Data Science Bootcamp
A comprehensive training that addresses essential components required for achieving success in the field of Data Science and Artificial Intelligence.
Starting Date: 5th January 2024
Complete Our Bootcamp and Become Job Ready
We help our successful candidates find Jobs and Internships, and also provide guidance in discovering Freelance opportunities.









About This Program
Acquire the essential data science expertise required to become a highly desirable professional capable of creating substantial value in any organization.
Comprehensive Learning Journey
Embark on a learning journey that starts with the fundamentals of data and progresses to advanced AI concepts.
Mastery of Essential Tools
Immerse yourself in widely-used data tools and software applications.
Deep Dive into Machine Learning
Acquire a deep understanding of both foundational and advanced machine learning techniques.
Hands-On Learning
Gain practical experience through real-world industry projects, putting your skills to the test.
Professional Advancement
Receive expert guidance on crafting resumes, acing interviews, and effectively showcasing your abilities to potential employers.






EXCEL IN YOUR CAREER
WITH DATA SCIENCE BOOTCAMP
Strengthen your data skills and excel in your existing career.
Excel in Your Career
with Data Science Bootcamp
Strengthen your data skills and excel in your existing career







Trusted by Leading Companies










How to become a Data Scientist?
About this Program
Acquire the essential data science expertise required to become a highly desirable professional capable of creating substantial value in any organization.
Start to Finish Learning: Begin with data basics and rise to advanced AI topics.
Master Industry Tools: Dive into popular data tools and software.
All About Machine Learning: Grasp foundational to advanced ML techniques.
Practical Experience: Work on industry projects for hands-on practice.
Career Support: Guidance on resumes, interviews, and showcasing your skills.
Key Features
Project Based Learning
Apply theory to real-world projects for practical proficiency.
Expert Instructors
Learn from industry professionals to gain practical insights.
Industry Ready Portfolio
Craft a portfolio that showcases your industry-ready data science projects.
Guidance & Support
Receive guidance and assistance in your pursuit of data science career opportunities. Skilled teaching assistants are there for you.
Speaker Sessions
Regular speaker series with industry professionals and hiring manager
1-on-1 Coaching & Mentorship
Receive individualized mentorship to navigate your way through your data science career.
Earn a Verified Certificate of Completion
Earn a data science certificate, verifying your skills. Step into the market with a proven and trusted skillset.

