Python Basics

Get introduced to the basic concepts of Python. Learn how to use Python interactively and by using a script. Create your first variables and acquaint yourself with Python’s basic data types.

Python Lists

This topic will equip you with skills to store, access, and manipulate data in lists: the first step toward efficiently working with huge amounts of data.

Functions and Packages

Acquire the capacity to use functions, methods, and packages to efficiently leverage the code that brilliant Python developers have written. The goal is to reduce the amount of code you need to solve challenging problems.

R is broad and powerful, with many analytic and graphic functions available (more than 50,000). With guidelines and instructions, you can navigate the tremendous resources available in R thereby accomplishing your work with style, elegance, and efficiency.

Many people and researchers despise statistics, mainly due to their non-mathematical background. This makes understanding complex statistical equations very difficult. The advent of computer programs such as R and the like provides a unique opportunity to teach statistics at a conceptual level without getting too bogged down in equations. However, the downside of the computer is that it makes it really easy to make complete fools of ourselves if we do not really understand what we are doing. Running an analysis using a computer without any statistical knowledge can be totally misleading. Hence this course could be called Unearthing the Statistician in You Using R.

• The R environment
• Getting data into and of R
• Data management in R
• Plotting in R

Assumptions

• Exploring
• Remedial measures

Correlations

• Bivariate correlations
• Partial correlations

Much of the world’s raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. Being able to wrangle and extract data from these databases using SQL is an essential skill within the data industry and in increasing demand.

In this introduction to SQL course, you’ll get to know the theory and the practice through lectures and interactive exercises where you can put your new-found skills to the test.

SQL is an essential language for building and maintaining relational databases, which opens the door to a range of careers in the data industry and beyond. You’ll start this course by covering data organization, tables, and best practices for database construction.

The second half of this course looks at creating SQL queries for selecting data that you need from your database. You’ll have the chance to practice your querying skills before moving on to customizing and saving your results.

PostGreSQL and SQL Server are two of the most popular SQL flavors. You’ll finish off this course by looking at the differences, benefits, and applications of each. By the end of the course you’ll have some hands-on experience in learning SQL and the grounding to start applying it on projects or continue your learning in a more specialized direction.

1.      Relational Databases

Before writing any SQL queries, it’s important to understand the underlying data. In this topic, we’ll discover the role of SQL in creating and querying relational databases. Using a database for a local library, we will explore database and table organization, data types and storage, and best practices for database construction.

2.      Querying

Learn your first SQL keywords for selecting relevant data from database tables! After practicing querying skills in a database of books, you’ll customize query results using aliasing and save them as views so they can be shared. Finally, you’ll explore the differences between SQL flavors and databases such as PostgreSQL and SQL Server.

Master the basics of Microsoft SQL Server—one of the world’s most popular database systems.

This course covers:
✓ How to use SELECT statements to retrieve data
✓ How to use SQL Server aggregate functions
✓ How to manipulate text fields
✓ How to retrieve data from multiple sources
✓ All of the key aspects of working with data in SQL Server

Each time you’re introduced to a new concept or function, you’ll have the opportunity to test your knowledge and build your confidence. You’ll work with a digital media database to review the sales of various artists and tracks, Eurovision datasets, and review trends in US power outages to explore a number of different data types and scenarios.

Learn how to leverage statistical techniques using spreadsheets to work with and extract insights from your data.

1. Getting To Know Your Data

Begin your journey by learning why and how to summarize your data using statistics such as the mean, median, and mode. While working with a variety of datasets ranging from Amazon revenue to U.S Presidential ratings, you’ll learn about the differences between each of these fundamental statistics and most importantly, when to use each.

2. Statistical Data Visualization

Data visualization is one of the most important parts of any data science workflow. It leads to a deeper understanding of your dataset which in turn allows you to more effectively communicate results to stakeholders. In this topic, you’ll learn how to visualize your data in Spreadsheets using statistical plots such as the histogram, scatter plot, and bar plot.

3. Statistical Hypothesis Testing

This topic introduces you to statistical hypothesis testing. You’ll learn how to construct a hypothesis, test it using different statistical tests, and properly interpret the results.

Handling statistical data is an essential part of the research. However, many people find the idea of using statistics, and especially statistical software packages, extremely daunting. This course, Introduction to SPSS, takes a step-by-step approach to statistics software through seven interactive activities.

• How to start SPSS
• Using the Menu
• Adding variables
• Obtaining descriptive statistics
• Correlation
• Independent T-Tests
• Paired samples T-Tests

Data literacy is an essential skill for every role within an organization—not just data scientists and analysts. As companies collect more data than ever before, it’s critical that everyone can read and analyze that data efficiently. In this course, you’ll learn the basics of data-driven decision-making and get to apply these skills to three real-life examples from the world of finance, marketing, and operations. You’ll also discover how to uncover new insights and opportunities by applying supply and demand, cost and benefit, and risk and rewards frameworks—gaining practical skills to help you thrive in the new data-driven world.

1. Data-Driven Decision-Making Framework

In this topic, you’ll get familiar with the data-driven decision framework. You’ll learn more about different types of data analysis and their objectives. By the end, you’ll be able to see where each decision fits within the framework.

2. Applying Data to Inform Marketing

Let’s apply the data-driven decision in a marketing context. You’ll use data to optimize ad spending, identify arbitrage opportunities for website traffic, and even how to forecast new product launches with limited but relevant data.

3.  Spotting Finance Opportunities With Data

Learning how to use data to inform decisions in finance can be satisfying and profitable. In this topic, you’ll review investment opportunities using data in consumer credit, real estate, and even in a non-traditional market of collectibles.

4. Data-Driven Business Operations

In the final topic, you’ll apply data to create a total addressable market calculation for a startup, learn about supply and demand curves used for staffing, and how to spot customer-driven areas for improvement. This will provide you with practical experience of how identifying optimization opportunities and supporting existing business operations.