Full Stack Master Certification in Data Science Course 

Get Trained for Full Stack Data Science Course 

  • Join for Beginner's / Advanced Course to Master Full stack data science course with top skills such as Python, SQL, R, Flask, Django and Jenkins and much more, starting from scratch.
  • Build your portfolio by working on real-world business projects and showcase your skills in career CV/Resume. 
  • Get Job Assistance support and ongoing guidance after course completion to ensure your success in the industry. 
Next Batch Starts

10 Feb 2024

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Key Features of Data Science Course

Learn skills from iNeuBytes "Master certification in Full stack Data science Course" program which features key tools with Python, SQL, R, Flask, Django and Jenkins.

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3/6 Months Course + Virtual Internship

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125+/280+ Hours Live Instructor Led Training

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Q&A Expert Mentorship Support

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Job Oriented Assistance

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Certificate of Completion

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24*7 Doubt Clearing Support

Why Full stack Data Science Course in 2024?

As we journey through the digital epoch in 2024, the need for proficient data science experts is escalating exponentially across every sector. Enterprises, more than ever, are leveraging the potency of data, not only to produce actionable insights but also to craft predictive models, enabling them to make foresighted decisions and guide their growth strategies. According to PayScale, as of 2024, the average annual salary for a Data Scientist in the United States is approximately $96,491, while in India, it is roughly INR 7.3 LPA. And in 10 years, the salary figure doubled upto INR 14 LPA  and these figures underscore the substantial return on investment that mastering data science can yield, thereby making it a lucrative career path in the contemporary data-driven world.

  • By taking the course, you can learn 10+ important tools in beginners program and 48+ tools in advanced course of data science course. 
  • You can build your portfolio by working on real-world business projects and gain practical experience.
  • Get Job Assistance support and ongoing guidance after course completion to ensure your success in the industry. 
course

Why Enroll for Data Science Course for Training?

With a growing demand for data science across sectors, upskilling the industry oriented skills for today's job market is very important.

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Mastering Full Stack Data Science is a passport to rewarding careers across industries, empowering professionals to decode complex data into transformative business insights.

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Full stack data science, is useful for complex tasks lik developing AI-driven solutions, and making data-informed strategic decisions, preferred by top MNC's like Microsoft, Amazon, and Deloitte.

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In India, the average salary for Advanced Data Science professionals ranges from INR 588,000 to INR 22,20,000 per annum, with an average annual salary of INR 730,000 per annum.

How does it work?

Navigating the field of data science opens up a wealth of opportunities with some of the world's top companies. Join iNeuBytes Full stack Data Science Course today!

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Annual Avg. Salary

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1

Beginner Course Overview

To know the Importance of data science modules and topics covered in 3 Month Beginner's course. Download the Curriculum Now.

Learning Outcomes

  • Understand Python basics, and master programming concepts and techniques.
  • Get hands-on experience with Python Libraries and use them for data manipulation.
  • Gain practical knowledge in exploratory data analysis, data visualization, and data cleaning using Python.

Topics Covered

  • The module 1 begins with an introduction to Python, covering its fundamentals, followed by deeper exposure to programming in Python. The module further delves into Python libraries, which form an essential part of data manipulation and analysis. It then takes you through the methods of Exploratory Data Analysis (EDA) using Python, where you understand data structures, patterns, and anomalies. The subsequent section introduces visualization in Python, allowing you to understand data more intuitively. The module also integrates an introduction to statistics for data science, providing a statistical foundation for your data analysis. Lastly, you'll learn data cleaning and preprocessing techniques in Python, which are crucial for preparing your data for further analysis or model building. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Master both basic and advanced SQL commands, gaining the ability to create, manipulate, and query databases efficiently.
  • Understand and implement data modelling concepts in SQL, improving your ability to design databases that best suit business or application needs.
  • Utilize SQL for data analysis, learning to apply various analytical functions to extract valuable insights from data.

Topics Covered

  • The module 2 introduces you to SQL, starting with fundamental commands and concepts, then progressing to more complex queries and structures in the Advanced SQL section. It covers how to structure and organize data optimally using Data Modelling with SQL, which includes understanding and implementing concepts like normalization and relationships. Lastly, the module emphasizes how SQL can be an effective tool for Data Analysis, teaching you to use various analytical functions to generate insights from data and execute complex data analysis tasks efficiently. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Acquire an understanding of data visualization principles and how they can be implemented using Tableau.
  • Master the basics of creating visualizations in Tableau and gradually progress to more advanced visualization techniques.
  • Learn to build interactive dashboards and use storytelling to make data insights more compelling and understandable.

