Practical Data Science
 5.5 hours ondemand video
 1 article
 30 downloadable resources
 Full lifetime access
 Access on mobile and TV
 Certificate of Completion
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Try Udemy for Business Understand the entire Data Science Process

Use Python and its Scientific Libraries: Pandas, NumPy, StatsModels and more...

Put Theory and Concepts into action through Practical Application
 Use various Statistical Methods to Extract useful Information from Data
 Hands on Experience with handling Big Data
 Python  IPython Notebook (Download/Installation instructions will be provided)
 You should have Microsoft Excel
"Junior Level Data Scientist Median Salary from $91,000 and up to $250,000".
As an experienced Data Analyst I understand the job market and the expectations of employers. This data science course is specifically designed with those expectations and requirements in mind. As a result you will be exposed to the most popular data mining tools, and you will be able to leverage my knowledge to jump start (or further advance) your career in Data Science.
You do not need an advanced degree in mathematics to learn what I am about to teach you. Where books and other courses fail, this data science course excels; that is each section of code is broken down through the use of Jupyter and explained in a easy to digest manner. Furthermore, you will get exposed to real data and solve real problems which gives you valuable experience!
 Junior Data Scientist
 Statistical Analyst
 Data Analyst
 This course is suited for individuals who want to advance their career in data science or data analytics
This is introduction to the topic of Data Science. We discuss what is Data Science and some of the buzz words surrounding this subject.
Perhaps the most commonly used data visualization technique is a Histogram. This lecture answers: What is a Histogram and How to generate one in Python.
Central Limit Theorem is a critical concept in statistics. The properties of this theorem allow us to make inferences about a population without knowing its true distribution. In this lecture we use simulations (in Python) to prove Central Limit Theorem (CLT) and use the CLT properties to evaluate central tendency and variance of a nonnormal (population) distribution.
We Introduce Parametric Models (for Statistics) and extend this idea to Linear Response Modelling. Before we can apply this to popular statistical techniques such as Linear Regression, we need to discuss the assumptions of Linear Response Models.
It is a common business objective to find which products or promotions increase sales. This lecture gives you an idea about how to utilize Exploratory Data Analysis as a means of Feature Selection and as well as Knowledge Discovery. We then use multiple regression to verify whether the effect really exists (based on what we learned in our Exploratory Data Analysis!).