Full Stack Data Science Course

|| Full Stack Data Science Certification Course

A Data Science Training course is a comprehensive program designed to equip learners with the complete set of skills needed to handle end-to-end data science projects. Data Science course covers a broad spectrum of topics, starting with foundational concepts in mathematics and statistics that underpin data analysis. Students will learn programming languages such as Python, essential for data manipulation and analysis. The curriculum includes extensive training in data preprocessing, cleaning, and exploratory data analysis, ensuring that participants can prepare raw data for advanced analysis. In the core data science modules, learners dive into machine learning algorithms, covering both supervised and unsupervised techniques, and explore deep learning with neural networks. Practical applications are emphasized through hands-on projects and real-world case studies, where students apply their knowledge to build predictive models, conduct natural language processing, and work with time-series data.

Additionally, the course includes training in data visualization tools and techniques using libraries like Matplotlib, Seaborn, and Plotly, as well as business intelligence tools like Tableau and Power BI. Students also gain exposure to data engineering concepts, learning how to design and implement data pipelines, and working with databases using SQL and NoSQL technologies. The full stack aspect is completed with instruction in deploying machine learning models and data applications using cloud services such as AWS, Azure, and Google Cloud. This involves learning about containerization with Docker, orchestration with Kubernetes, and creating APIs for model serving. By the end of the course, students are well-prepared to handle every stage of a data science project, from data collection and cleaning to model building and deployment, making them highly valuable in the data-driven job market.

Learners who complete the Intro to Data Science course will have the basic information and abilities needed to get started in the rapidly expanding subject of data science. Students will gain an understanding of the life cycle of data science projects, examine the analytics landscape, and become familiar with key tools and technologies by starting with an extensive introduction to data science course. The fundamental ideas of probability and statistics are then covered in depth throughout the course, which are essential for understanding and analyzing data.

Additionally, students will get hands-on experience with Python programming by working with its built-in functions, classes, control and loop statements, and data structures. Using Pandas, Matplotlib, Seaborn, and other libraries, the course guides students through data manipulation, analysis, and visualization with an emphasis on hands-on learning. Aspiring data scientists might benefit from advanced classes that include predictive modeling, time series forecasting, and an introduction to machine learning. Participants will have a solid understanding of the data science process and be equipped to handle data difficulties in the real world by the end of the course.

Please contact the nearest BIT training institute or send an email to inquiry@bitbaroda.com with any additional questions you may have regarding our Full Stack Data Science training course. We offer a free demo by calling us at +91-9328994901. We offer top-notch Full Stack Data Science Python classes in Vadodara-Sayajigunj, Vadodara - Waghodia Road, Vadodara - Manjalpur, Ahmedabad, Anand, and Nadiad.

|| 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

Advantages of BIT Coaching classes ,Interactive learning in BIT ,Certification and assessment in BIT ,Interactive session amd Group Activity at BIt ,Experienced instructors at BIT ,One on one Monitoring in BIT

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.

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.

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|| Tools to master

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

|| Skills To Master

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

|| 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

|| 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

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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.

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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

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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.

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

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.

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.

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.

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.

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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