Data Science Classes in Trimurti Nagar

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Learning Curve for Data Science

Learning curve for Data Science

Master In Data Science Course

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Empower your career with in-demand data skills and open doors to top-tier opportunities.

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Deep Learning Engineer
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Business Intelligence Analyst
Computer Vision Engineer
NLP Engineer
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Skills & Tools You'll Learn -

Data Visualization  iconData Visualization Learn to create compelling charts and graphs to uncover insights effectively.
Python  iconPython A versatile programming language widely used for data analysis and modeling.
SQL  iconSQL A database language for querying and managing structured data.
Pandas  iconPandas A Python library for data manipulation and analysis.
NumPy  iconNumPy A library for numerical computing and handling large datasets.
Scikit-learn iconScikit-learnA machine learning library for predictive modeling and analysis.
Deep Learning iconDeep LearningAn advanced subset of machine learning using neural networks.
Power Query & DAX iconPower Query & DAXTools for data transformation and advanced analytics in Power BI.
Model Evaluation & Optimization iconModel Evaluation & OptimizationStrategies to fine-tune machine learning models for better performance.
OpenCV iconOpenCVA library for computer vision tasks like image processing and object detection.
FastAPI iconFastAPIA high-performance framework for building APIs in machine learning and AI applications.
R iconRA statistical programming language widely used for data analysis and visualization.
Matplotlib & Seaborn iconMatplotlib & SeabornLibraries for creating insightful and visually appealing data visualizations.
Power BI & Tableau iconPower BI & TableauBusiness intelligence tools for interactive data visualization and reporting.
Data Preprocessing & Feature Engineering iconData Preprocessing & Feature EngineeringTechniques for preparing and optimizing data for modeling.
Statistical Modeling & Hypothesis Testing iconStatistical Modeling & Hypothesis TestingMethods for analyzing trends and validating assumptions.

Why Choose SevenMentor Data Science

Empowering Careers with Industry-Ready Skills.

Specialized Pocket Friendly Programs as per your requirements

Specialized Pocket Friendly Programs as per your requirements

Live Projects With Hands-on Experience

Live Projects With Hands-on Experience

Corporate Soft-skills & Personality Building Sessions

Corporate Soft-skills & Personality Building Sessions

Digital Online, Classroom, Hybrid Batches

Digital Online, Classroom, Hybrid Batches

Interview Calls Assistance & Mock Sessions

Interview Calls Assistance & Mock Sessions

1:1 Mentorship when required

1:1 Mentorship when required

Industry Experienced Trainers

Industry Experienced Trainers

Class Recordings for Missed Classes

Class Recordings for Missed Classes

1 Year FREE Repeat Option

1 Year FREE Repeat Option

Bonus Resources

Bonus Resources

Curriculum For Data Science

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Learning Comes Alive Through Hands-On PROJECTS!

Comprehensive Training Programs Designed to Elevate Your Career

Fake News Detection

Fake News Detection

Sales Forecasting Using Time Series Analysis(Data Science)

Sales Forecasting Using Time Series Analysis(Data Science)

Resume Screening System (Data Science)

Resume Screening System (Data Science)

Disease Prediction Using Patient Symptoms (Data Science)

Disease Prediction Using Patient Symptoms (Data Science)

Music Genre Classification(Data Science)

Music Genre Classification(Data Science)

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Course Content

About the Data Science Course

Data Science has become the backbone of modern decision-making, technological advancements, and innovation across industries. From retail to healthcare, finance to logistics, every sector is utilizing data-driven approaches to optimize processes and gain a competitive advantage. The growing need for professionals who can interpret complex datasets, build predictive models, and generate actionable insights has made Data Science one of the most sought-after skills globally. Businesses today thrive on structured and unstructured data, and the ability to transform this data into meaningful knowledge is invaluable. This is why pursuing Data Science Classes in Trimurti Nagar is a wise step for anyone aspiring to build a career in the evolving digital landscape.
 

