In this section of our Data Science bootcamp, you'll gain a comprehensive overview of the field, explore key career paths, and develop essential technical skills.

We cover real-world applications and prepare you for interviews in Statistics, Machine Learning, A/B Testing, Data Analysis, NLP, and Programming.

By the end, you’ll know exactly what to learn and practice to become a job-ready Data Scientist.

In this section of our Data Science bootcamp, you will master essential statistical concepts, starting with foundational topics like Random Variables, Mean, Variance, and Standard Deviation, and progressing to Covariance, Correlation, and Probability Distributions.

We delve into Conditional Probability and Bayes Theorem, alongside key analytical tools such as Hypothesis Testing, Statistical Significance, and Confidence Intervals.

This comprehensive coverage, including Inferential Statistics, advanced Statistical Tests, and principles like the Central Limit Theorem, equips you to analyze and interpret complex data confidently.

In the Fundamentals of Machine Learning section of our bootcamp, you’ll gain a comprehensive understanding of both supervised and unsupervised learning, as well as strategic model selection and training processes.

Key concepts such as the Bias-Variance Trade-off, Overfitting, and Regularization are thoroughly explored to solidify your foundational knowledge and practical application skills.

Paired with hands-on experience in popular classification, regression, and clustering algorithms, you’ll also delve into advanced techniques like Dimensionality Reduction, Feature Selection, and Optimization Methods.

This ensures you have the expertise needed to implement, evaluate, and refine ML models confidently, preparing you for complex, real-world data challenges.

Master A/B testing through comprehensive, industry-level training that guides you from defining business and statistical hypotheses to analyzing test results for impactful decision-making.

This section covers everything from designing an A/B test with power analysis, sample size calculations, and test duration considerations, to understanding novelty and maturation effects.

You'll learn how to choose the right statistical tests, interpret p-values, and assess statistical and practical significance effectively.

Additionally, gain insight into common pitfalls in A/B testing and strategies to avoid them, ensuring your experiments are reliable and drive meaningful insights.

NLP and AI are at the forefront of data science, unlocking powerful ways to analyze and understand human language.

This module starts with the foundations of text preprocessing, guiding you through practical examples of text cleaning and essential techniques like tokenization and bag-of-words.

Progress through advanced concepts such as word embeddings, semantic analysis, and calculating Term Frequency-Inverse Document Frequency (Tf-Idf) with clear, step-by-step processes.

Explore cutting-edge advancements in NLP with an in-depth look at transformers, including GPT4, and understand their strengths through direct comparisons.

By mastering these tools and techniques, you’ll be equipped to harness the power of NLP for impactful, real-world applications.

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.

In this module, we will create real-world case studies that reflect the practices of the world’s leading companies, delivering unparalleled, hands-on experience.

You’ll dive into projects spanning data analysis, product data science, machine learning, and advanced recommender systems.

Gain expertise as you analyze music playlists through exploratory data analysis and causal inference, predict job ad salaries with robust machine learning models, and build top-K job recommender systems using cutting-edge NLP and collaborative filtering.

This comprehensive training ensures you acquire the practical insights and essential skills needed to conquer complex data science challenges with confidence.