Full Stack Data Science Course in Anand

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The Full Stack Data Science course is designed to equip learners with comprehensive skills in data science, utilizing Python programming from basic to advanced levels. The course covers relational databases, essential mathematics, and both basic and advanced statistics to build a strong analytical foundation. Students will delve into machine learning, deep learning, natural language processing (NLP), and computer vision (CV) to solve complex data problems. Special focus areas include becoming proficient as NLP Engineers, Machine Learning Engineers, CV Engineers, and Generative AI Engineers. The curriculum includes practical applications and project-based learning to prepare students for real-world challenges in data science and AI.

|| Data Science Certification Course in Anand

The Full Stack Data Science Course in Anand is a comprehensive training program designed to equip learners with the essential skills needed to manage end-to-end data science projects. This course covers a wide range of topics, starting with foundational concepts in mathematics and statistics, which are crucial for data analysis. Students will gain proficiency in Python, the primary programming language used for data manipulation, analysis, and building data-driven solutions.

Through the  Data Science training course in Anand, participants will learn to preprocess, clean, and conduct exploratory data analysis to prepare raw data for deeper insights. The core data science modules focus on machine learning algorithms, covering both supervised and unsupervised techniques, as well as an introduction to deep learning with neural networks. Real-world projects and hands-on applications will allow students to build predictive models, perform natural language processing, and analyze time-series data. The course also includes training in data visualization tools like Matplotlib, Seaborn, and Plotly, along with business intelligence platforms such as Tableau and Power BI. These tools help students communicate complex data insights effectively. Additionally, learners will explore data engineering, gaining experience in building data pipelines and working with both SQL and NoSQL databases.


As part of the full-stack approach, the course provides instruction on deploying machine learning models and data applications using cloud platforms like AWS, Azure, and Google Cloud. This includes Docker for containerization, Kubernetes for orchestration, and API development for model deployment. By the end of the course, students will be equipped to manage every phase of a data science project, from data collection and cleaning to model development and deployment. The Full Stack Data Science classes in Anand offer a perfect blend of theoretical learning and practical experience, ensuring students are prepared for real-world challenges in the rapidly growing field of data science.


Join the  Data Science Course in Anand today and take the first step toward becoming a skilled data scientist, ready to innovate and excel in the ever-evolving world of data. "Become a Data Science Expert and Shape the Future of Technology in Anand"

|| What will I learn?

  • Basics of statistics, probability, and linear algebra necessary for understanding data analysis and machine learning algorithms.
  • Proficiency in languages such as Python, including libraries like NumPy, Pandas, and scikit-learn for data manipulation, analysis, and modeling.
  • Build and evaluate machine learning models for classification, regression, clustering, and recommendation.
  • Techniques for handling raw data, including cleaning, transforming, and preparing it for analysis and modeling.
  • Methods and tools for exploring data sets visually and statistically to uncover patterns, anomalies, and relationships.
  • Understanding and implementation of supervised (e.g., regression, classification) and unsupervised (e.g., clustering, dimensionality reduction) learning algorithms.
  • Deep learning for tasks like image recognition and natural language processing (NLP) using frameworks like TensorFlow or PyTorch.
  • Creation of informative and insightful visualizations using tools such as Matplotlib, Seaborn, and Plotly to communicate findings effectively.
  • Familiarity with handling large-scale data using platforms like Hadoop and Spark, and knowledge of cloud computing environments (e.g., AWS, Azure).
  • Understanding of database systems (SQL, NoSQL) for storing and retrieving data efficiently.
  • Techniques for deploying machine learning models into production environments and monitoring their performance over time.
  • Hands-on experience working on real-world data science projects, applying learned concepts to solve business problems and gain practical experience.

|| What will I learn?

  • Basics of statistics, probability, and linear algebra necessary for understanding data analysis and machine learning algorithms.
  • Proficiency in languages such as Python, including libraries like NumPy, Pandas, and scikit-learn for data manipulation, analysis, and modeling.
  • Build and evaluate machine learning models for classification, regression, clustering, and recommendation.
  • Techniques for handling raw data, including cleaning, transforming, and preparing it for analysis and modeling.
  • Methods and tools for exploring data sets visually and statistically to uncover patterns, anomalies, and relationships.
  • Understanding and implementation of supervised (e.g., regression, classification) and unsupervised (e.g., clustering, dimensionality reduction) learning algorithms.
  • Deep learning for tasks like image recognition and natural language processing (NLP) using frameworks like TensorFlow or PyTorch.
  • Creation of informative and insightful visualizations using tools such as Matplotlib, Seaborn, and Plotly to communicate findings effectively.
  • Familiarity with handling large-scale data using platforms like Hadoop and Spark, and knowledge of cloud computing environments (e.g., AWS, Azure).
  • Understanding of database systems (SQL, NoSQL) for storing and retrieving data efficiently.
  • Techniques for deploying machine learning models into production environments and monitoring their performance over time.
  • Hands-on experience working on real-world data science projects, applying learned concepts to solve business problems and gain practical experience.

|| Requirements

  • Basic programming knowledge (Python preferred)
  • Familiarity with basic statistics and linear algebra

|| Requirements

  • Basic programming knowledge (Python preferred)
  • Familiarity with basic statistics and linear algebra

|| Choose Full Stack Data Science Course From BIT

Features of BIT Coaching Classes ,Comprehensive Curriculum in BIT ,Hands on Learning ,Expert Faculty Of BIt ,Project Based Approach,Advantages of taking Admission at Bit