The Scope of Data Science in the Modern World

Data science encompasses much more than just analysis. The complete data management lifecycle is covered, including data collection, cleansing, exploration, visualisation, and machine learning model deployment. Strong technical abilities, an analytical attitude, and the capacity to effectively convey insights to stakeholders are all expected of data scientists. Professionals in data science are at the forefront of facilitating innovation as industries increasingly automate decision-making. Professionals who can extract insights from data are now viewed as strategic partners in growth, and organisations perceive data as an asset. Enrolling in Data Science training in Trimurti Nagar offered by SevenMentor provides individuals with the necessary exposure to practical industry use cases and ensures they are well-prepared to meet such demands.
 

Curriculum Coverage in Data Science Training

The curriculum of Data Science Courses in Trimurti Nagar offered by SevenMentor is designed to be holistic and industry-oriented. The journey begins with core programming skills, primarily in Python and R, since these languages are most commonly used for data manipulation and modeling. Learners are then introduced to statistics and probability, which form the mathematical foundation of Data Science. Concepts like hypothesis testing, regression analysis, and statistical inference ensure learners are adept at interpreting data distributions.

The training further covers data wrangling and cleaning techniques to handle raw datasets effectively. Students gain proficiency in SQL for database management and Excel for basic analytical tasks. The advanced modules focus on machine learning algorithms such as decision trees, random forests, support vector machines, and neural networks. Training also emphasizes hands-on exposure to visualization libraries like Matplotlib, Seaborn, and Tableau. These modules ensure learners can communicate complex results in a comprehensible manner.

The final segment of the curriculum includes big data handling with Hadoop and Spark, along with exposure to cloud platforms for deploying models. By the end of the program, learners emerge with the ability to work on end-to-end projects and solve real-world challenges, ensuring they are job-ready.
 

Practical Learning and Hands-On Exposure

A distinctive feature of Data Science Classes in Trimurti Nagar at SevenMentor is the emphasis on practical exposure. Rather than relying solely on theoretical learning, the training includes multiple projects and case studies. Learners are encouraged to work with real-world datasets from domains such as e-commerce, banking, healthcare, and telecommunications. These projects mimic actual industry challenges and prepare students to tackle them effectively.

Practical sessions often include data preprocessing tasks, exploratory data analysis, building machine learning models, and evaluating their performance. The iterative process of tuning models and testing outcomes teaches learners how to optimize solutions. This hands-on learning ensures that the students not only understand the concepts but also gain confidence in applying them to professional scenarios.
 

Tools and Technologies Covered in the Course

The training program introduces learners to the most widely used tools in Data Science. Python remains the primary language of instruction due to its versatility and rich ecosystem of libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow. R is introduced for advanced statistical operations, while SQL is taught for handling large datasets.

Tableau, Power BI, and Python libraries are used to handle data visualisation. In order to create sophisticated models, learners are also introduced to machine learning frameworks such as PyTorch and Keras. The big data processing curriculum incorporates Hadoop and Spark. Furthermore, familiarity with cloud services such as AWS, GCP, and Azure helps one comprehend how models are deployed in practical settings. By becoming proficient using these resources, students build a broad skill set that employers highly respect.
 

Industry Relevance of Data Science Training

The design of Data Science training in Trimurti Nagar at SevenMentor ensures it is aligned with current industry requirements. Employers seek professionals who can bridge the gap between raw data and strategic decision-making. By integrating business case studies and domain-specific projects into the training, learners gain exposure to practical problem-solving scenarios.

For instance, predictive modeling for customer churn in telecom, fraud detection in banking, or recommendation systems in retail are all common use cases covered in the course. These scenarios reflect real industry challenges, making learners familiar with the workflows they will encounter in their careers. This alignment with industry expectations increases the employability of students and enhances their career prospects.
 

Career Opportunities After Training

Completing Data Science Courses in Trimurti Nagar from SevenMentor opens up numerous career opportunities. It is possible to pursue careers as a data scientist, data analyst, business intelligence developer, machine learning engineer, or data engineer. These jobs are available in a variety of industries, giving graduates freedom to select their areas of interest.

As businesses depend more and more on data-driven insights, there is a growing need for data science specialists. Pay opportunities are also quite alluring, with talented individuals receiving compensation commensurate with their technical know-how and critical thinking skills. This course offers a crucial skill set for anyone who want to future-proof their careers in a digital economy.
 

Why Choose SevenMentor?

