My statistics course is ideal for those studying on their own, or if you are in a statistics class and struggling with your assigned textbook or lecture material. I know stats courses can be boring, so I try to make it as exciting as possible.
The examples have a psychology bend, but this course is absolutely relevant for business students, especially those in data analysis that have to get a better understanding of statistical tests and the fundamental concepts. So welcome to you whether you are in business or psychology.
I provide examples within the lessons, so this should cut down your study time. Furthermore, I make sure that students understand the links between the different lessons. This helps you understand the bigger picture, and it also tends to decrease study time as you are not stuck wondering "how does this all come together"?
Our course does not require any previous Data Science experience. The goal of 'Data Science for Beginners' is to get you acquainted with Data Science methodology, data science concepts, programming languages, give you a peak into how machine learning works, and finally show you a data science tool like GitHub, which lets you collaborate with your colleagues.
Now, while this is a beginner course, it does not mean that it is an easy course. For example in the Data Science methodology section, many different concepts are introduced. But please keep in mind that a. you will get concrete examples of what each concept means when it is brought up b. you can ask questions and c. most importantly, you are not meant to understand all the concepts fully.
Going through the methodology is meant to introduce you to concepts, not prepare you to fully apply them. You will get a chance to do this in other courses (ours or other providers).
Beyond this, you will get to build a simple chatbot. This hands on activity will illustrate in a more interactive way how machine learning works and how you can provide a machine learning service such as this in your future career.
We will prepare you to use Python for Data Science.
We start by illustrating Python programming fundamentals. You will learn about variables, data types, data structures (lists, sets, tuples, dictionaries), decision and looping structures, and functions.
Next, I have a whole section on how to work with nested data, nested iteration and list comprehension. These are more advanced topics that build on the fundamentals.
We then turn to working with libraries that are built on top of 'pure' or 'base' Python and are used for data analysis, data manipulation, and data science. These libraries were designed to help make data science work easier and more flexible. You will work with Numpy and Pandas.
In the first course 'Learn SAS and Become a Data Ninja' you will learn the data step and proc step, the primary skills of a SAS Programmer/Analyst. You will be able to create and work with data, including importing data, selecting only observations you want, creating new variables, merging/combining data, using logic, running statistics on your data, and more.
In the second course, 'Advanced SAS Programming: SAS SQL, Macros, Indices', you will learn more advanced programming within SAS. SAS has an implementation of SQL called Proc SQL. It also has Macro Programming. Both are more specialized. You will be interested in this section if you want a better understanding of how to fetch data and work with big data (as Macros/Indices) are a must when you are working with large volumes of data.
Finally, 'SAS Predictive Modeling using Logistic Regression', will introduce you to the predictive modeling process. We will go through all the steps and you will see how an algorithm like Logistic Regression can be used to make predictions for particular problems.
Buy today! This is the bundle to get you started with Data Science!
I've been a course instructor in the data science sphere for about 3 years. I have a particular passion for statistics/statistical programming and predictive modeling. I learned programming partly through school and partly self-teaching. At one point I was a Graduate student in Educational Psychology but decided at some point during that program that teaching and creating online content in the data science sphere was even a bigger passion.