Data Science Certification Course from Scratch

This bootcamp for data science beginners starts with an introduction to the world of data analysis! Learn key concepts and Python basics: loops, conditions, functions, and the Pandas library for data analysis. Chapter 1: Introduction to your future profession Chapter 2: Variables, data types, and arithmetic operations Chapter 3: Strings Chapter 4: Lists

Continue exploring Python dictionaries, commonly used in data fields. Make your code flexible and reusable with functions. Chapter 1: Loops Chapter 2: Conditional statements Chapter 3: Dictionaries Chapter 4: Functions

Learn to process data with the Pandas library. Handle missing values, duplicates, and imperfect data. Chapter 1: The Pandas library Chapter 2: Reading and visualizing data Chapter 3: Handling duplicates and missing values Chapter 4: Data filtering

Change data types, identify correlations, and create visualizations. Chapter 1: Data types Chapter 2: Feature engineering Chapter 3: Data transformation Chapter 4: Data visualization

In this sprint, you’ll get hands-on data science training in statistical analysis and probability. Study probability theory, common distributions, and statistical methods in Python. Learn sampling, statistical significance, and anomaly detection. Chapter 1: Descriptive statistics Chapter 2: Probability theory Chapter 3: Hypothesis testing

Identify patterns to predict whether a video game will succeed commercially.

Prepare your development environment. Use the command line, Git, and GitHub, and explore more advanced Python. Chapter 1: Introduction to the command line Chapter 2: Development environments Chapter 3: Git and GitHub Chapter 4: Intermediate Python Chapter 5: Individual development environment

Understand database structures and use SQL queries to extract data. Find data online. Chapter 1: Retrieving data from online resources Chapter 2: SQL as a tool for data analysis Chapter 3: Advanced SQL functions for analysts Chapter 4: Table relationships Chapter 5: Soft skills

Master the basics of machine learning (ML), statistical analysis, and actionable insights. Use the scikit-learn library to complete your first real-world project. Chapter 1: Training your first model Chapter 2: Model quality Chapter 3: Model improvement Chapter 4: Regression models

Dive into the most in-demand area of machine learning. Learn how to fine-tune models, improve metrics, and handle imbalanced data. Chapter 1: Data encoding and standardization Chapter 2: Classification metrics Chapter 3: Handling imbalanced classification Chapter 4: Regression metrics Chapter 5: Soft skills

Apply what you’ve learned to business scenarios. Explore business metrics, A/B testing, bootstrap techniques, and data labeling. Chapter 1: Business metrics Chapter 2: Implementing new functionality Chapter 3: Data collection Chapter 4: Soft skills

Create a machine learning prototype to help a mining company develop efficient solutions.

Explore algorithms you’ve learned and their practical applications. Dive into linear algebra concepts like vectors, matrices, and linear regression. Chapter 1: Vectors and vector operations Chapter 2: Distance between vectors Chapter 3: Matrices and matrix operations Chapter 4: Linear regression in-depth

Analyze algorithms using numerical methods and apply them to practical exercises. Learn gradient descent, gradient boosting, and neural networks. Chapter 1: Algorithm analysis Chapter 2: Gradient descent Chapter 3: Gradient descent training Chapter 4: Gradient boosting Chapter 5: Soft skills

Explore time series analysis. Understand trends, seasonality, and feature creation. Chapter 1: Time series analysis Chapter 2: Time series forecasting

Apply machine learning to text data. Learn text-to-number conversions, bag-of-words models, TF-IDF, embeddings, and BERT. Chapter 1: Text vectorization Chapter 2: Language representations

Work on basic computer vision tasks using pre-trained neural networks and Keras. Get a glimpse of deep learning. Chapter 1: Fully connected networks Chapter 2: Convolutional neural networks Chapter 3: Soft skills

Learn what to do when you lack target variable features. Work on clustering and anomaly detection tasks. Chapter 1: Clustering Chapter 2: Anomaly detection

Use everything you’ve learned in a two-week bootcamp simulating the experience of a junior data scientist’s job.