SevenMentor, the best training institute for Data Science Training has earned a reputation for delivering high-quality, industry-relevant education. With experienced trainers, an updated curriculum, and a focus on practical learning, it stands out as the preferred choice for students and professionals alike.

The institute’s unique teaching methodology combines conceptual clarity with project-based learning. Trainers bring years of industry experience, allowing them to share practical insights and mentor students effectively. SevenMentor also provides personalized guidance, ensuring every learner is supported in their journey. The institute’s strong placement support further enhances career prospects, making it a trusted name for Data Science education.

By enrolling in Data Science Classes in Trimurti Nagar, learners gain access to comprehensive training, practical exposure, and industry-aligned projects. With a curriculum covering everything from statistics to machine learning and big data, students acquire a robust skill set that guarantees employability. Choosing SevenMentor the best training institute for Data Science Training ensures not only quality education but also career guidance and support, making it the right decision for aspiring data professionals.

 

Online Data Science Classes

SevenMentor offers online Data Science Classes in Trimurti Nagar. These programs provide the same comprehensive curriculum as classroom training but can be accessed from anywhere. Online modules are designed to include live interactive sessions, recorded lectures, and doubt-solving sessions. Learners also gain access to cloud-based labs where they can practice tools and technologies remotely.

The online format is particularly useful for working professionals or students balancing multiple commitments. It ensures uninterrupted learning without geographical limitations. With project-based assignments and real-time evaluations, the online training experience is immersive and engaging. This format enables learners to upskill at their own pace while still benefiting from expert guidance.

 

Corporate Data Science Training

SevenMentor provides customized corporate Data Science Training in Trimurti Nagar tailored to industry needs. The corporate programs are designed to upskill employees in areas such as data analysis, machine learning, and visualization, ensuring organizations can leverage data effectively.

Corporate training sessions are customized according to the company’s domain and requirements. Trainers work closely with organizations to design case studies and projects relevant to their business challenges. This approach ensures employees not only learn technical concepts but also understand how to apply them in their work environment. By offering scalable solutions and hands-on workshops, corporate training helps businesses unlock the potential of Data Science for organizational growth.

Frequently Asked Questions

Everything you need to know about our revolutionary job platform

1

Why is Data Science important?

Ans:
Data Science holds a significant place for several reasons: Better Decision Making: Analyzing data allows organizations to make informed, data-backed decisions instead of just relying on instinct or previous experiences. Business Growth: Data Science enables businesses to understand customer behavior, market movements, and operational performance, leading to better services, satisfied customers, and higher profits. Forecasting Trends: With predictive modeling, organizations can anticipate future demands, understand customer needs, and prevent potential risks. Customization: Data-driven insights allow companies to customize their marketing strategies and product recommendations to match individual customer preferences. Driving Innovation: Data Science plays a key role in innovation by optimizing workflows, discovering new possibilities, and identifying areas for enhancement. Scientific Progress: In areas like medical research, environmental science, and social studies, Data Science helps in analyzing huge datasets to make groundbreaking findings.
2

What is Data Science?

Ans:
Data Science is a multidisciplinary field that combines various techniques from statistics, maths, computer science, and industry expertise to extract useful information from data. The main goal of data science is to gather, process, study, and interpret large sets of data to discover hidden trends, patterns, and correlations. These insights help organizations and businesses to make better decisions. By using advanced algorithms and tools, data scientists can recognize valuable information that helps in improving efficiency, optimizing operations, and finding new growth opportunities.
3

Why is Data Science essential for the future?

Ans:
Data Science has gained immense significance in the past years due to the explosion of data generated from sources like social media, online shopping, and IoT devices. This massive data can be used to refine decision-making, enhance efficiency, and develop new services. Some key ways Data Science is shaping the future include- Predictive Models: Data Science builds models that forecast future events based on past data. This is used in sales predictions, fraud detection, and risk management. Machine Learning Applications: It enables machines to learn from data and make decisions without specific programming. This is useful in areas like voice recognition, natural language processing, and image detection. Data Visualization: Complex data can be turned into visual formats that make it easier to understand trends, spot patterns, and present information to stakeholders.
4

What skills are needed for Data Science?