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    A Full Stack Data Science course covers the entire data science pipeline, providing a comprehensive understanding of both theoretical and practical aspects. It begins with programming fundamentals, focusing on Python or R and essential libraries like NumPy, pandas, and Matplotlib for data manipulation and visualization. The course delves into database management, teaching SQL and NoSQL for efficient data storage and retrieval. Students learn data cleaning and preprocessing techniques to handle missing values, outliers, and inconsistencies, followed by exploratory data analysis (EDA) using statistical methods and visualizations to uncover patterns. The curriculum includes machine learning, covering supervised and unsupervised algorithms such as linear regression, decision trees, and clustering, with hands-on experience in frameworks like Scikit-learn, TensorFlow, and Keras. Advanced topics include deep learning, natural language processing (NLP), and time series analysis, exploring neural networks, CNNs, and RNNs. Practical skills are emphasized through deploying models using cloud services like AWS, Azure, or Google Cloud, and containerization with Docker. Version control with Git ensures effective collaboration. Throughout the course, real-world projects and case studies provide hands-on experience, preparing students to tackle complex data science challenges and deploy scalable solutions in production environments.


    Data Science Course Near me - 2.webp





    • Python Basic Building
    • Python Keywords and identifiers
    • Comments, indentation, statements
    • Variables and data types in Python
    • Standard Input and Output


    • Operators
    • Control flow: if else elif
    • Control flow: while loop
    • Control flow: for loop
    • Control flow: break & continue
    • Python Data Structures


    • Strings
    • Lists, Lists comprehension
    • Tuples, Sets
    • Dictionary, Dictionary Comprehension


    • Python Functions
    • Python Builtin Functions.
    • Python Userdefined Functions.
    • Python Recursion Functions.


    • Python Lambda Functions.
    • Python Exception Handling, 
    • Logging And Debugging


    • Exception Handling 
    • Custom Exception Handling
    • Logging With Python
    • Debugging With Python
    • Python OOPS
    • Python Objects And Classes
    • Python Constructors
    • Python Inheritance
    • Abstraction In Python
    • Polymorphism in Python
    • Encapsulation in Python


    • File Handling
    • Create 
    • Read
    • Write
    • Append

    • Introduction to NumPy
    • NumPy Array
    • Creating NumPy Array
    • Array Attributes, 
    • Array Methods
    • Array Indexing, 
    • Slicing Arrays
    • Array Operation
    • Iteration through Arrays


    • Introduction to Pandas
    • Pandas Series
    • Creating Pandas Series
    • Accessing Series Elements
    • Filtering a Series
    • Arithmetic Operations
    • Series Ranking and Sorting
    • Checking Null Values
    • Concatenate a Series


    • Data Frame Manipulation
    • Pandas Dataframe 
    • Introduction Dataframe Creation
    • Reading Data from Various Files
    • Understanding Data
    • Accessing Data Frame Elements using Indexing
    • Dataframe Sorting
    • Ranking in Dataframe
    • Dataframe Concatenation
    • Dataframe Joins
    • Dataframe Merge
    • Reshaping Dataframe
    • Pivot Tables, 
    • Cross Tables
    • Dataframe Operations


    • Checking Duplicates
    • Dropping Rows and Columns
    • Replacing Values
    • Grouping Dataframe
    • Missing Value Analysis & Treatment
    • Visualization using Matplotlib
    • Plot Styles & Settings
    • Line Plot, 
    • Multiline Plot
    • Matplotlib Subplots
    • Histogram, Boxplot
    • Pie Chart ,Scatter Plot
    • Visualization using Seaborn
    • Strip Plot ,Distribution Plot
    • Joint Plot, 
    • Violin Plot, 
    • Swarm Plot
    • Pair Plot,
    • Count Plot
    • Heatmap
    • Visualization using Plotly
    • Boxplot
    • Bubble Chart
    • Violin Plot
    • 3D Visualization


    • EDA and Feature Engineering
    • Introduction of EDA
    • Dataframe Analysis using Groupby
    • Advanced Data Explorations

    • Working with SQL Using MySQL 
    • Work Bench / SQL Server"
    • USE, DESCRIBE, 
    • SHOW TABLES
    • SELECT, INSERT
    • UPDATE & DELETE
    • CREATE TABLE
    • ALTER: ADD, MODIFY, DROP
    • DROP TABLE, TRUNCATE, DELETE
    • LIMIT, OFFSET
    • ORDER BY
    • DISTINCT
    • WHERE Clause
    • HAVING Clause
    • Logical Operators
    • Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
    • GROUP BY
    • SQL Primary And Foreign Key
    • Join and Natural Join
    • Inner, Left, Right and Outer joins


    • Advance SQL
    • Subqueries/Nested Queries/Inner Queries
    • SQL Function And Stored Procedures
    • SQL Window Function
    • CTE In SQL
    • Normalization In SQL

    • Basic Math
    • Linear Algebra
    • Probability
    • Calculus
    • Develop a comprehensive understanding of coordinate geometry and linear algebra.
    • Build a strong foundation in calculus, including limits, derivatives, and integrals.

