Data Science
Data science is the study that uses a quantitative approach, procedures, algorithms, and algorithms to extract information and expertise through noisy, unstructured, and large datasets, as well as to apply that knowledge and actionable insights to a wide variety of application areas.
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About Data Science
Data scientists look into which problems need to be addressed and where the relevant information may be found. They're business-savvy and analytical, with the ability to gather, clean, and display data. Data scientists help companies find, organize, and analyze massive volumes of information. Data Science careers can be more difficult to master than other fields in technologies because of the highly technical specifications. Getting a strong grasp on such a diverse range of languages and implementations is a steep learning curve. Certain engineering degree program, including mechanical engineering, computer engineering, or industrial engineering, will qualify you for data science classes by providing you with essential analytical abilities. Anyone could be wholly unique to the subject of data science and have no idea what you're doing, but with the right data science training in India and hands-on practice, you may start your path to being a data scientist. Candidates interested in pursuing a career as a data scientist should look for domain-specific courses.
Several statisticians, like Nate Silver, claim that machine learning is simply a new moniker for statistics. Others say that data science is separate from statistics since it focuses on digital data issues and approaches. According to Vasant Dhar, statistics places a high value on numeric values and interpretation. Data science, on the other hand, is concerned with both quantitative and qualitative data (such as pictures) and focuses on prediction and action. Statistics, according to Columbia University's Andrew Gelman, are an optional element of data science. Data science and statistics are not distinguishable by the scale of datasets or the use of computation, according to Stanford professor David Donoho, and many graduate programs falsely tout analytics and figures training as the heart of a data science degree.
How can I become a data scientist?
Data science courses are amongst the most constantly rising careers in the twenty-first century. Big Data actually solutions to crucial issues in every business, from enterprises to nonprofits to state agencies. There is an almost endless number of data that can be processed, analyzed, and used for a range of reasons.
How can an organization go through purchase data to develop a marketing strategy? How can government agencies leverage behavior responses to produce fun community activities? How can a non-profit make the most of its marketing money to improve overall operations? It ultimately boils down to data scientists' efforts. Data scientists are qualified to collect, arrange, and analyze data, and they help clients from many walks of life and companies. A data scientist is fundamentally anyone who collects and analyses data to achieve a conclusion. Scientists use several techniques to do all this.
They may provide the data in a visual context, or computer vision, noting evident data trends that could otherwise go unnoticed if the data is simply given in hard numbers on a spreadsheet. Data scientists suggest that this trend efficient algorithms are used to look for patterns, extract information from a cacophony of numbers and statistics, and gather reports that are beneficial for a business or organization. Data science is, beyond the most basic level, the way of detecting insight in huge quantities of data. Let's take a gander at a realistic scenario with a data scientist. Perhaps a mobile telephone provider wants to understand which of its existing customers are most likely to move to a vendor's service. The business may pay a professional to review large amounts of data (or, more specifically, design an algorithm to examine millions of data points) relating to previous clients. Consumers who consume a specific amount of bandwidth are more likely to depart, or customers who are married and between the ages of 35 and 45 are more inclined to migrate carriers, but according to this data analyst (or scientist). The mobile phone provider might then alter their business strategy or marketing activities to engage and attract customers.
Machine Learning Course in India is not to be misunderstood with statistics. Even though these two fields have comparable abilities and aims (such as evaluating massive amounts of data to derive conclusions), they are distinct in one important way. Machine Learning Training in India is a relatively young subject that mainly relies on computers and technology. It retrieves data from enormous databases, manipulate data with code, and displays numbers in something like a digital manner. Data Science Course is primarily concerned with hypothesis testing and utilizing existing ideas. It's a more conventional subject that hasn't altered much in the previous 100 years or so, whereas computer science has largely grown in tandem with the rise of computer use.
By becoming a data scientist, consider the following three steps:
Acquire a bachelor of science degree in engineering, computer science, economics, business, or a related area;
A master's degree in data science or a similar discipline is required.
Acquire knowledge in the field in which you would like to work (ex: healthcare, physics, business).
Aspiring data scientists or having done the Machine Learning Classes in India would have to have excellent organizational skills. As before said, there are millions of different data points, therefore assuring that data is structured in a meaningful way is critical. Although data science may be tedious at times, a healthy dose of tenacity is a desirable trait. When things become difficult and it appears that there isn't a solution, a competent data scientist will keep restructuring, reanalyzing, and manipulating the data in the anticipation of a "Eureka!" breakthrough. There are several avenues to a career in data science, but it is practically hard to get into the discipline without a college background. A four-year bachelor's degree is required for data scientists. Keep in mind, however, that 79 percent of industry professionals have a master's degree and 38 percent have a doctorate. If you want to work in a senior leadership position, you'll need to get a master's or doctoral degree.