Ans:
To become a successful data scientist, one must master a variety of skills. Some key skills required are: Programming Proficiency: Knowledge of languages like Python, R, or SQL is necessary for data analysis, manipulation, and visualization. Data Cleaning & Preprocessing: The ability to refine and structure unprocessed data to make it useful for analysis. Mathematics & Statistics: A strong grasp of statistics, probability, linear algebra, and calculus is important for effective data modeling. Machine Learning Expertise: Familiarity with classification, regression, clustering, and recommendation algorithms is crucial. Data Visualization: Being able to present insights clearly using tools like Tableau, Power BI, or libraries such as Matplotlib. Industry Knowledge: Understanding the field where data science is applied, like finance, healthcare, or manufacturing, improves the quality of insights. Collaboration Skills: Since data science often involves working in teams with engineers, analysts, and business stakeholders, teamwork is an important skill.
5

Which Programming Languages Are Required for Data Science?

Ans:
Programming plays a crucial role in data science as it helps in handling, analyzing, and interpreting large datasets. By using different programming languages and tools, data scientists can extract meaningful insights, make informed decisions, and solve challenging problems based on data. Some of the essential programming languages and skills for data science include: Python: One of the most widely used languages in data science due to its simplicity, flexibility, and a rich set of libraries. Libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn make it easier to work with data, visualize patterns, and implement machine learning models. R: A programming language designed mainly for statistical analysis and data visualization. It provides a variety of statistical techniques and graphical tools, making it useful for research, reporting, and data analysis tasks. SQL: Structured Query Language (SQL) is essential for working with relational databases. Data scientists use SQL to query, retrieve, and manipulate data efficiently from large datasets stored in databases. Top Programming Languages for Data Science The most preferred languages for data science are Python and R due to their extensive capabilities. Python is a general-purpose language that is easy to learn and comes with a variety of libraries, including NumPy, Pandas, and SciPy. On the other hand, R is a statistical computing language with a vast collection of packages specifically designed for data analysis, visualization, and modeling. Other programming languages that are sometimes used in data science include Julia, SAS, and MATLAB. Julia is a high-performance language optimized for numerical computing. SAS is widely used in business analytics for handling large-scale statistical operations. MATLAB is known for its efficiency in numerical computing, especially in areas like signal and image processing. The choice of programming language in data science depends on the nature of the task. While Python and R are the most commonly used for general data science applications, Julia, SAS, and MATLAB serve specialized purposes.
6

Why Is Python Popular in Data Science?