Topics Covered

  • The module 3 is centered around understanding and utilizing Tableau, a powerful data visualization tool. You'll start with the fundamentals of the software, learning how to import and organize data within the platform. From there, you'll gain proficiency in creating various types of charts and graphics to represent your data visually, supporting a more intuitive understanding of your data's patterns and trends. As you advance, you'll delve into more complex visualization techniques, crafting intricate and insightful visuals. The module culminates in learning how to create comprehensive dashboards and practice the art of storytelling with Tableau. This ability to create a narrative with your data helps to present your findings in a compelling and easily understood manner, facilitating data-driven decision-making processes. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Acquire foundational knowledge of machine learning, its applications and problem setting.
  • Understand the principles of supervised and unsupervised learning.
  • Learn about model overfitting, underfitting, validation and evaluation metrics.

Topics Covered

  • The module 4 covers the basics of machine learning, starting with a foundational understanding of the field and problem setting in machine learning. It covers two main types of machine learning: supervised learning, where the model learns from labeled data, and unsupervised learning, where the model identifies patterns in unlabeled data. It also discusses concepts such as overfitting and underfitting, which refer to the model's performance on training and unseen data. The module also covers model validation techniques to assess the accuracy and quality of models, and evaluation metrics that help quantify the performance of models. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Take a deep dive into tree models and ensemble methods in machine learning.
  • Learn about time series forecasting, model selection, and validation.
  • Gain skills in feature engineering, neural networks, and basics of deep learning.

Topics Covered

  • The module 5 continues the journey into machine learning, providing a deep dive into tree models, a type of predictive modelling technique. It introduces the concept of time series forecasting, which is crucial in many business scenarios. It also covers ensemble methods in machine learning, which combine predictions from multiple models to improve overall performance. The module moves forward with model selection and validation techniques. It also introduces feature engineering techniques, a process of creating new features from existing data to improve model performance. Then, you will get an introduction to neural networks, a type of machine learning model inspired by the human brain, and the basics of deep learning, a subfield of machine learning involving neural networks with multiple layers. The module concludes with data storytelling and interpretation, a key aspect of presenting your data analysis and model results. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Understand the basics of NLP and text processing.
  • Gain practical experience in working with text data in Python.
  • Learn about feature extraction from text, sentiment analysis, and text classification.

Topics Covered

  • The module 6 starts with an introduction to Natural Language Processing (NLP), a field at the intersection of computer science, artificial intelligence, and linguistics. You will learn the basics of text processing, including techniques to clean, normalize, and prepare text data for analysis or model building. Working with text data in Python is then covered, giving you hands-on experience. The module progresses into feature extraction from text, a crucial step in transforming text data into a format that can be analyzed or fed into models. The module then discusses sentiment analysis, a method used to identify and extract subjective information from text data, and text classification, a common task in NLP where text is categorized into predefined classes. Download Curriculum to Know the topics clearly.

Learning Outcomes

  • Apply the skills and knowledge acquired from the course in a practical work setting.
  • Gain experience working on real data science projects.
  • Develop professional skills such as teamwork, communication, and problem-solving.
  • Receive feedback on performance and areas of improvement.
  • Understand the challenges and dynamics of working in a data science role.

Topics Covered

  • In the final module, Interns will undertake a one-month virtual internship to apply the skills and knowledge they have acquired. You'll gain practical experience and receive feedback on their work, which is vital in transitioning from classroom learning to real-world application. This hands-on experience will help the course to understand and navigate the challenges of working as a data scientist. Download Curriculum to Know the topics clearly.
2

Advance Course Overview

To know the Importance of data science modules and topics covered in 6 Month Advance course. Download the Curriculum Now.

Learning Outcome

  • Gain an understanding of the data science process, with the ability to identify and apply the stages of the CRISP-DM and OSEMN methodologies.
  • Develop proficiency in various data science tools, including Jupyter notebooks, Anaconda, and Google Colab, as well as command-line basics.
  • Understand and apply Python libraries for data science such as NumPy, Pandas, and SciPy, and acquire skills in data collection techniques like web scraping and using APIs.

Topics Covered

  • In this foundational module, you will gain a comprehensive overview of the data science process, touching on methodologies like CRISP-DM and OSEMN. Key data science tools, including Jupyter notebooks, Anaconda, and Google Colab will be introduced. You'll learn how to utilize various Python libraries essential for data science such as NumPy, Pandas, and SciPy. Techniques for data collection, such as web scraping with Beautiful Soup and using APIs, will also be taught. Furthermore, you will get introduced to Git and version control systems for managing data science projects, basics of command line for data manipulation, and an overview of various cloud platforms like AWS, Google Cloud, and Azure. You'll also get familiar with Linux basics and Docker for creating reproducible environments. The module wraps up with the importance of ethics in data science and real-world applications and case studies. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply the concepts of data cleaning, preprocessing, and visualization using various libraries and tools.
  • Gain proficiency in creating interactive dashboards and utilizing advanced Excel functions for data analysis.
  • Learn to use SQL for data analysis and time series analysis with Pandas and Statsmodels.