    • Descriptive Statistics
    • Sampling Techniques
    • Measure of Central Tendency
    • Measure of Dispersion
    • Skewness and Kurtosis
    • Random Variables
    • Bassells Correction Method
    • Percentiles and Quartiles
    • Five Number Summary
    • Gaussian Distribution
    • Lognormal Distribution
    • Binomial Distribution
    • Bernoulli Distribution


    • Inferential Statistics
    • Standard Normal Distribution 
    • ZTest
    • TTest
    • ChiSquare Test
    • ANOVA / FTest
    • Introduction to Hypothesis Testing
    • Null Hypothesis
    • Alternet Hypothesis


    • Probability Theory
    • What is Probability?
    • Events and Types of Events
    • Sets in Probability
    • Probability Basics using Python
    • Conditional Probability
    • Expectation and Variance

    • Introduction to Machine Learning
    • Machine Learning Modelling Flow
    • "Supervised and Unsupervised 
    • Types of Machine Learning Algorithms


    • Linear Regression using OLS
    • Introduction of Linear Regression
    • Types of Linear Regression
    • OLS Model
    • Math behind Linear Regression
    • Decomposition Variability
    • Metrics to Evaluate Model
    • Feature Scaling
    • Feature Selection
    • Regularisation Techniques
    • Ridge Regression 
    • Lasso Regression
    • ElastivNet Regression


    • Optimisation Techniques
    • What is Optimisation?
    • Gradient Descent
    • Adagrad Algorithm
    • Adam Algorithm
    • Linear Regression with SGD
    • Prerequisites


    • Introduction to Stochastic Gradient Descent (SGD)
    • Preparation for SGD
    • Workflow of SGD
    • Implementation of SGD on Linear Regression


    • Logistic Regression
    • Maximum Likelihood Estimation
    • "Logistic Regression Using Sigmoid 
    • Activation Function"
    • Performance Metrics 
    • Confusion Matrix
    • Precision, Recall, F1Score
    • Receiver Operating Characteristic Curve


    • KNN
    • Euclidean Distance
    • Manhattan Distance
    • Implementation for KNN


    • SVM
    • Support Vector Regression
    • Support Vector Classification
    • Polynomial Kernel
    • Cost Function
    • GridSerchCV


    • Decision Trees
    • Decision Tree for Classification
    • Decision Tree for Regression
    • ID3 Algorithm
    • CART Algorithm
    • Entropy
    • Gini Index
    • Information Gain
    • Decision Tree: Regression
    • Mean Square Error
    • PrePruning and PostPruning


    • Naive Bayes
    • Introduction to Bayes Theorem
    • Explanation for naive bayes


    • Ensemble Technique
    • Bagging
    • Random Forest Classifier
    • Random Forest Regression
    • Random Forest – Why & How?
    • Feature Importance
    • Advantages & Disadvantages


    • Boosting
    • Bootstrap Aggregating
    • AdaBoost
    • XgBoost
    • Project For Random Forest
    • Project Penguin Classification
    • Project Texi Prediction


    • Kmeans Clustering
    • Prerequisites
    • Cluster Analysis
    • Kmeans
    • Implementation of Kmeans
    • Pros and Cons of Kmeans
    • Application of Kmeans
    • Elbow Method
    • Model building for Kmeans Clustering


    • Hierarchical Clustering
    • Types of Hierarchical Clustering
    • Dendrogram
    • Pros and Cons of Hierarchical Clustering
    • Model building for Hierarchical Clustering


    • DBSCAN Clustering
    • Introduction for DBSCAN Clustering
    • implementation of DBSCAN


    • Principal Components Analysis
    • Prerequisites
    • Introduction to PCA
    • Principal Component
    • Implementation of PCA
    • Case study
    • Applications of PCA
    • Project on PCA


    • Time Series Modelling
    • Understand Time Series Data
    • Visualising Time Series Components
    • Exponential Smoothing
    • ARIMA
    • SARIMA
    • SARIMAX
    • Project on Forecasting
    • Cloud Basics
    • ML on Cloud

    Artificial Neural Network (ANN)

    • Biological and Artificial Neurons
    • Activation Functions
    • Perceptron
    • Feed Forward Network
    • Multilayer Perceptron (MLP)
    • Back Propagation, Deep ANN
    • Optimisation Algorithms
    • Gradient Descent
    • Stochastic Gradient Descent (SGD)
    • MiniBatch Stochastic Gradient Descent
    • Stochastic Gradient Descent with Momentum
    • AdaGrad, RMSProp , Adam
    • Batch Normalisation


    • KERAS
    • What is Keras?
    • How to Install Keras?
    • Why to Use Keras?
    • Different Models of Keras
    • Preprocessing Methods
    • What are the Layers in Keras?


    • Tensorflow 2.0
    • TensorFlow in Realtime Applications
    • Advantages of TensorFlow
    • How to Install TensorFlow
    • TensorFlow 1x vs TensorFlow 2.0
    • Eager Execution in TensorFlow 2.0


    • Convolutional Neural Network(CNN)
    • Introduction to Computer Vision
    • Convolutional Neural Network
    • Architecture of Convolutional network
    • Image as a Matrix, Convolutional Layer
    • Feature Detector & Feature Maps
    • Pooling Layer, Max Pooling
    • Min Pooling, 
    • Avg Pooling
    • Flattening Layer, Padding, Striding
    • Image Augmentation
    • Basics of Digital Images


    • Recurrent Neural Network (RNN)
    • RNN Network Structure
    • Different Types of RNNs
    • Bidirectional RNN
    • Limitations of RNN

    • Natural Language Processing 
    • Part I NLTK
    • What is NLP?
    • Typical NLP Tasks
    • Morphology
    • Sentence Segmentation & Tokenization
    • Pattern Matching with Regular Expression
    • Stemming, Lemmatization
    • Stop Words Removal (English)
    • Corpora/Corpus
    • Context Window – Bigram, Ngram
    • Applications of NLP
    • Introduction to the NLTK Library
    • Processing Raw Text
    • Regular Expression
    • Normalising Text
    • Processing Raw Text – Tokenise Sentences
    • String Processing with Regular Expression, Normalising Text
    • Extracting Features from Text
    • Bag-of-Words(BoW), TF-IDF
    • Similarity score Cosine similarity


