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

In the previous lesson, we looked at the intuition behind the gradient descent algorithm and the update equation. In this lesson, we are going to implement it in Python. We are going to predict the tips paid by a customer at a restaurant. We will choose the best model using gradient descent.

# Minimization with Gradient Descent
Recall that the gradient descent algorithm is 

* Start with a random initial value of $\theta$.
* Compute $\theta_t - \alpha \frac{\partial}{\partial \theta} L(\theta_t,Y)$ to update the value of $\theta$.
* Keep updating the value of $\theta$ until it stops changing values. This can be the point where we have reached the minimum of the error function. 

We will be using the *tips* dataset that has the following data. 


# Tips Dataset
# total_bill : Total bill of the customer
# tip : Total tip paid by the customer
# gender : Tgender of the customer(Male/Female)
# smoker : smoker(yes/no)
# day : which day of the week
# time: time of visit (lunch/dinner)
# people: total people that came to dine in. 

Our simple model said that for predicting tips we only need the amount of the total bill paid by the customer. Therefore, our prediction ($\hat{y}$) depends on the total bill ($x$) and the model parameter ($\theta$). We have:  
$$\hat{y} = \theta x$$

We will need a function that gives us the derivative of the loss function.

## `gradient`
Before we can implement this in code on our predicting tips example, we need to evaluate the gradient term in the update expression

$$ \theta_{t+1} = \theta_t - \alpha \frac{\partial}{\partial \theta} L(\theta_t,Y) $$ 

We know that if our Loss function is *mean squared error* then:

$$\frac{\partial}{\partial \theta} L(\theta_t,Y) = \frac{\partial}{\partial \theta_t}[ \frac{1}{n}\sum_{i=1}^{n} (y_i - \hat{y}_i )^2  ] $$

since we chose a simple model which said that for predicting tips we only need the amount of the total bill paid by the customer. Therefore, our prediction ($\hat{y}$) depends on the total bill ($x$) and the model parameter ($\theta$). We have   
$$\hat{y} = \theta x$$

$$\frac{\partial}{\partial \theta} L(\theta_t,Y) = \frac{\partial}{\partial \theta_t}[ \frac{1}{n}\sum_{i=1}^{n} (y_i - \theta_t x_i )^2  ] $$

Evaluating this expression gives us: 

$$ \frac{\partial}{\partial \theta} L(\theta_t,Y)  = \frac{-2}{n} \sum_{i=1}^{n} (y_i - \theta_t x_i)(x_i ) $$

Therefore, we will be coding this expression to compute the gradient. `gradient` function will need the same arguments as the `mse_loss_func` which are:

* `theta`: The model parameters $\theta$
* `x`: The dataset needed to make predictions
* `y`: The actual values with which to compare 

This lesson will focus on how to implement gradient descent algorithm in Python. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Optimization with Gradient Descent

Minimization with Gradient Descent