Topics Covered

  • The Module 2 dives into data analysis and visualization techniques. Key skills include data cleaning and preprocessing with Pandas, and visualization with libraries like Matplotlib, Seaborn, Plotly, and Bokeh. You'll also learn to visualize geospatial data with GeoPandas and Folium. In terms of business intelligence, you'll be introduced to Tableau. The module will also touch on advanced excel functions for data analysis, SQL for data analysis, and time series analysis with Pandas and Statsmodels. Additionally, creating interactive dashboards with Dash and using PowerBI for data analysis and visualization will be covered. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand the principles of hypothesis testing, statistical significance, and multivariate statistical analysis.
  • Learn and apply data imputation techniques, experiment design, and Bayesian statistics.
  • Gain skills in advanced SQL queries for data analysis, and statistical analysis using R.

Topics Covered

  • The Module 3 will introduce advanced statistical analysis techniques including hypothesis testing, multivariate statistical analysis, correlation and covariance, data imputation techniques, Bayesian statistics, and design of experiments. In addition, you'll learn how to compose advanced SQL queries for data analysis and the use of R for statistical analysis. The module will also delve into non-parametric statistical methods, survival analysis, and the handling of multi-dimensional data. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply concepts related to ETL processes, database design, and normalization.
  • Learn to use SQL and NoSQL databases, and real-time data processing tools like Apache Kafka.
  • Gain proficiency in data storage and retrieval techniques, with an emphasis on data encryption and security.

Topics Covered

  • The module 4 focuses on data engineering aspects of data science, where you will learn about ETL (Extract, Transform, Load) processes, database design and normalization, and working with SQL and NoSQL databases. You'll get introduced to Apache Kafka for real-time data processing and create data pipelines with Apache Airflow. You will also learn how to handle big data with Apache Spark and get an introduction to data warehousing with Amazon Redshift. Concepts around data storage and retrieval with Elasticsearch and cloud data storage such as AWS S3 and Google Cloud Storage will be taught. The module ends with a focus on using Hadoop for distributed data storage and processing and an overview of data encryption and security. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply concepts related to Google BigQuery, Amazon Athena, and data warehousing tools.
  • Learn to work with OLAP and OLTP systems, data lakes, and real-time data processing tools.
  • Gain insights into data governance, data cataloging, privacy, and security in big data.

Topics Covered

  • In module 5, you'll learn about Google BigQuery and Amazon Athena for big data analytics, as well as understanding OLAP and OLTP systems. You'll get to work with data warehousing tools like Snowflake and Teradata, and gain an understanding of data lakes. You'll also get an introduction to Apache Hadoop and Apache Spark, and learn about real-time data processing with Apache Flink. Furthermore, the module will cover data partitioning and indexing strategies, data governance and cataloging, privacy and security in big data, implementing ETL processes in data warehousing, and conclude with big data analytics use cases. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply techniques for handling categorical features, feature scaling, and normalization.
  • Learn dimensionality reduction techniques and how to handle missing data.
  • Gain skills in text and image feature extraction, automated feature engineering, and feature selection techniques.
  • The Module 6 delves into feature engineering, starting with handling categorical features and techniques for feature scaling and normalization. It will also cover binning and feature discretization, handling missing data, and using dimensionality reduction techniques for feature extraction like PCA and t-SNE. The module also presents text feature extraction techniques such as Bag of Words, TF-IDF, word embeddings, and image feature extraction with pre-trained models. Additionally, you'll learn about automated feature engineering with tools like Featuretools, creating interaction features, encoding techniques for categorical variables, and feature selection techniques. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply various supervised learning algorithms, including linear regression, logistic regression, and tree-based models.
  • Gain an understanding of neural networks, bias-variance tradeoff, and gradient boosting algorithms.
  • Learn techniques for model interpretation, handling imbalanced datasets, and using ensemble methods.