    • Computer Vision
    • Image Formation
    • Sampling and Quantisation
    • Image Processing – flipping, cropping, rotating, scaling
    • Image statistics & Histogram
    • Spatial Resolution
    • Gray level/Intensity Resolution
    • Spatial Filtering
    • Convolution
    • Smoothing, Sharpening
    • Color Space Conversion & 
    • Histogram
    • Thresholding for Binarization
    • Morphological Operations
    • Image Gradient
    • Bounding Box
    • Sobel’s Edge Detection Operator
    • Template Matching
    • Image Feature – Keypoint and Descriptor
    • Harris Corner Detector
    • Object Detection with HoG
    • Stream Video Processing with OpenCV

    • Computer Vision
    • Image Formation
    • Sampling and Quantisation
    • Image Processing – flipping, cropping, rotating, scaling
    • Image statistics & Histogram
    • Spatial Resolution
    • Gray level/Intensity Resolution
    • Spatial Filtering
    • Convolution
    • Smoothing, Sharpening
    • Color Space Conversion & Histogram


    • Thresholding for Binarization
    • Morphological Operations
    • Image Gradient
    • Bounding Box
    • Sobel’s Edge Detection Operator
    • Template Matching
    • Image Feature – Keypoint and Descriptor
    • Harris Corner Detector
    • Object Detection with HoG
    • Stream Video Processing with OpenCV

    • Advance NLP
    • "Use Logistic Regression, 
    • Naive Bayes and Word vectors to implement Sentiment Analysis"
    • R-CNN
    • RNN
    • Encoder-Decoder
    • Transformer
    • Reformer
    • Embeddings
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    • Text Generation
    • Named Entity Recognition
    • GRU
    • Siamese Network in TensorFlow
    • Self Attention Model
    • Advanced Machine Translation of Complete Sentences
    • Text Summarization


    • Prompt Engineering
    • Why Prompt Engineering?
    • ChatGPT
    • Few Standard Definitions:
    • Label, Logic
    • Model Parameters (LLM Parameters)
    • Basic Prompts and Prompt Formatting
    • Elements of a Prompt, Context
    • Task Specification
    • Constraints
    • General Tips for Designing Prompts:
    • Be Specific ,Keep it Concise
    • Be Contextually Aware
    • Test and Iterate
    • Prompt Engineering Use Cases
    • Information Extraction
    • Text Summarization
    • Question Answering
    • Code Generation
    • Text Classification
    • Prompt Engineering Techniques
    • N-shot Prompting
    • Zero-shot Prompting
    • Chain-of-Thought (CoT) Prompting
    • Generated Knowledge Prompting

    • Introduction of MLOps
    • What and why MLOps
    • MLOps fundamentals
    • MLOps vs DevOps
    • Why DevOps is not sufficient for MLOps
    • Challenges in traditional ML Pipeline
    • DevOps and MLOps tools and platform
    • What is SDLC?
    • Types of SDLC
    • Waterfall vs AGILE vs DevOps vs MLOps


    • MLOps Foundation
    • Fundamental of Linux for MLOps and data scientist
    • Important Linux Commands
    • Source code managements using GIT
    • GIT configuration and GIT commands
    • YAML for Configuration Writing
    • YAML vs JSON Schema
    • Docker for Containers
    • Docker Basic Command, Dockerhub, Dockerfile
    • Cloud Computing and Cloud Infrastructure
    • Cloud Service Provider- AWS, GCP, AZURE
    • Data Managements and Versioning with DVC
    • Monitoring, Alerting, Retraining With Grafana and
    • prometheus
    • Experiment tracking with MLFLOW
    • Model Serving With BENTOML


    • End to End project implementation with Deployment implementation with Deployment
    • Understanding Machine learning Workflow and Project Setup
    • Project Template Setup with GitHub
    • Modular workflow Introduction and Implementation
    • Understanding the Training Pipeline and Its Components


    • Data Ingestion, Data Transformation Model Trainer Model
    • Evaluation
    • Creating Prediction Pipeline and End Point Creation
    • Continues Integration, Continues Delivery and Continues
    • Training understanding and Project Deployment


    • Prompt Engineering
    • Why Prompt Engineering?
    • ChatGPT
    • Few Standard Definitions:
    • Label
    • Logic
    • Model Parameters (LLM Parameters)
    • Basic Prompts and Prompt Formatting
    • Elements of a Prompt:
    • Context
    • Task Specification
    • Constraints
    • General Tips for Designing Prompts:
    • Be Specific
    • Keep it Concise
    • Be Contextually Aware
    • Test and Iterate
    • Prompt Engineering Use Cases
    • Information Extraction
    • Text Summarization
    • Question Answering
    • Code Generation
    • Text Classification
    • Prompt Engineering Techniques
    • N-shot Prompting
    • Zero-shot Prompting
    • Chain-of-Thought (CoT) Prompting
    • Generated Knowledge Prompting

    • Computer Vison
    • Convolution Neural Networks (CNN)
    • Why CNN? Building an Intuition for CNN
    • CNN, Kernels, Channels, Feature Maps, Stride, Padding
    • Receptive Fields, Image Output Dimensionality Calculations, MNIST Dataset
    • Explorations with CNN
    • MNIST CNN Intuition, Tensorspace.js, CNN Explained, CIFAR 10 Dataset Explorations with CNN
    • Dropout & Custom Image Classification for Cat and Dog Datasets
    • Deployment in Heroku, AWS or Azure


