Learn Python for Data Science in our notebook-based course. Learn with examples, exercises, tutorials and hands-on projects perfect for the aspiring data professional. 

Python for Beginner Data Scientists Course | Temzee

You can put whatever number you want for `age` as long as it meets the specifications.

You can put whatever number you want for `height_cm` as long as it meets the specifications.

You can put whatever number you want for `weight_kg` as long as it meets the specifications.

You can put whatever number you want for `number_of_pets` as long as it meets the specifications.

You can put whatever number you want for `number_of_cars` as long as it meets the specifications.

In Python, variables store values. Stored values can be displayed by the print() function. They can only consist of letters, numbers, and underscores, and are case-sensitive.

Python Variables

The print function (aka the print statement) displays the value that is stored to a variable to the output section of a code cell. In notebooks, it is not the only way to print out a value, but it is a foolproof method for doing so.

The Print Function in Python

The Print Function

Numbers and Math in Python

An overview of Machine Learning Modeling ands relationship to many fundamental Python coding concepts. An overview of different categories of algorithms including supervised learning's classification and regression algorithms.

What is Machine Learning?

Strings are how we represent text in Python. They are a sequence of characters that are wrapped in quotation marks. They're important for recording categorical data and can be manipulated in a variety of ways.

Strings in Python

Comments are how we can leave notes in our code for the benefit of ourselves and others. Lines of code that are "commented out" won't be executed by Python, but are still readable by humans viewing the code.

Comments in Python - single multiline and docstring

Comments in Python

Functions are one of the most important parts of any programming language. They allow us to store code to variables and make our code reusable. We pass information to functions via parameters and arguments and then functions return outputs to us.

Functions in Python

f-strings allow us to insert raw values or values held within variables into text strings. This helps us create well formatted and readable output to display below our code cells for the benefit of anyone looking through our notebook.

String Formatting in Python with f-strings

String Formatting with F-Strings

Booleans, Conditionals and If Statements all come together to allow us to conditionally execute certain lines code in our programs if the right conditions are met. This gives our programs some basic decision making abilities and gives a logical flow to our program's execution which (as the writer of the code) we're able to control.

Booleans, Conditionals and If Statements in Python

Conditionals, Booleans, and If Statements

Lists are so important to Data Scientists! This is our first "data structure" which will allow us to store multiple pieces of data within a single variable. This is where we actually get to start working with and analyzing data!

Intro to Python Lists

Practice creating new lists and using some of Python's built-in functions to manipulate lists of numbers. We'll write a function that can calculate the mean of any list of numbers!

Intro to Python Lists - Exercises

In this lesson we'll draw from our knowledge of one kind of sequence (strings) to learn about another kind of sequence (lists), to show that many operations on these sequences have a similar (if not identical) coding syntax.

Lists as a Sequence of Values

More lessons are coming to this course soon. Written versions of each lesson and associated exercises will be released first followed and then video versions will be released sometime later. DM me (Ryan) on Discord if you're dying for the next installment.

Coming Soon to Python For Beginner Data Scientists

Coming Soon...

Python for Beginner Data Scientists

Improve your Data Science skills and learn Python by attempting a code challenge. Get hints and view solutions if you get stuck.

Python Code Challenges for Data Scientists

There so are many interesting ways to solve FizzBuzz! We'll be crowd-sourcing some of the most creative solutions and adding them here over time. 

Below is an example of one of the most common approaches, however, there are many methods that are faster, and more concise than this solution.

FizzBuzz is one of the best-known code challenges in the world. Solving it with vanilla Python is fine. But things get spicy when other Python packages get involved.

FizzBuzz Code Challenge in Python with NumPy and Pandas

FizzBuzz

### Solution 1:
If we're doing everything manually with vanilla Python, we can use a for loop to get a running total of the elements of the list at the same time that we use a counter to find the length of the list, and then use those two values to calculate the mean.

### Solution 2:
If we're willing to use a couple of Python's built-in functions (`sum()` and `len()`) then calculating the mean becomes quite straightforward.

### Solution 3:
If we import Python's statistics module we can access a `mean()` function directly. I still have to ask you to wrap it in the outer `mean_list()` function so that I can evaluate your code.

