AstroLabs Academy

Tools for Data Science, Applications for Data Analysis and Languages for Data Ccience

Selecting Optimal Data Graphics, Communicating with Color and C Analyses for Data Science

Clustering, Classifying and Anomaly Detection

Data Preparation Basics Filtering and Selecting

Grouping and Aggregation & Conditional Statements

The Data Collection Process

Practical Data Science Applications: Getting Data in Using Web Scraping

Mocking Inputs / Pagination - Search and Filters

Saving, Reading / Writing to a File

Data Sourcing via Web Scraping

Data Parsing

Cleaning and Stemming Textual Data

Project- Scraping Yellow Pages

Descriptive Analyses and Predictive Models

Anomaly Detection, Dimensionality Reduction, Feature Selection and Creation

Mathematics for Data Science: Algebra & Calculus

Acting on Data Science, Interpretability Actionable Insights

Collaborative Analytics with Plotly

Opening Files Text vs. Binary Mode

Practical Data Visualization

Data Engineering Introduction

Tools in Big Data

Scientific Python Overview

Overview of Jupyter Notebooks & Overview of DataBricks

Understand Markdown Cells, Edit Notebooks

Slicing Learn Boolean Indexing

Pandas Overview, Load CSV Files, Parse Time

Main Components of Machine Learning, Prediction (with Linear Regression)

Classification (with Support Vector Machine)

Fundamental Concepts of Machine Learning

Supervised Learning & Unsupervised Learning

Top-N Hit Rate: Many Ways Coverage, Diversity, and Novelty

Deep Learning Introduction & Deep Learning Prerequisites

Using DataBricks

Coverage, Diversity, and Novelty

Content-based Recommendations and the Cosine Similarity Metric

Tuning Collaborative Filtering Algorithms

Playing with TensorFlow

Tuning Restricted Boltzmann Machines

Introduction and Installation of Apache Spark

AWS SageMaker and Factorization Machines

Statistics and Probability Theory

Linear Algebra

Calculus and Geometry

Discrete Mathematics

Brief