    • CNN Architectures
    • LeNet-5
    • AlexNet, VGGNet
    • Inception, ResNet
    • Data Augmentation
    • Benefits of Data Augmentation
    • Exploring Research Papers
    • Exploring Augmentor


    • Object Detection Basics
    • What is Object Detection?
    • Competitions for Object Detection
    • Bounding Boxes
    • Bounding Box Regression
    • Intersection over Union (IoU)
    • Precision & Recall
    • What is Average Precision?
    • Practical Training using Tensorflow1.x
    • Custom Model Training in TFOD1.x
    • Our Custom Dataset
    • Doing Annotations or labelling data
    • Pretrained Model from Model Zoo
    • Files Setup for Training
    • Export Frozen Inference Graph
    • Inferencing with our trained model in Colab, Training in Local
    • Inferencing with our trained model in Local


    • Practical Training using Tensorflow2.x
    • Introduction to TFOD2.x
    • Using the Default Colab Notebook
    • Google Colab & Drive Setup


    • Visiting TFOD2.x Model Garden
    • Inference using Pretrained Model
    • Inferencing in Local with a pretrained model


    • Practical Object Detection Using YOLO V5
    • Introduction for YoloV5
    • YoloV5 Google Colab Setup
    • Inferencing using Pre-Trained Model


    • Prompt Engineering
    • Why Prompt Engineering?
    • ChatGPT
    • Few Standard Definitions:
    • Label
    • Logic
    • Model Parameters (LLM Parameters)
    • Basic Prompts and Prompt Formatting
    • Elements of a Prompt:
    • Context
    • Task Specification
    • Constraints
    • General Tips for Designing Prompts:
    • Be Specific
    • Keep it Concise
    • Be Contextually Aware
    • Test and Iterate
    • Prompt Engineering Use Cases
    • Information Extraction
    • Text Summarization
    • Question Answering
    • Code Generation
    • Text Classification
    • Prompt Engineering Techniques
    • N-shot Prompting
    • Zero-shot Prompting
    • Chain-of-Thought (CoT) Prompting
    • Generated Knowledge Prompting

    • Generative AI
    • Why are generative models required?
    • Understanding generative models and their significance
    • Generative AI v/s Discriminative Models
    • Recent advancements and research in generative AI
    • Gen AI end-to-end project lifecycle
    • Key applications of generative models


    • Text Preprocessing and Word Embedding
    • Segmentation and Tokenization
    • Change Case, Spell Correction
    • Stop Words Removal, Punctuations Removal, Remove White
    • spaces, Stemming and Lemmatization
    • Parts of Speech Tagging
    • Text Normalization, Rephrase Text
    • One hot encoding, 
    • Index-based encoding
    • Bag of words, 
    • TF-IDF
    • Word2Vec, 
    • FastText
    • N-Grams, Elimo
    • Bert-based encoding


    • Large Language Models(LLM)
    • In-depth intuition of Transformer-Attention all your need Paper
    • Guide to complete transformer tree
    • Transformer Architecture
    • Application and use cases of LLMs
    • Transfer learning in NLP
    • Pre-trained transformer-based models
    • How to perform finetuning of pre trained transformer based models
    • Mask language modeling


    • BERT- Google, GPT- OpenAI
    • T5- Google
    • Evaluations Matrixs of LLMs models
    • GPT-3 and 3.5 Turbo use cases
    • Learn how Chatgpt trained
    • Introduction to Chatgpt- 4


    • Hugging face And its Applications
    • Hugging Face Transformers
    • Hugging face API key generation
    • Hugging Face Transfer learning models based on the state-of-the-art transformer architecture
    • Fine-tuning using a pre-train models
    • Ready-to-use datasets and evaluation metrics for NLP.
    • Data Processing, Tokenizing and Feature Extraction with
    • Standardizing the Pipelining
    • Training and callbacks
    • Language Translation with Hugging Face Transformer


    • Generative AI with LLMs and LLM Powered Applications
    • Text summarization with hugging face
    • Language Translation with Hugging Face Transformer
    • Text to Image Generation with LLM with hugging face
    • Text to speech generation with LLM with hugging face


    • Guide to Open AI and its Ready to Use Models with Application
    • What is OpenAI API and how to generate OpenAI API key?
    • Installation of OpenAI package
    • Experiment in the OpenAI playground
    • How to setup your local development environment
    • Different templates for prompting
    • OpenAI Models GPT-3.5 Turbo DALL-E 2, Whisper, Clip,
    • Davinci and GPT-4 with practical implementation
    • OpenAI Embeddings and Moderation with Practical
    • Implementation of Chat completion API,


    • Functional calling and Completion API
    • How to manage the Tokens
    • Different Tactics for getting an Optimize result
    • mage Generation with OpenAI LLM model
    • Speech to text with OpenAI
    • Use of Moderation for content complies with OpenAI
    • Understand rate limits, error codes in OpenAPI
    • OpenAI plugins connect ChatGPT to third-party applications.
    • How to do fine-tuning with custom data
    • Project: Finetuning of GPT-3 model for text classification
    • Project: Telegram bot using OpenAI API with GPT-3.5 turbo
    • Project: Generating YouTube Transcript with Whisper
    • Project: Image generation with DALL-E
    • Prompt Engineering Mastering with OpenAI