Job Guidance and Placement
Go from Learning to Earning
Interview Preparation
Interview workshops and mock interviews.
Job Search
We teach you strategies for unconventional job hunting
Community Building
We support you to build your network with data science professionals
Resume Building
How to build a professional CV and LinkedIn profile building
Curriculum
Basics of Data Science
Module : Python for AI and ML
Module : Exploratory Data Analysis (EDA) and Machine Learning
Module: NLP,LLMs, Computer Vision And MLOps
Basics of Data Science
Basic Data and Statistical Concepts
- Data Literacy
- Statistical Foundations
- Descriptive Statistics
- Statistical Inference and Sampling Techniques
- Regression Analysis
Data analysis using chatGPT
- Basic Data and Statistical Concepts
- Data Cleaning, Preparation, and Management
- Data Processing
PowerBI
- Importing data from diverse sources
- Creating a basic report with various visuals
- Data analysis, manipulation and filtering in Power BI
- Creating measures and calculated columns
- Filtering data in a report
- Using slicers, dynamic filtering of a report
- Introduction to DAX
- Using DAX to solve complex data problems
- Visualizing cross sections of data
SQL
- Introduction to SQL
- Filtering, Sorting, and Aggregating Data
- Table Joins and Case Statements
- Advanced Query Techniques
- Window Functions and Data Modification
- Creating New Tables
- Advanced Topics and Artificial Intelligence
- Date Variables and Artificial Intelligence
Prompt engineering
- Basics of Prompt Engineering
- Definition and importance of prompt engineering.
- The relationship between prompts and AI outputs.
- Applications in Data Science and Machine Learning
- Case studies illustrating the impact of prompt engineering in these fields.
GIS
- Introduction of spatial analysis
- Introduction of QGIS
- Creating shapefiles. (Point, line and polygon)
- Learning basic Cartography
- Learning basic vector analysis tools.
- Creating heatmaps
- Using Google maps to create maps in QGIS
- Introduction of Raster file
- Real-world examples of GIS in various industries and fields
Mathematical Foundation
- Linear algebra
- Probability
- Statistics
Module : Python for AI and ML
Installation
- Introduction to the Python programming language and its applications
- Setting up the Python environment: installation of Python and necessary libraries
- Configuring the development environment: IDEs, text editors, and Jupyter Notebook
Python Basics I
- Introduction to Python: history, features, and advantages
- Expressions and operators: arithmetic, assignment, comparison, and logical
- Understanding type() function and type inference
- Introduction to data structures: lists, tuples, and dictionaries
Python Basics II
- Recap of Python basics
- Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
- Using comparison operators: equal to, not equal to, greater than, less than, etc.
- Logical operators: and, or, and not
- Exploring advanced data types: sets and strings manipulation.
Expressions, Conditional Statements & For Loop
- Evaluating expressions: operator precedence and associativity
- Introduction to conditional statements: if, elif, and else
- Executing code based on conditionals.
- Understanding the flow of control in conditional statements
- Iteration using the for loop: range(), iteration over lists, and strings.
While loop, Break and Continue Statements, and Nested Loops
- Working with while loop: syntax, conditions, and examples
- Combining loops and conditionals
- Using the break statement to exit loops prematurely.
- Utilizing the continue statement to skip iterations.
- Implementing nested loops for complex iterations
Functions
- Introduction to functions: purpose, advantages, and best practices
- Defining and calling user-defined functions
- Parameters and arguments: positional, keyword, and default values
- Return statement and function output.
- Variable scope and lifetime
- Function documentation and code readability
Exception Handling and File Handling
- Understanding exceptions: errors, exceptions, and exception hierarchy
- Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
- Raising exceptions and creating custom exception classes
- File handling in Python: opening, reading, writing, and closing files.
- Working with different file modes and file objects
Python Modules: NumPy,Pandas and Matplotlib
- Introduction to the NumPy module: features and applications
- Working with multidimensional arrays: creation, indexing, slicing, and reshaping
- Performing element-wise operations: arithmetic, logical, and statistical
- Overview of the Matplotlib module: data visualization and plotting
- Customizing plots: line properties, markers, colors, labels, and legends
Advanced Topics
- Introduction to Kaggle platform: features and benefits
- Leveraging Kaggle for real-life datasets: data exploration, analysis, and visualization
- Introduction to machine learning modules on Kaggle: scikit-learn, TensorFlow, and PyTorch
- Overview of running machine learning experiments on Kaggle
- Resources for further learning and exploration
Module : Exploratory Data Analysis (EDA) and Machine Learning
Introduction and Missing Value Analysis
- Introduction to Exploratory Data Analysis (EDA)
- Importance of EDA in data analysis
- Steps involved in EDA
- Handling missing values: identification, analysis, and treatment strategies • Imputation techniques for missing values
Data Consistency, Binning, and Outlier Analysis
- Data consistency checks using fuzzy logic
- Binning and discretization techniques for continuous variables
- Outlier detection and analysis methods
- Handling outliers: techniques for treatment or removal
Feature Selection and Data Wrangling
- Importance of feature selection in EDA
- Feature selection techniques: filter methods, wrapper methods, and embedded methods
- Data wrangling: cleaning and transforming data for analysis
- Handling categorical variables: encoding techniques
