covers the field's vast reach as the act of analyzing raw data to uncover trends and respond to questions. It however does comprise a variety of strategies and multiple objectives. Certain elements of the data analytics process can aid a range of endeavors. A good data analytics program will present a clear vision of where you are, where you have been, and where you should go by merging these elements. So in short data analytics brings out hidden insights of the data to give a clear view and decision-making capabilities by looking at the data presented.
What are the career opportunities for Data Analysts?
In 2020, the demand for new generation tech workers would be 4.4 lakhs, while the supply will be 2.4 lakhs. Companies have acknowledged the need for a data-driven business approach in examining the stream of acquired data and providing useful insight. As a result, as our ecosystem continues to digitize, the demand for smart applicants with data capabilities will only increase in leaps and bounds. Over 6% of all data analytics job postings in the globe come from India. There are roughly 97000 data analytics job openings in India right now, with 24 percent of them centered around Bengaluru. In India's data analytics sector, Delhi and Mumbai are also developing magnets for job seekers. 70% of Data analytics job postings were for junior-level roles, while 30% were for senior-level positions.
SevenMentor Data Analytic Course in Mumbai
SevenMentor Institute has partnered with international organizations, helping students to see themselves in a larger context. The Data Analyst Classes in Mumbai are led by experts in the field with years of expertise. Storage arrays and processing with Hadoop, Spark, and HDFS are discussed in great detail. Information is retrieved using Apache Hadoop, and it is subsequently analyzed using Apache Spark. Data Preprocessing, Information Extraction, and Experimental Data Analysis are all discussed in depth. Real-life examples are used to demonstrate data mining of organized (RDBMS) and unorganized (Big Data) data using Python and R programming. The essential ideas of Machine Learning and creating Machine Learning Algorithms for Prediction Modeling using Linear Regression are covered in a module. In the top Data Analytics training in Mumbai, several black-box techniques such as Neural Networks and SVM are demonstrated, as well as the usefulness of Data Visualization approaches to convey findings.
Data Analytics Courses in Mumbai are tailored to fit a student's requirements and degree of competence because we enroll both freshmen and working professionals. Students get extensive topic knowledge as well as hands-on experience to perform according to global standards. Learners get a great combination of soft skills and hard skills that the profile requires through projects, case studies, videos, assignments, and live sessions.
Data Analytics Trends
Over the last ten years, the fast adoption of digital technology has altered the terrain of our universe. In 1995, the digital universe was estimated to be 140 billion gigabytes. By 2020, the figure will have risen to 50 trillion gigabytes. To handle this digital environment, millions of new workers will be required, and corporations will compete for hundreds of thousands of people. India is now ranked in the top ten nations in terms of Big Data Analytics, with roughly 600 companies. Throughout India, the Big Data Analytics market size was valued at $ 2 billion and is expected to grow to $ 16 billion by 2025.
Certification Course
This certificate of completion for the Data Analytics course validates your expertise in data analytics, machine learning, and data visualization. The Data Analytics Certificate from SevenMentor is evidence of your hard work and dedication. Use this certification to set yourself apart from your peers and superiors. This degree from the finest data analytics training college in Mumbai can help you advance in your profession. IIT Madras professors and industry professionals established our Data Analytics course in Mumbai in collaboration with leading IT company experts. This training will assist you in obtaining employment with the greatest companies. This Data Analytics certificate course in Mumbai also includes real-time projects and case analysis, which are quite useful in the workplace. So come and get yourself the best certificate course in Data Analytics in Mumbai and headstart your career in the most happening sector of the IT industry.
Data Analytics Course by SevenMentor
In this data analytics course, you'll learn about Hadoop Distributed File System (HDFS), MapReduce, YARN, and the fundamentals of the Linux operating system. This course will teach you how to process and analyze huge data sets stored in HDFS using Pig, Hive, Python, and Scala. Understand SQOOP, which is used to migrate data from RDBMS to Big Data sets. The Machine Learning Training with Python and R programming module covers an introduction of analytical approaches for manipulating large amounts of data and extracting useful business insights from it. In this course, you'll learn how to do regression analysis and develop prediction models with Python and R. This is without a doubt one of Mumbai's top data analytics training.