    • Introduction to Prompt Engineering
    • Different templates for prompting
    • Prompt Engineering: What & Why?
    • Prompt Engineering & ChatGPT Custom Instructions
    • The Core Elements Of A Good Prompt
    • Which Context Should You Add?
    • Zero- One- & Few-Shot Prompting
    • Using Output Templates
    • Providing Cues & Hints To ChatGPT
    • Separating Instructions From Content
    • Ask-Before-Answer Prompting
    • Perspective Prompting
    • Contextual Prompting
    • Emotional Prompting
    • Laddering Prompting
    • Using ChatGPT For Prompting
    • Find Out Which Information Is Missing
    • Self-evaluative Prompting
    • ChatGPT-powered Problem Splitting
    • Reversing Roles
    • More Prompts & Finding Prompt Inspirations
    • Super Prompts Like CAN & DAN


    • Vector database with Python for LLM Use Cases
    • Storing and retrieving vector data in SQLite
    • Chromadb local vector database part1 setup and data insertion
    • Query vector data
    • Fetch data by vector id
    • Database operation: create, update, retrieve, deletion, insert and
    • update
    • Application in semantic search
    • Building AI chat agent with langchain and openai
    • Weviate Vector Database
    • Pinecone Vector Database


    • Hands-on with LangChain
    • Practical Guide to LlamaIndex with LLMs
    • Bonus: Additional Productive Tools to Explore
    • Chainlit ( async Python framework)
    • LIDA (Automatic Generation of Visualizations and
    • Infographics)
    • Slidesgo ( AI Presentation Maker )
    • Content Creation (Jasper, Copy.ai, Anyword)
    • Grammar checkers and rewording tools (Grammarly, Wordtune,
    • ProWritingAid)
    • Video creation (Descript, Wondershare Filmora, Runway)
    • Image generation (DALL·E 2, Midjourney)
    • Research (Genei, Aomni)

     Creating a structured framework for data science case studies helps in effectively presenting your work and its impact. Here’s a comprehensive framework you can follow:

     

    • Introduction
    • Overview: Provide a brief introduction to the project, including its objectives and significance.
    • Problem Statement: Clearly define the problem or opportunity your project aims to address.


    • Data Collection and Pre-processing
    • Data Sources: Describe where the data came from and its characteristics (structured/unstructured).
    • Data Cleaning: Detail the steps taken to clean the data, handle missing values, and address outliers.
    • Feature Engineering: Explain how features were selected or engineered to improve model performance.


    • Exploratory Data Analysis (EDA)
    • Summary Statistics: Present key statistics and distributions of the data.
    • Data Visualization: Use charts, graphs, and plots to explore relationships and patterns in the data.
    • Insights: Highlight any initial insights gained from the EDA phase.


    • Machine Learning or Statistical Models
    • Model Selection: Justify the choice of machine learning algorithms or statistical models used.
    • Model Training: Explain how models were trained and validated using appropriate techniques (e.g., cross-validation).
    • Hyperparameter Tuning: Discuss the process of optimizing model hyperparameters for better performance.


    • Evaluation Metrics
    • Performance Metrics: Specify the metrics used to evaluate model performance (e.g., accuracy, precision, recall, F1-score).
    • Benchmarking: Compare your model’s performance against baseline models or industry standards.


    • Results
    • Model Performance: Present the results of your models, including metrics and any visualizations that support your findings.
    • Key Findings: Summarize the main findings and insights derived from your analysis.


    • Deployment and Implementation
    • Deployment Strategy: Describe how the model or solution was deployed in a real-world environment.
    • Integration: Discuss any challenges faced during deployment and how they were overcome.
    • Scalability: Address the scalability of your solution and its potential for future growth.


    • Impact and Conclusion
    • Business Impact: Quantify the impact of your project in terms of business outcomes or improvements.
    • Lessons Learned: Reflect on challenges encountered and lessons learned during the project.
    • Future Work: Suggest potential future enhancements or extensions to your work.


    • Documentation and Presentation
    • Document Structure: Ensure clarity and coherence in presenting each section of the case study.
    • Visual Aids: Use visuals (charts, graphs) to enhance understanding and convey key points effectively.
    • Narrative Flow: Create a compelling narrative that guides the reader through your data science journey.


    • References and Acknowledgments
    • Data Sources: Provide citations or acknowledgments for datasets, tools, or libraries used.
    • Contributors: Acknowledge team members, advisors, or stakeholders who contributed to the project.

Get in touch

|| Tools to master

Full stack data science tools , data science tools ,docker ,git ,aws ,openNN ,MySQL ,Tableau ,Open Ai ,Generative Ai ,Bard

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placement report placement report

|| Skills To Master

Full Stack Data Science Course Skills to Master , PYthon ,Descriptive statistics,data visualization ,mathematical modelling ,machine learning Algorithms ,Linear algebra

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|| Future Scope and Market Demand of Full Stack Data Science Course

Future Scope Full Stack Data Science ,Future Scope of Full stack Data Science in India ,interdisciplinary applications ,technological advancements ,increased data generation ,emphasis on data driven decision making

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|| Career Option and Job Opportunities in India

A full stack data science course equips individuals with comprehensive skills across the entire data science lifecycle, from data collection and cleaning to model deployment and monitoring. This training prepares individuals for various career options and job opportunities in India, which is experiencing a burgeoning demand for data science professionals across multiple industries. Here's an overview of the career paths and opportunities available:

 

