Learning Statistics
My Learning Process.
My goal in these posts is to put down information about data science and the branches of science that it build itself upon. I initially (2 years ago) was interested in using R to build predictive models and I was able to build some predictive models using R and my basic introduction to how machine learning and data models can be used to produce a predicted value. The website I was into was from the good folks at https://www.analyticsinhr.com/blog/tutorial-people-analytics-r-employee-churn/
I was largely using my technical/programming skills to take advantage of what I largely did not understand, Statistics. How do you go about statistics? Where do I begin and what do I learn first? So, after a some time spent on a software implementation, I was left with the choice of learning this process.
I decided to leave R for Python after listening to the outstanding podcast https://talkpython.fm/ Talk Python to Me. I was immediately fascinated and because I was building Linux VM's I though it would help me refresh my Linux skills. I soon learned that Python was like many other programming environments, diverse. I eventually settled on PyCharm for my development environment of choice and to begin, I walked thru the Titanic Machine Learning Tutorial at https://www.kaggle.com/c/titanic.
This tutorial not only produced more questions but also answered many of the questions I had regarding the statistics process and how to properly query a data set. Fantastic!
I am currently reading the Guidelines for Assessment and Instruction in Statistics Education (GAISE) in an attempt to better understand the statistical process from an educators perspective.
1. Teach statistical thinking.
2. Focus on conceptual understanding.
3. Integrate real data with a context and a purpose.
4. Foster active learning.
5. Use technology to explore concepts and analyze data.
6. Use assessments to improve and evaluate student learning.
That looks like what I want to do. Lets get going...
My goal in these posts is to put down information about data science and the branches of science that it build itself upon. I initially (2 years ago) was interested in using R to build predictive models and I was able to build some predictive models using R and my basic introduction to how machine learning and data models can be used to produce a predicted value. The website I was into was from the good folks at https://www.analyticsinhr.com/blog/tutorial-people-analytics-r-employee-churn/
I was largely using my technical/programming skills to take advantage of what I largely did not understand, Statistics. How do you go about statistics? Where do I begin and what do I learn first? So, after a some time spent on a software implementation, I was left with the choice of learning this process.
I decided to leave R for Python after listening to the outstanding podcast https://talkpython.fm/ Talk Python to Me. I was immediately fascinated and because I was building Linux VM's I though it would help me refresh my Linux skills. I soon learned that Python was like many other programming environments, diverse. I eventually settled on PyCharm for my development environment of choice and to begin, I walked thru the Titanic Machine Learning Tutorial at https://www.kaggle.com/c/titanic.
This tutorial not only produced more questions but also answered many of the questions I had regarding the statistics process and how to properly query a data set. Fantastic!
I am currently reading the Guidelines for Assessment and Instruction in Statistics Education (GAISE) in an attempt to better understand the statistical process from an educators perspective.
1. Teach statistical thinking.
2. Focus on conceptual understanding.
3. Integrate real data with a context and a purpose.
4. Foster active learning.
5. Use technology to explore concepts and analyze data.
6. Use assessments to improve and evaluate student learning.
That looks like what I want to do. Lets get going...
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