Ans:
Python is an extremely versatile language that can be used for building different types of applications. It is widely favored in data science due to its straightforward syntax, ease of learning, and ability to work across different platforms. As an open-source language, Python has a vast and active developer community. It is a great choice for beginners due to its readable code structure and extensive learning resources. Additionally, its scalability and efficiency make it a preferred tool for professional data scientists. With strong industry adoption, Python remains a top choice for students and professionals entering the data science field. Importance of Data Science in Python Python is a widely used programming language in data science due to its flexibility, rich library ecosystem, and strong community support. Below are some key reasons why Python plays a crucial role in data science: Data Cleaning and Manipulation: Python provides powerful libraries such as NumPy and Pandas, which simplify data cleaning and manipulation tasks. These libraries allow users to handle large datasets efficiently by sorting, filtering, grouping, and restructuring data. Data Visualization: With libraries like Matplotlib, Seaborn, and Plotly, Python makes it easy to create engaging and interactive visualizations. These tools help data scientists interpret patterns, trends, and insights from data effectively. Machine Learning and AI: Python has an extensive collection of machine learning and artificial intelligence libraries, including TensorFlow, Scikit-learn, and PyTorch. These libraries provide advanced tools for building, training, and deploying predictive models, making data-driven decision-making easier. Natural Language Processing (NLP): Python is widely adopted for NLP tasks like text classification, sentiment analysis, and named entity recognition. Libraries such as NLTK and spaCy simplify text processing, enabling efficient analysis of unstructured data. Data Integration & Connectivity: Python supports seamless integration with different data sources, including SQL and NoSQL databases, APIs, and cloud storage platforms. This flexibility allows data scientists to aggregate and analyze information from multiple sources effortlessly. Python for Data Science and Machine Learning Python is a general-purpose, high-level programming language that has gained immense popularity in the fields of data science and machine learning. Its simplicity, versatility, and comprehensive libraries make it a preferred choice for professionals in these areas. Simplicity: Python’s clear and concise syntax makes it easy to learn, making it accessible to both beginners and experienced programmers. This allows data scientists to focus on solving analytical problems without being overwhelmed by complex code. Versatility: Python is not limited to a specific task—it can be used for data preprocessing, model development, and deployment. This makes it a valuable tool for handling diverse data science and machine learning projects. Extensive Library Support: Python’s rich ecosystem of libraries and frameworks makes it a powerful tool for data science. Libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow provide a broad range of functionalities, from data visualization and manipulation to machine learning model development. Importance of R Programming in Data Science R programming plays a vital role in data science due to its specialized statistical capabilities and powerful data visualization features. Here’s why R is significant in this field: Open-Source & Community Support: Being an open-source language, R is freely available and supported by a vast community of users and developers. This collaborative environment continuously enhances R’s functionality, ensuring its relevance in evolving data science challenges. Comprehensive Statistical Analysis: R provides an extensive set of statistical and data analysis functions, making it a go-to tool for statisticians. It supports various techniques, including regression analysis, clustering, time-series forecasting, and hypothesis testing. Data Visualization Capabilities: R excels in data visualization with libraries like ggplot2, which is widely used for generating high-quality graphs and charts. These visual tools help in creating compelling data presentations and reports. Seamless Integration with Other Tools: R easily integrates with other programming languages such as Python, Java, and C++, allowing data scientists to leverage multiple tools for complex analysis. Machine Learning & AI: With specialized packages for machine learning and artificial intelligence, R supports predictive modeling, natural language processing, and data-driven decision-making in various industries. Essential Database Skills for Data Science SQL for Data Retrieval: Proficiency in SQL is fundamental for data science professionals. It is essential to know how to write optimized SQL queries for retrieving, filtering, aggregating, and joining data from relational databases. Understanding query execution plans is also crucial for improving performance. Database Design & Modeling: Database knowledge extends beyond just querying tables—it includes understanding entity-relationship (ER) diagrams, schema design, and data validation constraints. A well-structured database schema ensures efficient data storage and retrieval for analytical tasks.
7

What is Data Cleaning and Transformation?

Ans:
Data cleaning and transformation are essential steps for any data scientist to ensure raw data is in the proper format for analysis. It’s crucial to have skills in extracting data from multiple sources, converting it into the correct format, and then loading it into a database. Mastery of ETL tools, scripting languages like Python or R, and data transformation techniques is necessary for success in data science.
8

What is Database Optimization?

Ans:
Database optimization involves using various techniques to enhance database performance, such as creating indexes, denormalizing the structure, and leveraging caching mechanisms. Indexes, in particular, play a significant role in speeding up data retrieval. By adding indexes, the database engine can locate data more efficiently, leading to faster query responses.
9

How to Ensure Data Integrity and Quality?

Ans:
Maintaining data integrity is key to ensuring the accuracy and reliability of data. Various constraints, including unique, not null, and check constraints, are applied to prevent data errors. Additionally, using transactions ensures that multiple operations are handled as one, preventing partial updates and ensuring consistency.
10

Why is Integration with Tools and Languages Important for Data Scientists?

Ans:
To be successful, a data scientist must understand how to connect to databases and analyze data using programming languages like Python. Mastery of tools such as Python's pandas, R, and visualization libraries allows for effective data analysis and comprehensive presentation of findings. These tools enhance a data scientist's ability to draw meaningful insights from data.
11

What is Database Management and Storage in Data Science?

Ans:
In data science, managing and storing data is essential, especially when handling large datasets. Key components of this process include: Data Storage: Relational Databases like MySQL, PostgreSQL, and SQL Server are typically used for storing structured data. NoSQL Databases like MongoDB and Cassandra are ideal for handling unstructured or semi-structured data. Data Warehouses like Amazon Redshift and Google BigQuery are optimized for reporting and analytics tasks. Data Management: Data Cleaning ensures raw data is transformed into useful formats, involving tasks such as handling missing values and detecting outliers.
12

What Are the Key Tools for Database Management and Storage in Data Science?