Online Classes
The Online Data Analytics course in Mumbai provides you with an overview of the numerous approaches that make data analytics on large datasets possible. Students will understand how data analysis technologies may be used to create upgradable systems for processing and handling massive quantities of information. The online curriculum teaches students how to evaluate unstructured data, as well as how to develop sophisticated prediction models using machine learning algorithms and data visualization. The Online Data Analytics course is designed for individuals who wish to have a thorough understanding of Big Data frameworks. The HDFS, YARN, and MapReduce, which are the foundations of Linux OS, will be covered in online Data Analytics classes in Mumbai. Students will learn how to process and analyze huge data sets stored in HDFS as well as how to migrate data from unorganized data systems through the use of Scoop. Neural Networks and SVM approaches will also be covered in our online Data Analysis Training in Mumbai. The training is jam-packed with real-world case studies that will help participants solve complicated business challenges and boost profits in their businesses. As a result, you can enroll in India's top online data analytics course.
Course Eligibility
- Freshers
- BE/ Bsc Candidate
- Any Engineers
- Any Graduate
- Any Post-Graduate
- Working Professionals
Syllabus of Data Analytics
- 1. Installation Of Vmware
- 2. MYSQL Database
- 3. Core Java
- 1.1 Types of Variable
- 1.2 Types of Datatype
- 1.3 Types of Modifiers
- 1.4 Types of constructors
- 1.5 Introduction to OOPS concept
- 1.6 Types of OOPS concept
- 4. Advance Java
- 1.1 Introduction to Java Server Pages
- 1.2 Introduction to Servlet
- 1.3 Introduction to Java Database Connectivity
- 1.4 How to create Login Page
- 1.5 How to create Register Page
- 5. Bigdata
- 1.1 Introduction to Big Data
- 1.2 Characteristics of Big Data
- 1.3 Big data examples
- 6. Hadoop
- i) BigData Inroduction,Hadoop Introduction and HDFS Introduction
- 1.1. Hadoop Architecture
- 1.2. Installing Ubuntu with Java on VM Workstation 11
- 1.3. Hadoop Versioning and Configuration
- 1.4. Single Node Hadoop installation on Ubuntu
- 1.5. Multi Node Hadoop installation on Ubuntu
- 1.6. Hadoop commands
- Cluster architecture and block placement
- 1.8. Modes in Hadoop
- Local Mode
- Pseudo Distributed Mode
- Fully Distributed Mode
- 1.9. Hadoop components
- Master components(Name Node, Secondary Name Node, Job Tracker)
- Slave components(Job tracker, Task tracker)
- 1.10. Task Instance
- 1.11. Hadoop HDFS Commands
- 1.12. HDFS Access
- Java Approach
- ii) MapReduce Introduction
- 1.1 Understanding Map Reduce Framework
- 1.2 What is MapReduceBase?
- 1.3 Mapper Class and its Methods
- 1.4 What is Partitioner and types
- 1.5 Relationship between Input Splits and HDFS Blocks
- 1.6 MapReduce: Combiner & Partitioner
- 1.7 Hadoop specific Data types
- 1.8 Working on Unstructured Data Analytics
- 1.9 Types of Mappers and Reducers
- 1.10 WordCount Example
- 1.11 Developing Map-Reduce Program using Eclipse
- 1.12 Analysing dataset using Map-Reduce
- 11.13 Running Map-Reduce in Local Mode.
- 1.14 MapReduce Internals -1 (In Detail) :
- How MapReduce Works
- Anatomy of MapReduce Job (MR-1)
- Submission & Initialization of MapReduce Job (What Happen ?)