Topics Covered

  • The Module 7 provides a thorough introduction to supervised learning algorithms. This includes understanding and implementing linear regression, logistic regression, and tree-based models like decision trees, random forests, and XGBoost. You'll also explore support vector machines, gain an introduction to neural networks, and understand the bias-variance tradeoff. Additionally, you'll learn the basics of neural networks including perceptron and multi-layer perceptron, gradient boosting algorithms like LightGBM and CatBoost, and model interpretation techniques like LIME and SHAP. The module concludes with handling imbalanced datasets, using ensemble methods, and a brief introduction to reinforcement learning. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply clustering techniques, dimensionality reduction techniques, and anomaly detection methods.
  • Gain skills in association rules mining and recommender systems.
  • Develop an understanding of natural language processing, including topic modeling and sentiment analysis.

Topics Covered

  • In this module, you'll delve into unsupervised learning algorithms, starting with k-means and hierarchical clustering techniques. You'll then explore advanced clustering methods such as DBSCAN, OPTICS, and Spectral Clustering, and get an introduction to dimensionality reduction techniques. Additionally, you'll learn about anomaly detection techniques, association rules mining with Apriori and Eclat, and recommender systems such as collaborative filtering and content-based filtering. The module concludes with an introduction to natural language processing, including topic modeling and sentiment analysis. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply concepts related to deep learning frameworks like TensorFlow, PyTorch, and Keras.
  • Learn about various network architectures including CNNs, RNNs, LSTMs, and transformers.
  • Gain proficiency in generative deep learning, reinforcement learning with deep learning, and natural language processing with deep learning.

Topics Covered

  • The Module 9 covers deep learning and neural networks, starting with an introduction to deep learning frameworks like TensorFlow, PyTorch, and Keras. You'll learn about convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). The module also introduces the concept of transfer learning, transformers, and attention mechanisms. You'll explore generative deep learning through GANs and style transfer, and reinforcement learning with deep learning techniques like Q-Learning and Deep Q-Networks. Natural Language Processing with deep learning including BERT and GPT, image and video processing with deep learning, and cloud-based AI services will also be covered. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply evaluation metrics for classification and regression tasks.
  • Learn and apply cross-validation techniques and hyperparameter tuning techniques.
  • Gain an understanding of model selection techniques, mitigating model overfitting and underfitting, and model interpretability.

Topics Covered

  • In this module, you'll understand and apply evaluation metrics for classification tasks like confusion matrix, ROC, AUC, and for regression tasks like MSE, RMSE, MAE, and R-Squared. It will also introduce you to cross-validation techniques like K-Fold, Stratified, and Time-Series. Hyperparameter tuning techniques such as grid search, random search, and Bayesian optimization will be covered. Additionally, you'll learn about model selection techniques, regularization, stepwise regression, and understand how to mitigate model overfitting and underfitting. The module wraps up with an introduction to model interpretability and concepts of model fairness, accountability, transparency, and ethics (FATE). Download Curriculum to Know the topics clearly.

Learning Outcome

  • Understand and apply concepts related to model deployment, including using Docker and cloud-based AI platforms.
  • Gain skills in MLOps principles, including continuous integration and deployment for ML models.
  • Learn about monitoring and maintaining models in production, A/B testing, serverless deployment, and scaling ML models.

Topics Covered

  • In the final technical module, you will learn about model deployment strategies using Flask, Django, and cloud-based AI platforms. You'll explore the containerization of models using Docker and get an introduction to MLOps (DevOps for machine learning). The module will also cover continuous integration and deployment for ML models with tools like Jenkins, monitoring and maintaining models in production, A/B testing, and multivariate testing. Moreover, you'll learn about serverless deployment using AWS Lambda and Google Cloud Functions, scaling ML models, and using Kubernetes for managing deployed models. The module concludes with a discussion on ethics, privacy, and legal considerations in model deployment. Download Curriculum to Know the topics clearly.

Learning Outcome

  • Apply all learned data science concepts in a real-world context, enhancing practical understanding and experience.
  • Develop professional skills such as teamwork, project management, and communication in a data science environment.
  • Gain insights into industry practices, challenges, and strategies in the field of data science.

Topics Covered

  • In the concluding module, you will apply all of the learned data science concepts in a real-world context during a three-month virtual internship program. This will help you develop practical skills, enhance your understanding and experience in data science, and expose you to industry practices, challenges, and strategies. This hands-on experience will prepare you for a successful transition into a professional data science role. Download Curriculum to Know the topics clearly.

Instructor-led Master Certification in Data Science Course Live Online Training Schedule

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Instructor-led Master Certification in Data Science Course Live Online Training Schedule

Flexible batches for you

Price ₹80,000

48,000

40% OFF, Save ₹32000.
Ends in 12d : 00h : 4m : 0s
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Skills Covered

With its impressive array of 70+ Skills, data science provides valuable insights from data, Informed Decision-Making,Predictive Capabilities, Risk Management, Innovation and bolster business success.