Ans:
Two of the most important tools for managing and storing data in data science are MySQL and Hadoop. MySQL is a relational database management system that allows users to store, organize, and retrieve data efficiently. It is widely used for handling large datasets and is essential for data science tasks. Hadoop is a distributed computing framework ideal for processing and analyzing large datasets across multiple machines. It helps businesses and organizations handle vast amounts of data, enabling effective data management and insight extraction at scale.
13

How is Data Science Different from Data Analysis?

Ans:
Data science involves a wider range of activities than data analysis. It covers tasks such as collecting, organizing, cleaning, and interpreting data, in addition to developing and deploying machine learning models. These models can be used across many applications. Data analysis, however, is a more focused activity that revolves around exploring and visualizing data to identify trends and patterns that can assist with decision-making. While data analysis is an integral part of data science, it represents just one facet of the broader discipline.
14

Best Institute for Data Science in Pune?

Ans:
SevenMentor Institute is recognized as one of the top Data Science Training Institutes in Pune for the following reasons: The course offers a detailed overview of the entire Data Science Lifecycle, ensuring a well-rounded understanding of all aspects within the field. You will gain proficiency in analytics with Python and MySQL, equipping you with essential skills and knowledge to thrive in the industry. The Data Science course fees in Pune are competitive, providing great value for money and a solid investment in your career. In addition to the course, SevenMentor Institute offers webinars and more than 20 assignments, all designed to help you become job-ready.
15

Is This Course Suitable for Beginners?

Ans:
Yes, this course is designed with beginners in mind. SevenMentor Institute is recognized as one of the top Data Science Institutes in Pune, and it starts by teaching the fundamentals of mathematics and statistics. Students will also learn Python, the most commonly used programming language for data science.
16

What Are the Prerequisites for Enrolling in a Data Science Course?

Ans:
To enroll in this data science course, it is helpful to have a basic understanding of: Mathematics, including probability, statistics, algebra, and calculus. Object-oriented programming languages such as Java, C, or Python. Familiarity with data manipulation tools like SQL and Excel can also be an advantage. Good problem-solving and critical thinking skills are also recommended.
17

What Does the Curriculum of the Data Science Course Cover?

Ans:
The data science curriculum is designed to equip students with the skills needed to analyze and interpret large, complex datasets. The program usually includes: In-depth training in Python and other programming languages for data manipulation. Topics like data visualization, statistical analysis, machine learning, deep learning, and real-world projects that provide hands-on experience. By the course’s end, students are prepared to tackle complex data analysis challenges and make data-driven decisions.
18

What Programming Tools & Languages Are Covered?

Ans:
Data science involves several tools and languages that are essential for analyzing and interpreting data. Key tools include: Python and R: Both are widely used for their versatility and rich libraries in data analysis and manipulation. Visualization tools like Tableau and Power BI: These are used to create visual insights from data. SQL: A crucial tool for querying and retrieving data from databases. Machine learning libraries: These are used to build predictive models. These tools are necessary for data scientists to gather, manipulate, and derive meaningful insights from data.
19

Who Can Benefit from a Data Science Course?

Ans:
Anyone interested in working with data to extract valuable insights can benefit from this course, including: Students aiming for a career in data science. Working professionals from diverse fields such as IT, finance, healthcare, and more. Individuals considering a career change to data-related roles.
20

What Are the Job Prospects After Completing a Data Science Course?

Ans:
Data Science is a rapidly growing industry, and there is a high demand for skilled professionals. After completing the course, graduates can apply for roles such as: Data Analyst Data Scientist Machine Learning Engineer Business Intelligence Analyst And more, across various industries.
21

Can I Enroll in This Course After Completing My Degree?

Ans:
Yes, you can enroll in a data science program after completing your degree. SevenMentor Institute offers a program that provides hands-on experience through real industry projects, making it a great opportunity for recent graduates.
22

What Is the Expected Salary for a Fresher or Experienced Data Scientist in India?

Ans:
A fresher in Data Science can expect to earn an average salary of INR 5,00,000 annually. With experience, a Data Scientist can earn up to INR 6,00,000 or more, depending on their skills and job role.

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