Explore the fundamentals of Machine Learning, empowering you to implement, evaluate, and improve ML models in real-world scenarios.

Unleash your potential in data science! Propel your career to unprecedented heights.

“Not Just For Tech Giants, LunarTech Revolutionizes Data Science and AI Learning״

Forbes

“LunarTech is reshaping the very fabric of educational accessibility in the tech world״

Entrepreneur

“The Data Science Bootcamp combines a state-of-the-art curriculum, practical project-based learning. ״

Bloomberg

“In today's data-driven world, LunarTech shines in its mission to make AI education accessible for all.״

Benzinga

BECOME JOB READY DATA SCIENTIST

Our meticulous action plan ensures no time is wasted—your energy, money, and goals aligned for triumph.

Our curriculum stands as the gold standard, empowering your confidence and skills. No more hopping from place to place; everything you need is right here.

No waiting around. Complete the bootcamp, step into the workforce, and start earning from day one.

Forget hefty price tags. We charge just 1% of the norm, yet deliver priceless knowledge.

We don’t hold back. Whether it’s data science, career guidance, networking, or personal branding, we equip you with every essential tool for growth.

CAREER GUIDE

Dive into the world of Data Science with us and arm yourself with strategies to confidently tackle interviews, equipping you to become a job-ready Data Scientist.

In the initial section of our Data Science bootcamp, you’ll embark on a comprehensive journey through the realm of Data Science. Starting with an overview of the field, we chart out the common career paths, delve into essential technical and practical skills, and showcase real-world applications across various industries. We’ll conclude with a detailed exploration of the Data Science interview process and content, equipping you with strategies to confidently tackle interviews in Statistics, Machine Learning, A/B Testing, Data Analysis, NLP, and Programming. At the end of this section you will know what exactly you need to learn and practice to become a Job Ready Data Scientist.

- DATA SCIENCE OVERVIEW
- DATA SCIENCE CAREER PATH
- MACHINE LEARNING INTERVIEW

- MUST-HAVE SKILLS
- DATA SCIENCE USAGE
- A/B Testing INTERVIEWS

- INTERVIEW PROCESS
- STATISTICS INTERVIEWS
- Coding INTERVIEWS

OVERVIEW OF DATA SCIENCE

DATA SCIENCE SKILLS

DATA SCIENCE INTERVIEWS

STATISTICS

Dive into the fundamental Statistical concepts empowering you to analyze, model, and interpret complex data.

In the second section of our Data Science bootcamp, we dive into essential statistical concepts. Starting with Random Variables, we cover core measures like Mean, Variance, Standard Deviation, and explore the relationship between variables using Covariance and Correlation. We demystify Probability Distribution Functions and Conditional Probability, including an introduction to Bayes Theorem. Introduction to Econometrics, Causal Analysis, Hypothesis Testing, and Statistical Significance. We conclude with a variety of basic to advanced Statistical Tests and Inferential Statistics, cementing your understanding of the Central Limit Theorem and the Law of Large Numbers. This section will reinforce your statistical foundation, equipping you with the statistical skills to analyze, model and interpret complex data.

- Random Variables
- Sample Space
- Probability
- Variance
- Standard Deviation
- Covariance

- Correlation
- Probability Density Functions (PDFs)
- Conditional Probability
- Bayes’ Theorem
- Linear Regression
- Ordinary Least Squares (OLS)

- Hypothesis Testing
- Significance Level
- P-Values
- Type I & II Errors
- Confidence Intervals
- Statistical Tests

STATISTICS FOR DATA ANALYSIS

STATISTICS FOR MACHINE LEARNING

STATISTICS FOR A/B TESTING

STATISTICS FOR CAUSAL ANALYSIS

MACHINE LEARNING

In the ‘Fundamentals to Machine Learning’ section of our bootcamp, you’ll start by understanding the essential elements of machine learning, including a deep dive into supervised and unsupervised learning. We guide you on how to strategically select the best machine learning model for your data science project and meticulously walk you through the entire process of training an ML model We tackle essential concepts such as the Bias-Variance Trade-off, Overfitting, and Regularization. You’ll delve into the intricacies of both linear and non-linear modeling using a wide variety of popular classification and regression algorithms. Additionally, we cover an extensive list of clustering algorithms to help you handle unstructured data. We also shed light on Dimensionality Reduction, Feature Selection, Resampling Techniques, and Optimization Techniques. By the end of this section, you will be well-versed in implementing, evaluating, and improving various Machine Learning models in real-world scenarios.