- Assigning & Execution of Tasks
- Monitoring & Progress of MapReduce Job
- Completion of Job
- Handling of MapReduce Job
- Task Failure
- TaskTracker Failure
- JobTracker Failure
- 1.15 Advanced Topic for MapReduce (Performance and Optimization) :
- Job Sceduling
- In Depth Shuffle and Sorting
- 1.16 Speculative Execution
- 1.17 Output Committers
- 1.18 JVM Reuse in MR1
- 1.19 Configuration and Performance Tuning
- 1.20 Advanced MapReduce Algorithm :
- 1.21 File Based Data Structure
- Sequence File
- MapFile
- 1.22 Default Sorting In MapReduce
- Data Filtering (Map-only jobs)
- Partial Sorting
- 1.23 Data Lookup Stratgies
- In MapFiles
- 1.24 Sorting Algorithm
- Total Sort (Globally Sorted Data)
- InputSampler
- Secondary Sort
- 1.25 MapReduce DataTypes and Formats :
- 1.26 Serialization In Hadoop
- 1.27 Hadoop Writable and Comparable
- 1.28 Hadoop RawComparator and Custom Writable
- 1.29 MapReduce Types and Formats
- 1.30 Understand Difference Between Block and InputSplit
- 1.31 Role of RecordReader
- 1.32 FileInputFormat
- 1.33 ComineFileInputFormat and Processing whole file Single Mapper
- 1.34 Each input File as a record
- 1.35 Text/KeyValue/NLine InputFormat
- 1.36 BinaryInput processing
- 1.37 MultipleInputs Format
- 1.38 DatabaseInput and Output
- 1.39 Text/Biinary/Multiple/Lazy OutputFormat MapReduce Types
- iii)TOOLS:
- 1.1 Apache Sqoop
- Sqoop Tutorial
- How does Sqoop Work
- Sqoop JDBCDriver and Connectors
- Sqoop Importing Data
- Various Options to Import Data
- Table Import
- Binary Data Import
- SpeedUp the Import
- Filtering Import
- Full DataBase Import Introduction to Sqoope
- 1.2 Apache Hive
- 1.2 Apache Hive
- What is Hive ?
- Architecture of Hive
- Hive Services
- Hive Clients
- How Hive Differs from Traditional RDBMS
- Introduction to HiveQL
- Data Types and File Formats in Hive
- File Encoding
- Common problems while working with Hive
- Introduction to HiveQL
- Managed and External Tables
- Understand Storage Formats
- Querying Data
- 1.3 Apache Pig :
- What is Pig ?
- Introduction to Pig Data Flow Engine
- Pig and MapReduce in Detail
- When should Pig Used ?
- Pig and Hadoop Cluster
- Pig Interpreter and MapReduce
- Pig Relations and Data Types
- PigLatin Example in Detail
- Debugging and Generating Example in Apache Pig
- 1.4 HBase:
- Fundamentals of HBase
- Usage Scenerio of HBase
- Use of HBase in Search Engine
- HBase DataModel
- Table and Row
- Column Family and Column Qualifier
- Cell and its Versioning
- Regions and Region Server
- HBase Designing Tables
- HBase Data Coordinates
- Versions and HBase Operation
- Get/Scan
- Put
- Delete
- 1.5 Apache Flume:
- Flume Architecture
- Installation of Flume
- Apache Flume Dataflow
- Apache Flume Environment
- Fetching Twitter Data
- 1.6 Apache Kafka:
- Introduction to Kafka
- Cluster Architecture
- Installation of kafka
- Work Flow
- Basic Operations
- Real time application(Twitter)
- 4)HADOOP ADMIN:
- Introduction to Big Data and Hadoop
- Types Of Data
- Characteristics Of Big Data
- Hadoop And Traditional Rdbms
- Hadoop Core Services
- Hadoop single node cluster(HADOOP-1.2.1)
- Tools installation for hadoop1x.
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Analyze the cluster using
- a)NameNode UI
- b)JobTracker UI
- SettingUp Replication Factor
- Hadoop Distributed File System:
- Introduction to Hadoop Distributed File System
- Goals of HDFS
- HDFS Architecture
- Design of HDFS
- Hadoop Storage Mechanism
- Measures of Capacity Execution
- HDFS Commands
- The MapReduce Framework:
- Understanding MapReduce
- The Map and Reduce Phase
- WordCount in MapReduce
- Running MapReduce Job
- WordCount in MapReduce
- Running MapReduce Job
- Hadoop single node Cluster
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper
- Hadoop single node Cluster Setup :
- Hadoop single node cluster(HADOOP-2.7.3)
- Tools installation for hadoop2x
- Sqoop,Hive,Pig,Hbase,Zookeeper.