Inference, Hypothesis Testing, and Visualization
- Inference and hypothesis testing in EDA
- Common statistical tests: t-test, chi-square test, ANOVA, etc.
- Visualization techniques for EDA: histograms, box plots, scatter plots, etc.
- Hands-on practical session for complete EDA using a dataset
Machine Learning Performance Metrics and Naive Bayes
- Evaluation metrics for classification problems: accuracy, precision, recall, F1 score, etc.
- Introduction to Naive Bayes algorithm and its applications
- Implementing Naive Bayes for classification tasks
Logistic Regression, SVM, Decision Trees, and Random Forests
- Logistic Regression: theory, interpretation, and applications
- Support Vector Machines (SVM): concepts, kernels, and use cases
- Decision Trees: construction, pruning, and interpretability
- Random Forests: ensemble learning and feature importance
- Bagging and Boosting: techniques for improving model performance
Hyperparameter Tuning, PCA, and SVD
- Hyperparameter tuning techniques: grid search, random search, and Bayesian optimization
- Principal Component Analysis (PCA): dimensionality reduction and feature extraction
- Singular Value Decomposition (SVD): applications in matrix factorization and data compression
Clustering Introduction, Partitioning Algorithms, and Cluster Evaluation
- Introduction to clustering: unsupervised learning technique
- Partitioning algorithms: K-means, K-medoids
- Hierarchical clustering: agglomerative and divisive approaches
- Density-based clustering: DBSCAN, OPTICS
- Cluster evaluation metrics: silhouette coefficient, Davies-Bouldin index
Regression and Evaluation of Regression Methods
- Introduction to regression analysis
- Linear regression: assumptions, interpretation, and model evaluation • Evaluation metrics for regression: mean squared error, R-squared, etc.
- Other regression methods: polynomial regression, ridge regression, lasso regression
Introduction to Time Series and Time Series Forecasting
- Concepts and characteristics of time series data
- Time series components: trend, seasonality, and noise
- Popular time series forecasting models: ARIMA, SARIMA, exponential smoothing • Implementing time series forecasting models
Models and Hyperparameter Tuning
- Evaluation metrics for time series forecasting: mean absolute error, mean absolute percentage error, etc.
- Cross-validation techniques for time series data
- Hyperparameter tuning for time series models
Module: NLP,LLMs, Computer Vision And MLOps
Introduction to Natural Language Processing (NLP) and Large Language Models (LLMs)
- Overview of Natural Language Processing (NLP)
- Evolution of Large Language Models (LLMs)
- Importance and Applications of NLP and LLMs
Fundamentals of NLP
- Linguistic Concepts
- Tokenization and Text Preprocessing
- Part-of-Speech (POS) Tagging
- Named Entity Recognition (NER)
- Sentiment Analysis
- Text Classification
- Word Embeddings and Language Representations
Introduction to Large Language Models
- The Transformer Architecture
- Attention Mechanisms
- GPT, BERT, and Other Key Models
- Pretraining and Fine-Tuning Techniques
- Evaluation Metrics and Benchmarks
Practical Applications of NLP and LLMs
- Chatbots and Conversational AI
- Text Summarization
- Machine Translation
- Content Generation and Creative Writing
- Question Answering Systems
- Semantic Search and Text Mining
Ethical Considerations and Challenges
- Bias and Fairness
- Privacy and Security
- Model Interpretability and Explainability
- Environmental Impact and Computational Requirements
Hands-On Exercises
- Getting Started with NLP Libraries (spaCy, NLTK, Hugging Face Transformers)
- Building a Simple Text Classifier
- Fine-Tuning a Large Language Model for a Specific Task
- Evaluating Model Performance and Error Analysis
Future Trends and Opportunities in NLP and LLMs
- Multimodal Models and Human-AI Interaction
- Low-Resource Languages and Transfer Learning
- Knowledge-Enhanced Language Models
- Efficient Training and Deployment Techniques
Computer Vision
- Cascade and HOG classifiers to detect faces
- Face detection using OpenCV and Dlib library
- Detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
- KCF and CSRT algorithms to perform object tracking
- convolutional neural networks and implement them using Python and TensorFlow
- Detect objects in images in videos using YOLO, one of the most powerful algorithms today
- Recognize gestures and actions in videos using OpenCV
- Create hallucinogenic images with Deep Dream
- Create images that don’t exist in the real world with GANs (Generative Adversarial Networks)
Reinforcement Learning
- Fundamentals of Reinforcement Learning
- Sample-based Learning Methods
- Prediction and Control with Function Approximation
Stable Diffusion Models
- Fundamentals of Diffusion Models
- Stable Diffusion in Practice
- Methods, Jobs and Tools of Stable Diffusion
Machine Learning Operations(MLOps)
- Github Actions
- Airflow
- Kubernetes
- MLFlow
- ML System Design
- API Building (Flask/FastAPI)
- Cloud Services (AWS/Azure)
- WandB
-10+ projects in 6 months
-International speakers and mentors for guided projects
-Industry level data sets and projects
-Continuous practice with real world case studies with data analytics
-Skills demonstration on data cleaning, data analysis, data visualization
-Email writing
-Logic and critical thinking
-Reporting writing
-LinkedIn optimisation
-Presentation and visual communication
-Resume,CV and cover letter writing
-Acing interviews
-Personal branding
-Global market understanding
-One on one mentorship
MONTH 1: Data Literacy & Excel Foundation
Learning Outcomes
1. Become familiar with and demonstrate clarity in understanding of basic data and statistical concepts.
2. Enhance understanding of mathematical foundations and operations for data science.
3. Become proficient in data cleaning, data processing, and data management with Microsoft Excel.
4. Become acquainted with PowerBI for data visualization.

