Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

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Python3

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming. It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA). In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Data Science for Non-Programmers

A point estimate can give us a rough idea of a population parameter. But there is a chance that it contains some errors. We need to take many samples to reduce these errors. But taking many samples is not feasible all the time. As in our example of Data Scientists, it might not be feasible to take many different samples of 100 data scientists. What do we do if we face such a situation? We make *confidence intervals*. 



# Confidence intervals
A **confidence interval** is a range of values above and below a point estimate that might contain the true value of a population parameter. The interval is associated with a *confidence level*. The bigger the confidence level, the wider the range of the interval. The confidence level is decided before calculating the confidence intervals. Confidence intervals are usually reported with point estimates to show how reliable the estimates are.

The intervals are calculated from samples which means for each sample, there will be a different interval. Commonly, a $95\%$ confidence level is used, which means there is a $95\%$ chance that the true population parameter lies in the range. Or in other words, if we take $100$ samples and calculate confidence intervals for each of the $100$ samples with $95\%$ confidence, the true population parameter will lie in $95$ of these intervals. 

## Calculating confidence intervals
We can calculate confidence intervals from point estimates. We add and subtract a margin of error to the point estimate to calculate both boundaries of the interval. The margin of error depends on the:

* Confidence level chosen
* Size of the data
* Standard deviation of the data which denotes the spread of the data

There are two ways of calculating confidence intervals that depend on whether we know the standard deviation of the population or not.



This lesson will focus on the importance and calculation of confidence intervals. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Confidence Intervals

Confidence intervals

Calculating confidence intervals