Intro to Data Analytics, SQL, and EDA Using Python

Overview

The ability to understand and work with data has become increasingly important in today's world, where data is ubiquitous and valuable. This course covers a range of topics, including what data is and its different types, what big data looks like, and how companies are using it. It also explores the fields of data analysis and data science and how the two come together. To help form the field of data analytics, we'll look at the entire data analytics process and how it works, from defining the analytics problem to interpreting and presenting the results. We'll also look at some case studies, which are presented to illustrate the application of data analytics in real-world scenarios.

Syllabus

Module 1: Introduction to Data & Data Analytics and Defining the Problem

The ability to understand and work with data has become increasingly important in today's world, where data is ubiquitous and valuable. The first module in this course covers a range of topics, including what data is and its different types, what "big" data looks like, and how companies are using it. It also explores the fields of data analysis and data science, and how the two come together to help form the field of data analytics.

Module 2: Introduction to Data Wrangling and SQL

There are multiple ways of storing and accessing data, with a variety of deployment strategies and storage options, including databases, data warehouses, and data lakes. In week 2, we’ll look at accessing data in a database. We’ll provide some context around relational databases, and then do a deep dive into SQL, which is a query language used for accessing and updating databases.

Module 3: Exploratory Data Analysis (EDA) Using Python

Real-world problems are often hidden behind large amounts of data. Through exploratory data analysis (EDA), we can distill valuable information from data and make reliable predictions about it. In other words, Python and data analytics provide computer scientists with effective tools for understanding, processing, and utilizing real-world data. This module will take an in-depth look at how to use the powerful Python libraries pandas and NumPy to perform key tasks such as loading, querying, cleaning, summarizing, and visualizing data.