Data Literacy
• Data Types
• Data Life Cycle

Excel
• Data Cleaning
• Data Preparation

Excel Advanced
• Pivot Tables
• Basic Analysis

Data Analytics and Visualization on PowerBI
MONTH 2: SQL Foundation
Learning Outcomes
- Identify a subset of data from a column or set of columns and write an SQL query to limit to those results.
- Use SQL commands to filter, sort, and summarize data.
- Create an analysis table from multiple queries using the UNION operator.
- Manipulate strings, dates, and numeric data using functions to integrate data from different sources into fields with the correct format for analysis.

Selecting and Retrieving Data with SQL
• Statistical Foundation

Subqueries and Joins in SQL
• Statistical Foundation

Filtering, Sorting, and Calculating Data with SQL
• Mathematical Foundation

Modifying and Analyzing Data with SQL
• Mathematical Foundation
MONTH 3: Python
Learning Outcomes
- Get familiar with basic Python syntax, data types, operators, and expressions.
- Learn about for and while loops, break and continue statements, and functions.
- Learn to work with Numpy and Matplotlib
- Learn about handling exceptions, raising exceptions, and custom exceptions.

Introduction to Python
• Setting up a development environment
• Basic Python syntax and data types
Basic Operators and Expressions
• Basic operators and expressions
• Control structures: if else case statements

Loops
• For and while loops
• Break and continue statements
Functions
• Defining and calling functions

Working with Data
•Lists and tuples
•Dictionaries and sets
•File handling
Modules: Numpy & Matplotlib
• Modules and the import statement
• Plotting

Exception Handling
• Handling exceptions
• Raising exceptions
• Custom exceptions
Review and Practice
MONTH 4: Machine Learning
Learning Outcomes
- Get acquainted with the meaning of Machine Learning along with relevant case studies
- Delve into Feature Engineering
- Get familiar with Statistical Models and Tree Based Models
- Become acquainted with GIS for Spatial Analysis

Introduction to ML
• What is ML? (Demystifying the buzz words such as ML, AI, DS and DL)
• Building Blocks for ML & Real World Examples
• Exploratory Data Analysis

Feature Engineering
• Data Scaling
• Identifying important features
• Creating new features

Bagging and Boosting
• Bayes’ Theorem
• Naive Bayes
• Decision Tree

GIS & Remote Sensing
MONTH 5: Machine Learning Engineering & Data Analytics
Learning Outcomes
- Learn how to deal with Regression Models
- Learn about Neural Networks
- Get an insight into Unsupervised Learning

Regression Models
• Linear Regression
• Logistic Regression
• SVM

Neural Network:
• Perceptron
• Artificial Neural Networks
• Intro to Deep Neural Nets

Unsupervised Learning
• K-Means Clustering
• Hierarchical clustering
• Principal Component Analysis

AutoML
• Auto train the model, fine-tune it,
and evaluate it on a given dataset
• Learn libraries like PyCaret and H2O
MONTH 6: NLP, Computer Vision & Job Market Preparation
Learning Outcomes
- Gain familiarity with major NLP concepts.
- Create your third professional portfolio project.
- Learn Computer Vision.
- Revise and Consolidate Concepts and Projects.

NLP
• Basic Text Processing
• NLTK & SpaCy
• Intro to Transformers

Computer Vision
• Basic Image Processing
• CNN and its advanced variants

Job Market Preparation
• Resume Building for Data Scientists
• Interview Preparation
• Portfolio Building & Demonstration

Job Market Preparation
• Resume Building for Data Scientists
• Interview Preparation
• Portfolio Building & Demonstration
MONTH 7: Capstone Project
All Weekends will hold:
- TA sessions for hand-holding support
- Guest lectures by international data scientists
- Real world data practice by industry leaders
Testimonials
Hear what our atoms say
Frequently Asked Questions
We recommend investing 2 extra hours daily to dive into materials, ace assignments, and truly embrace the learning journey.
Classes run five days a week, ensuring an immersive and engaging learning experience.
Our bootcamp explores cutting-edge data science technologies, covering machine learning, data analysis, and essential programming languages.
Absolutely! Active participation is crucial, with 80% attendance required for certification.
Yes, participants dive into hands-on assignments, reinforcing learning and practical application.
Definitely! We actively support participants in securing internships or jobs, rewarding those who show exceptional commitment and engagement.
A Bachelor’s Degree in various fields is accepted, including Computer Science, Engineering, Maths, Stats, Economics, and Business
Our bootcamp features live training sessions, providing real-time interaction with instructors and peers.
Fulfill attendance, engage actively, complete assignments, and demonstrate proficiency in the taught skills.
Regular attendance is encouraged, but we understand life happens. Communicate any issues, and we'll support your journey.
It covers essential math concepts, laying the groundwork for understanding and applying advanced data science and machine learning algorithms.
Yes, we host Teaching Assistant (TA) sessions for reviewing material, clarifying concepts, and addressing queries.
This series focuses on soft skills, industry awareness, and career readiness, ensuring you're well-equipped for success in data science.
We understand accessibility is key. Contact us at team@atomcamp.com for information on financial assistance or scholarships.
Fill out the application form below, and our admissions team will guide you through the next steps.