- Yarn:
- Introduction to YARN
- Need for YARN
- YARN Architecture
- YARN Installation and Configuration
- Hadoop Multinode cluster setup:
- hadoop multinode cluster
- Checking HDFS Status
- Breaking the cluster
- Copying Data Between Clusters
- Adding and Removing Cluster Node
- Name Node Metadata Backup
- Cluster Upgrading
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- >7. MONGODB
- 8. SCALA
- 1.1 Introduction to scala
- 1.2 Programming writing Modes i.e. Interactive Mode,Script Mode
- 1.3 Types of Variable
- 1.4 Types of Datatype
- 1.5 Function Declaration
- 1.6 OOPS concepts
- 9. APACHE SPARK
- 1.1 Introduction to Spark
- 1.2 Spark Installation
- 1.3 Spark Architecture
- 1.4 Spark SQL
- Dataframes: RDDs + Tables
- Dataframes and Spark SQL
- 1.5 Spark Streaming
- Introduction to streaming
- Implement stream processing in Spark using Dstreams
- Stateful transformations using sliding windows
- 1.6 Introduction to Machine Learning
- 1.7 Introduction to Graphx
- Hadoop ecosystem:
- Sqoop
- Hive
- Pig
- HBase
- zookeeper
- 10. TABLEAU
- 11. DATAIKU
- 12. Product Based Web Application Demo based on java(EcommerceApplication)
- 13. Data deduplication Project
- 14. PYTHON
- 1.Introduction to Python
- What is Python and history of Python?
- Unique features of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- First Python Program
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments
- Getting User Input
- Python Data Types
- What are variables?
- Python Core objects and Functions
- Number and Maths
- Week 1 Assignments
- 2.List, Ranges & Tuples in Python
- Introduction
- Lists in Python
- More About Lists
- Understanding Iterators
- Generators , Comprehensions and Lambda Expressions
- Introduction
- Generators and Yield
- Next and Ranges
- Understanding and using Ranges
- More About Ranges
- Ordered Sets with tuples
- 3.Python Dictionaries and Sets
- Introduction to the section
- Python Dictionaries
- More on Dictionaries
- Sets
- Python Sets Examples
- 4. Python built in function
- Python user defined functions
- Python packages functions
- Defining and calling Function
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
- 5.Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 6. Python Object Oriented
- Overview of OOP
- Creating Classes and Objects
- Accessing attributes
- Built-In Class Attributes
- Destroying Objects
- 7. Python Exceptions Handling
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
- 8. Python Regular Expressions
- What are regular expressions?
- The match Function
- The search Function
- Matching vs searching
- Search and Replace
- Extended Regular Expressions
- Wildcard
- 9. Python Multithreaded Programming
- What is multithreading?
- Starting a New Thread
- The Threading Module
- Synchronizing Threads
- Multithreaded Priority Queue
- Python Spreadsheet Interfaces
- Python XML interfaces
- 10. Using Databases in Python
- Python MySQL Database Access
- Install the MySQLdb and other Packages
- Create Database Connection
- CREATE, INSERT, READ, UPDATE and DELETE Operation
- DML and DDL Oepration with Databases
- Performing Transactions
- Handling Database Errors
- Web Scraping in Python
- 11.Python For Data Analysis –
- Numpy:
- Introduction to numpy
- Creating arrays
- Using arrays and Scalars
- Indexing Arrays
- Array Transposition
- Universal Array Function
- Array Processing
- Arrary Input and Output
- 12. Pandas:
- What is pandas?
- Where it is used?