  • Data Scientist: Analyze complex data sets to provide insights, build predictive models, and solve business problems.
  • Skills Needed: Statistics, machine learning, data visualization, programming (Python/R), data wrangling.
  • Data Analyst: Interpret data, analyze results using statistical techniques, and provide ongoing reports.
  • Skills Needed: Excel, SQL, data visualization tools (Tableau, Power BI), basic statistical knowledge.
  • Machine Learning Engineer: Design, implement, and maintain machine learning models and systems.
  • Skills Needed: Advanced programming (Python, Java, C++), machine learning frameworks (TensorFlow, PyTorch), software engineering.
  • Data Engineer: Develop, construct, test, and maintain architectures such as databases and large-scale processing systems.
  • Skills Needed: SQL, NoSQL, Hadoop, Spark, data pipeline tools, ETL (Extract, Transform, Load).
  • Business Intelligence (BI) Developer: Develop and manage BI solutions, create reports and dashboards to support business decision-making.
  • Skills Needed: BI tools (Tableau, Power BI), SQL, understanding of business processes.
  • AI Research Scientist: Conduct research in artificial intelligence and machine learning, develop new algorithms and models.
  • Skills Needed: Deep learning, advanced mathematics, programming, research methodologies.
  • Big Data Specialist: Handle large datasets, perform data mining, and develop scalable data processing systems.
  • Skills Needed: Hadoop, Spark, NoSQL databases, programming (Python, Scala, Java).

A full stack data science course offers a robust foundation for various career paths in India’s thriving data science landscape. With the right skills and qualifications, professionals can explore numerous opportunities across diverse industries, contributing to significant business and technological advancements.

|| Job Roles of Full Stack Data science

Full Stack Data Science Course Skills to Master ,Python ,descriptive statistics ,data visualization ,machine learning Algorithms ,Programming Language ,mathematical modelling

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|| The average salary for full-stack data scientists in India

Salaries in Data Science in India

Salaries in the data science field can vary based on factors such as experience, location, and the specific company. Here’s an overview of average annual salaries:

  • Entry-Level (0-2 years)
  • Data Scientist: ₹6,00,000 - ₹8,00,000
  • Data Analyst: ₹3,50,000 - ₹6,00,000
  • Machine Learning Engineer: ₹5,00,000 - ₹8,00,000
  • Data Engineer: ₹4,00,000 - ₹7,00,000
  • BI Developer: ₹4,00,000 - ₹6,00,000
  • Mid-Level (2-5 years)
  • Data Scientist: ₹8,00,000 - ₹15,00,000
  • Data Analyst: ₹6,00,000 - ₹10,00,000
  • Machine Learning Engineer: ₹8,00,000 - ₹14,00,000
  • Data Engineer: ₹7,00,000 - ₹12,00,000
  • BI Developer: ₹6,00,000 - ₹10,00,000
  • Senior-Level (5+ years)
  • Data Scientist: ₹15,00,000 - ₹25,00,000+
  • Data Analyst: ₹10,00,000 - ₹15,00,000
  • Machine Learning Engineer: ₹14,00,000 - ₹20,00,000+
  • Data Engineer: ₹12,00,000 - ₹20,00,000+
  • BI Developer: ₹10,00,000 - ₹15,00,000
  • Top-Level Roles
  • Data Architect: ₹20,00,000 - ₹30,00,000+
  • AI/ML Research Scientist: ₹20,00,000 - ₹35,00,000+

Factors Influencing Salaries

  • Experience: More experienced professionals command higher salaries.
  • Location: Salaries are typically higher in metropolitan areas like Bangalore, Mumbai, Delhi, and Hyderabad.
  • Company: Multinational corporations and tech giants tend to offer higher salaries compared to startups.
  • Skills and Specialization: Advanced skills in machine learning, big data technologies, and cloud computing can significantly boost earning potential.
  • Education: Advanced degrees (Master’s, Ph.D.) and certifications from reputed institutions can enhance salary prospects.

Overall, data science is a lucrative and growing field in India, offering diverse opportunities and competitive salaries for professionals with the right skills and experience.

Placement in Vadodara from BIT ,Resume Building at BIT ,Profile Enhancement at BIT ,Interview and placement support at BIT

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|| Some Prominent Companies in India that use Full Stack Data Science Course

In India, numerous companies across various sectors are leveraging Full Stack Data Science to drive their business operations, enhance decision-making processes, and gain competitive advantages. Here are some notable companies:


  • Technology Companies
  • Infosys: Utilizes data science for optimizing internal processes, enhancing client services, and driving innovation in technology solutions.
  • Tata Consultancy Services (TCS): Implements data science to offer advanced analytics, machine learning, and AI solutions to its global clients.
  • Wipro: Employs data science to improve service delivery, develop predictive models, and provide data-driven insights for clients across industries.
  • Tech Mahindra: Leverages data science to enhance customer experience, optimize operations, and develop AI-based applications.
  • E-commerce
  • Flipkart: Uses data science for customer behavior analysis, personalized recommendations, inventory management, and sales forecasting.
  • Amazon India: Implements data science for logistics optimization, dynamic pricing, fraud detection, and personalized shopping experiences.
  • Myntra: Utilizes data science to enhance customer engagement, optimize supply chain management, and improve product recommendations.
  • Finance and Banking
  • HDFC Bank: Leverages data science for credit scoring, fraud detection, customer segmentation, and personalized marketing.
  • ICICI Bank: Uses data analytics to improve risk management, enhance customer experience, and develop financial products.
  • State Bank of India (SBI): Implements data science for optimizing operations, improving customer service, and developing predictive models for various banking functions.
  • Healthcare
  • Apollo Hospitals: Employs data science for patient care optimization, predictive analytics in healthcare, and operational efficiency.
  • Practo: Uses data science to improve healthcare services, enhance patient engagement, and develop data-driven healthcare solutions.
  • Telecommunications
  • Reliance Jio: Utilizes data science for network optimization, customer analytics, and predictive maintenance.
  • Bharti Airtel: Implements data science for improving customer experience, optimizing operations, and enhancing service delivery.
  • Vodafone Idea: Leverages data science to enhance network performance, develop customer insights, and drive marketing strategies.
  • Retail
  • Reliance Retail: Uses data science for inventory management, customer behavior analysis, and sales optimization.
  • Future Group: Implements data science for supply chain optimization, personalized marketing, and enhancing customer experience.
  • Startups
  • Ola: Leverages data science for ride optimization, driver and passenger matching, and dynamic pricing strategies.
  • Swiggy: Utilizes data science to optimize delivery routes, enhance customer recommendations, and manage supply chains.
  • Zomato: Employs data science for personalized recommendations, optimizing delivery times, and analyzing customer feedback.
  • Byju's: Uses data science to personalize learning experiences, track student progress, and optimize educational content.
  • Government and Public Sector
  • Digital India Initiative: Employs data science for efficient implementation and analysis of various digital transformation projects.
  • National Informatics Centre (NIC): Uses data science to support e-governance, enhance public service delivery, and develop data-driven government policies.