Data science diplomas are available at several colleges, which is an obvious choice. Data science degrees will teach you how to process and evaluate a large amount of data and will include humans have created such as statistics, computers, and efficient knowledge management. The majority of information Bachelors frequently require on-the-job training before they should get started in their jobs. Data Science Classes in India is frequently focused on a company's unique programs and internal systems. Advanced analytics approaches that aren't taught in college may be included. Since this area of data science is continuing to evolve, it is critical to continue your learning while working in this profession. Data scientists continue to educate themselves over their careers to even be on the cutting edge of internet communication knowledge.
Best Data science Course in India
Cloud Computing, Algorithms, and Data Scientists Modeling are the three core components of something like the Data Science curricula. Statistics, Cryptography, Business Analytics, Data Stores, Mathematics, Machine Learning, and Algorithms are among the primary topics addressed in the Data Science course. If you're new to data science courses, there have been many online introduction courses you may take to get a better understanding of fundamentals. Below is a brief overview of the Machine Learning program for beginners:
Data Science: An Overview
Exploratory Data Analysis: An Overview
Artificial Intelligence (AI)
Model selection and assessment
Data warehousing is a term that refers to the process of storing
Data Exploration
Visualization of Data
Big Data is a type of computing
Insight into the business world
Data-driven storytelling
Presentation and Communication
The Data Science Course is meant to assist students in gaining business knowledge as well as utilizing tools and statistics to tackle organizational difficulties shortly. As a result, the skills learned throughout the Data Science and Machine Learning courses are critical to becoming a valuable asset in the field of Data Science. Whether you're searching for a Data Science Syllabus for beginners or specialists, we've put up a generic Data Science syllabus for you. The three most significant components of Data Science Classes that most colleges follow to assist you to adapt to both the academic and practical elements of the topic are as follows:
Data abounds ( Big Data)
Artificial Intelligence (AI)
Artificial Intelligence & Business Acumen
In data science, modeling is important.
Online Classes
Online Data Science classes in India can be easily done in any institute which provides the course. Online courses are many in different quality Training centers. One of such quality training centers is at Pune, the SevenMentor training institute. Our organization relates to all types of technical courses such as networking and software. Students can get trained here with the best technical knowledge and implementation using it. Lab exams help students learn all the courses practically. Our trainers focus on the skills which have to be trained. Trainers multiply the skills of students and encourage them to lead to success. SevenMentor is the best-suggested training institute for fresh graduates who want to improve their skills.
Course Eligibility
- Freshers
- BE/ Bsc Candidate
- Any Engineers
- Any Graduate
- Any Post-Graduate
- Working Professionals
Syllabus of Data Science
- 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 Science
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 Science 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 all new aspects of Data Science.
- You will have a good understanding of Data Science Algorithms.
- You will be able to work on real time projects.
- You will be able to work on file formats on different data.
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.
Batch Schedule
DATE | COURSE | TRAINING TYPE | BATCH | CITY | REGISTER |
---|---|---|---|---|---|
21/10/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | India | Book Now |
15/10/2024 |
Data Science |
Online | Regular Batch (Mon-Sat) | India | Book Now |
19/10/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | India | Book Now |
19/10/2024 |
Data Science |
Online | Weekend Batch (Sat-Sun) | India | Book Now |
Students Reviews
A fantastic learning opportunity! The way you explained things, moving from one topic to the next while linking them, and putting the principles into real-world circumstances were all excellent.
- Ketki Dhamale
The lessons were excellent and quite beneficial. It was a fantastic and revitalising experience.
- Karan Thapa
Despite the fact that the material was dry, the faculty made it engaging. They addressed the audience well, and it was a highly engaging and educational discussion.
- Jyotsna Pawar
Course video & Images
Corporate Training
Professionals and employees can get trained in many training centers. According to the job requirement employees will tend to learn more technical topics. Corporate Data science training in India is an important part of an employee's career. Being trained in an experienced training center can help professionals develop their skill set. SevenMentor training center trains students as well as employees. Training is given according to the time employees will be available. The exams and tests are taken help the professional to become involved in the subject and gain good results. Data science has many sub-topics. Machine learning is part of a Data Science course.
Our Placement Process
Eligibility Criteria
Placements Training
Interview Q & A
Resume Preparation
Aptitude Test
Mock Interviews
Scheduling Interviews
Job Placement
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