- Supervised vs Unsupervised
- RSS, MSE, RMSE, Gini Index, Entropy
- Linear Regression (OLS)
- Logistic Regression (MLE)
- LDA
- KNN
- Grid Search

- Decision Trees
- Bagging
- Random Forest
- AdaBoost
- GBM
- XGBoost
- Gradient Decent

- K-Means
- Hierarchical Clustering
- DBSCAN
- PCA
- Cross Validation
- Bootstrapping
- SGD, SGD-Momentum, Adam

POPULAR MACHINE LEARNING ALGORITHMS

ML MODEL TRAINING

ML MODEL EVALUATION

ML MODEL OPTIMIZATION

Testing

In this industry level training section, we provide complete guide to A/B testing, discussing its definition, uses, and the process involved. We go in-depth into the concept of business and statistical hypotheses and primary metrics. Next, we focus on designing an A/B test, where you will learn about power analysis, minimum sample size calculation and test duration, along with an understanding of novelty and maturation effects. When it comes to running the A/B test, we provide guidance on key considerations to ensure its success. The section on result analysis helps you understand how to choose the right statistical test for your A/B test, how to calculate and interpret p-values, for the statistical significance and practical significance. Lastly, we shine a light on common pitfalls in A/B testing, and how to avoid these pitfalls to ensure the reliability of your A/B tests.

- Primary Metrics
- Business Hypothesis
- Statistical Hypothesis
- Statistical Tests
- Common Pitfalls

- Conversion Rate
- Click Through Rate
- A/B Test Design
- Power Analysis
- Practical Significance

- Minimum Detectable Effect
- Significance Levels
- Minimum Sample Size
- Test Duration
- Statistical Significance

COMPLETE GUIDE TO A/B TESTING

A/B TEST DESIGN

POWER ANALYSIS

A/B TEST RESULTS ANALYSIS

NLP & AI

Embark on a transformative journey into the world of Natural Language Processing and AI, unlocking the power to analyze and understand human language.

Introduction to Natural Language Processing” section begins with an overview of text preprocessing in NLP, highlighting the process and examples of cleaning text step-by-step. We examine the basic NLP techniques such as tokenization, bag-of-words, word embeddings, semantic analysis. We also cover Term Frequency-Inverse Document Frequency (Tf-Idf), explaining its definition, idea, and the step-by-step process for calculating Term Frequency (Tf) and Inverse Document Frequency (Idf), along with examples. Lastly, we leap into the future with the latest innovations in Natural Language Processing (NLP), exploring transformer models like BERT and GPT-3. Comparisons between these models are also highlighted.

- Text Preprocessing
- Tokenization
- Bag-of-Words Representation
- Count-Vectorizer

- Word Embeddings
- Semantic Analysis
- Tf-Idf
- ChatGPT

- Cutting-Edge NLP Developments
- Transformers
- BERT
- GPT-3

TEXT PREPROCESSING GUIDE

BASIC NLP TECHNIQUES

RECENT DEVELOPEMNETS IN NLP, LLM, AND AI

A/B TEST RESULTS ANALYSIS

DATA SCIENCE

Embark on a comprehensive journey into Python programming for Data Science, equipping yourself with the essential skills and tools to analyze, preprocess, visualize, and interpret data confidently.

This industry level section starts with best coding practices and the use of the PyCharm environment. It introduces various data types, variables, complex structures like lists, dictionaries, and matrices, and fundamental constructs like for-loops and if-else statements. The section also explores essential Python libraries for data science and demonstrates data loading, exploration, preprocessing, and random generation. We further delve into data filtering, sorting, and grouping, along with methods for calculating descriptive statistics. This includes handling tasks related to merging datasets, creating User Defined Functions (UDFs), text cleaning for NLP, and a range of data visualization techniques. Finally, we examine various data sampling methods, and we provide a comprehensive and step-by-step walkthrough of A/B Test results analysis in Python.

- BEST CODING PRACTICES
- PyCharm IDE
- Data Types
- Data Structures
- For-loops, If-Else Statements
- A/B Test Analysis

- Data Science Python Libraries
- Data Loading
- Data Preprocessing
- Random Data Generation
- Data Aggregation
- Data Sampling

- Descriptive Statistics
- Merging Datasets
- User Defined Functions (UDFs)
- NLP Text Preparation
- Data Analysis
- Data Visualization

PYTHON Basics

DATA ANALYSIS IN PYTHON

DATA VISUALIZATION IN PYTHON

TEXT PREPARATION IN PYTHON

A/B TEST RESULTS ANALYSIS IN PYTHON