Containerization

Virtualization

Data-Cleaning

Data-Interpolation

Geospatial-Analysis

Hypothesis-Testing

Non-Parametric-Statistics

Time-Series-Analysis

Bayesian-Inference

Stream-Processing

Distributed-Computing

Data-Partitioning

Data-Warehousing

Data-Lake-Management

Feature-Engineering

Dimensionality-Reduction

Text-Processing

Semantic-Analysis

Ensemble-Learning

Gradient-Boosting

Deep-Learning

Clustering

Association-Rules

Generative-Models

Convolutional-Networks

Recurrent-Networks

Transfer-Learning

Hyperparameter-Optimization

Microservices

Continuous-Deployment

Tools Covered

Unlock the full potential of your data with Data! We cover 48+ Tools/software, Download Advanced Data Science Curriculum to know more.

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Full Stack Data Science Projects

Projects provide practical application of learned skills, enhancing understanding and retention. Here are some sample projects: 

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Practice Essential Tools

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Designed By Industry Experts

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Get Real-world Experience

  ineubytes Other Coaching Institutes
Teaching Methodology Live Online Comprehensive, interactive sessions, practical examples, hands-on exercises Mix of theoretical lectures and practical exercises with pre-recorded classes
Course Content Designed by Industrial Specialists based on Top MNC Recruitment Standards Not based on Recruitment standards
Trainers Alumni of IIT & Top MNCs such as Infosys, IBM and also had Ph.D Experience Limited Experience
Student Support Dedicated support through online forums, Q&A sessions, personalized guidance Support availability may vary
Flexibility and Convenience Flexible course schedules, online learning options Varies, some offer fixed schedules, differing online learning options
Learning Resources Access to a variety of learning materials, such as e-books, practice exercises, and video tutorials Limited Availability and quality of learning resources 
Success Rate Track 1500+ record of student success, testimonials, and alumni achievements Success rates may vary and depend on various factors
Feedback and Reviews Positive student feedback and reviews on the website Reviews and feedback may differ among coaching centers
Soft-skills Training
Portfolio Preparation
Resume Preparation
Mock Interviews
Personal Branding

iNeubytes alumni work at reputed tech organizations and promising startups

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Have a Doubt?

Frequently Asked Question

The Full Stack Data Science course by iNeuBytes is an intensive program split into two sub-courses: Data Science Beginners (3 months) and Advanced Data Science (6 months). The curriculum is designed to impart a comprehensive understanding of data science, ranging from fundamental concepts to advanced techniques. It covers topics like data analysis, visualizations, advanced data analysis, data engineering, data warehousing, big data, feature engineering, supervised and unsupervised learning algorithms, deep learning, neural networks, model evaluation, validation, and deployment. The curriculum is available for download, allowing learners to understand the value of the courses before purchase.

With the rise of big data, the demand for full-stack data scientists who can handle all aspects of the data science pipeline is growing. The course provides key benefits like in-depth understanding of data science, hands-on experience through a virtual internship, and career opportunities in various industries. Data scientists can work in roles such as data analyst, data engineer, machine learning engineer, and business intelligence analyst.

The course is suitable for anyone aspiring to build a career in data science. This could include students, IT professionals, analysts, engineers, and even managers who deal with data. Basic mathematical skills, familiarity with a programming language (preferably Python), and a logical and analytical mindset are useful.

The primary objective of the course is to equip learners with the necessary skills to understand, analyze, and interpret complex data. Upon completion, learners will be able to clean, visualize, and analyze data; apply machine learning and deep learning algorithms; create data-driven solutions; and deploy models in a real-world environment.

For the Beginner's Data science program, basic foundation of python is needed and for Advanced Data Science course, it's beneficial to have a foundational understanding of data science concepts, basic programming skills (Python recommended), and knowledge of statistics and mathematics.

The internship included in the Full Stack Data Science course is virtual, enabling students to gain hands-on experience remotely.

The virtual internship is integrated within the course timeline and runs concurrently with the course. The course data science for Beginner's is 3 months course including one month Virtual data science internship and the Advanced data science course is 6 months course including three months Virtual data science internship program.

Yes, completing the virtual internship is essential as it offers practical experience and the opportunity to apply the concepts learned during the course.

Upon completion of the major project in the virtual internship, you'll receive an internship completion letter. After the training with satisfactory attendance, you'll receive a training certificate. These certifications attest to your skills and knowledge in the field of data science and can significantly boost your professional profile.

🔥🔥 Start (OR) Switch your career into Data Analyst to get 20 LPA 🔥🔥