- Series in pandas
- Index objects
- Reindex
- Drop Entry
- Selecting Entries
- Data Alignment
- Rank and Sort
- Summary Statics
- Missing Data
- Index Heirarchy
- 13. Matplotlib: Python For Data Visualization
- 14. Welcome to the Data Visualiztion Section
- 15. Introduction to Matplotlib
- 16. Django Web Framework in Python
- 17. Introduction to Django and Full Stack Web Development
- 15. R Programming
- 1.1 Introduction to R
- 1.2 Installation of R
- 1.3 Types of Datatype
- 1.4 Types of Variables
- 1.5 Types of Operators
- 1.6 Types of Loops
- 1.7 Function Declaration
- 1.8 R Data Interface
- 1.9 R Charts and Graphs
- 1.10 R statistics
- 16) Advance Tool for Analysis
- 1.1 git
- 1.2 nmpy
- 1.3 scipy
- 1.4 github
- 1.5 matplotlib
- 1.6 Pandas
- 1.7 PyQT
- 1.8Theano
- 1.9 Tkinter
- 1.10 Scikit-learn
- 1.11 NPL
- 17. Algorithm
- 1.naive bayes
- 2.Linear Regression
- 3.K-nn
- 4.C-nn
Trainer Profile of Data Analytics
Our Trainers explains concepts in very basic and easy to understand language, so the students can learn in a very effective way. We provide students, complete freedom to explore the subject. We teach you concepts based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates can learn in our one to one coaching sessions and are free to ask any questions at any time.
- Certified Professionals with more than 8+ Years of Experience
- Trained more than 2000+ students in a year
- Strong Theoretical & Practical Knowledge in their domains
- Expert level Subject Knowledge and fully up-to-date on real-world industry applications
Data Analytics Exams & Certification
SevenMentor Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.
Our certification at SevenMentor is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.
Proficiency After Training
- Learn the all aspects of Data Analytics
- proficient in HIVE, R, Scala, and SQL, or Structured Query Language
- Understand the ecosystem of Data Analytics
- Practicals on Pig Hive Hbase
- Practicals on commercial distributions
Key Features
Skill level
From Beginner to Expert
We are providing Training to the needs from Beginners level to Experts level.
Course Duration
12 weeks
Course will be 90 hrs to 110 hrs duration with real-time projects and covers both teaching and practical sessions.
Total Learner
2000+ Learners
We have already finished 100+ Batches with 100% course completion record.
Frequently Asked Questions
Batch Schedule
DATE | COURSE | TRAINING TYPE | BATCH | CITY | REGISTER |
---|---|---|---|---|---|
23/12/2024 |
Data Analytics |
Online | Regular Batch (Mon-Sat) | Mumbai | Book Now |
24/12/2024 |
Data Analytics |
Online | Regular Batch (Mon-Sat) | Mumbai | Book Now |
28/12/2024 |
Data Analytics |
Online | Weekend Batch (Sat-Sun) | Mumbai | Book Now |
28/12/2024 |
Data Analytics |
Online | Weekend Batch (Sat-Sun) | Mumbai | Book Now |
Students Reviews
SevenMentor is Mumbai's top training institute, with excellent instructors and classroom amenities. Major ideas from the Data Analysis course have been grasped, and I have gained confidence in myself.
- Virat Singh
Thank you for your kind and versatile training, SevenMentor. I am pleased to have finished the Data Analyst course and to have been offered a nice position at Sevenmentor. This has been a fantastic start to my professional career.
- Varun Patki
SevenMentor is one of the greatest IT training colleges in the world. I enrolled in their Data Analyst course and also was pleased with the knowledge I received. I will strongly advise all of you to attend this Data Analyst training in Mumbai. Thank you very much.
- Jatin Tiwathi
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Corporate Training
SevenMentor offers a team of trainers with years of expertise in teaching and working in the technology industry. The instructors are industry specialists with many years of operational experience and an extensive understanding of company development and the use of technology in business upgrades. We work with a variety of small and big businesses, providing IT, software, and hardware maintenance, server maintenance courses, and business development training regularly. For corporate personnel, SevenMentor provides specialized training as well as on-the-job training. It is a very quick and effective training that will undoubtedly assist firms in improving the knowledge and abilities of their staff. We also offer joint sessions with numerous firms, which helps to share information and skills across industries. For a variety of software-related training, our corporate clients have ranked us as the finest in the business. Many firms have highly recommended us for providing the best corporate Data Analytics course in Mumbai.
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