These companies represent a broad spectrum of industries in India that are actively using Full Stack Data Science to drive innovation, optimize operations, and enhance customer experiences. The diverse application of data science across these sectors highlights its critical role in modern business practices.

|| Top Hiring Companies

Hiring Companies ,Top Companies ,Job Placement ,Patterns,Cognizant,Ananta ,Tech Mahindra ,Rapido ,Accenture ,Top Hiring Companies at BIT , Top Placement Companies at BIT

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|| Get  Full Stack Data Science Certification

Three easy steps will unlock your Full Stack Data Science Certification:

 

  • Finish the online / offline course of Full Stack Data Science Course and the Assignment
  • Take on and successfully complete a number of industry-based Projects
  • Pass the Full Stack Data Science   certification exam

 

The certificate for this Full Stack Data Science   course will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile.

 

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At BIT Institute, we offer a comprehensive Full stack Data Science Program that ensures job placement for our students. Our program is designed with an industry-aligned curriculum, covering key aspects of data science, machine learning, and AI, alongside practical tools such as Python, SQL, and advanced analytics platforms. With over 22 years of experience in IT training, BIT emphasizes hands-on learning through real-world projects, case studies, and capstone projects. Our dedicated placement cell works closely with top-tier companies to secure job interviews for our graduates, ensuring that they are job-ready upon completion of the program.

|| Empowering Your Career Transition From Learning To Leading

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Megha Bhatt

Megha Bhatt, an ML Engineer at Cognizant, demonstrates prowess by leveraging unique tools such as Alteryx for advanced data blending and Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

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Darshna Dave

Darshna Dave, excelling as a Data Analyst at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

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Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-Business Analytics course, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

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Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post learning Data Analytics course, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

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Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ after studying a Full-Stack Data science course from BIT. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, which showcases his expertise in essential data processing tools.

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Vanshika Patel

Vanshika Patel, demonstrating mastery as a Sr. ML Engineer at Genpact, demonstrates proficiency in essential tools such as Python, TensorFlow, and scikit-learn. Her adept use of these critical software contributes to effective machine learning solutions.

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Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ post our IT institute. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, showcasing expertise in essential data processing tools.

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|| Frequently asked question

Full Stack Data Science refers to the end-to-end process of developing data-driven solutions, from data acquisition and preprocessing to model building, deployment, and maintenance. It encompasses a broad range of skills and technologies required to handle the entire data science pipeline.

This course is suitable for individuals interested in pursuing a career in data science, machine learning, or related fields. It caters to beginners with little to no experience in data science as well as professionals looking to expand their skill set or transition into data science roles.

Most reputable Full Stack Data Science courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

After completing the course, you may continue to have access to resources such as course materials, alumni networks, career services, and professional development opportunities. Some providers offer lifetime access to course materials or alumni benefits to support your continued growth and success.

Yes, many Full Stack Data Science courses are available online, offering flexibility in terms of timing and location. Online courses often include video lectures, interactive assignments, and discussion forums to facilitate learning.

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

You will learn essential skills such as programming in Python/R, data wrangling, exploratory data analysis (EDA), machine learning algorithms, data visualization, big data technologies, database management, and model deployment.

This course is suitable for anyone interested in analyzing and interpreting data to make informed decisions. It's ideal for aspiring data scientists, analysts, researchers, and professionals looking to transition into data-driven roles.

While not always required, familiarity with programming basics can be beneficial. Most courses start with foundational programming concepts and gradually progress to more advanced topics in data science.

Projects typically include real-world scenarios where you'll apply data science techniques to solve problems. Examples could range from predictive analytics in finance to sentiment analysis in social media data.

This varies depending on the course format (full-time, part-time, self-paced) and your prior knowledge. Generally, expect to spend several hours per week on lectures, assignments, and projects to maximize learning.

Our student projects

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Kunj Patel
Project Title: Online Payment Fraud Detection
Data Science
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Vidit Modi
Project Title: Brain Tumor Classification
Data Science
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Project Title: Air Quality Project
Data Science
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Tirth Modi
Project Title: Abalone Age Prediction
Data Science
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Vidit Modi
Project Title: Pockemon Machine Learning Project
Data Science
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VACHA HIREN SONI
Project Title: Brain Tumor Classification
Data Science

Our student projects

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Kunj Patel
Project Title: Online Payment Fraud Detection

Data Science
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Vidit Modi
Project Title: Brain Tumor Classification

Data Science
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Rutvik Panchal
Project Title: Air Quality Project

Data Science
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Tirth Modi
Project Title: Abalone Age Prediction

Data Science
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Vidit Modi
Project Title: Pockemon Machine Learning Project

Data Science
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VACHA HIREN SONI
Project Title: Brain Tumor Classification

Data Science

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