### Solution 4:
If we use NumPy, then throwing the list into a NumPy array gives us access to the `.mean()` method that exists on NumPy arrays

### Solution 5:
Same thing with Pandas, but using the Pandas `Series` object (which leans heavily on NumPy arrays behind the scenes).

What other creative ways were you able to calculate the mean of a list?


Write a function that calculates the mean (average) of a list of numbers.

Mean of a numeric list in Python - Code Challenge

Mean of a List

There are many ways to solve this challenge successfully.

The main consideration is that when the passed-in list contains an even number of elements the operation should sort the list and then find the middle two numbers **and return the _average_ of those two numbers**.

When the passed-in list contains an odd number of elements, the function should sort the list and simply return the middle number.

Write a function that calculates the median of a list of numbers.

Median of a List in Python - Code Challenge

Median of a List

### Solution 1 
Solution 1 contains a verbose yet (hopefully) highly readable vanilla python implementation of the sample variance.

### Solution 2 
Uses NumPy arrays to eliminate the for loop and results in a calculation that is more similar to the mathematical equation provided above

### Solution 3 
Uses the `.var()` method of a pandas Series. Pandas Series objects always calculate the *sample* variance by default.

### Solution 4
We can use the `.var()` method of a NumPy array to the same effect, but NumPy arrays calculate the *population* variance by default so an extra parameter `ddof=1` is required to indicate that we are interested in the *sample* variance.

Write a function that calculates the *sample* variance of a list of numbers.

Sample Variance of a list in Python - Code Challenge

Variance of a List

### Solution 1 
Solution 1 contains a verbose yet (hopefully) highly readable vanilla Python implementation of the sample standard deviation.

### Solution 2
Solution 2 relies on one of the `var_list` function solutions that we wrote in the [Variance of a List](https://temzee.com/code-challenges/variance-of-a-list-python) code challenge as a helper function. Our standard deviation function returns the square root of the value returned by the `var_list` function. To take the square root we are raise the result to the `1/2` power.

### Solution 3
Uses NumPy arrays to eliminate the for loop and results in a calculation that reads more like mathematical equation.

### Solution 4
Uses the `.std()` method of a Pandas Series. Pandas Series objects always calculate the *sample* standard deviation by default.

### Solution 5
We can use the `.std()` method of a NumPy array to the same effect, but NumPy arrays calculate the *population* standard deviation by default so an extra parameter `ddof=1` is required to indicate that we are interested in the *sample* standard deviation.

Write a function that calculates the *sample* Standard Deviation of a list of numbers.

Sample Standard Deviation in Python - Code Challenge

Standard Deviation of a List

### Solution 1
In Solution 1 we calculate the sample size and the mean of the two passed-in lists (`x` and `y`). Then, in a for loop, we calculate the deviation of each observation from its respective mean and multiply those deviations against each other. We use a running total to sum up all of these individual product deviations. This results in the `sum_of_product_deviations` which we then divide by `n-1` to find (and return) the desired covariance.

### Solution 2
In Solution 2 we use two Pandas Series objects and the built-in `.cov()` method to find the covariance between these two Series. We could have reversed the positions of `x` within the return statement found the same result.

### Solution 3
In Solution 3 we used `np.cov()` to find the covariance matrix. We then select from the covariance matrix the particular covariance value that we are interested in.

Try your hand at calculating the sample covariance between two random variables (lists of integers).

Sample Covariance Python Code Challenge

Sample Covariance

### Solution 1
In Solution 1 we calculate the sample covariance and two sample standard deviations separately –using helper functions–and then we put them together to calculate the correlation coefficient.

### Solution 2
In Solution 2 we use two Pandas Series objects and the built-in .corr() method to find the covariance between these two Series. We could have reversed the positions of x within the return statement found the same result.

### Solution 3
In Solution 3 we used np.corrcoef() to find the correlation matrix. We then select from the correlation matrix the particular covariance value that we are interested in.

Write a function to calculate the sample correlation coefficient (r). This task will be very approachable if you have already completed the Standard Deviation and Covariance code challenges.

Correlation Coefficient Python Code Challenge

Correlation Coefficient

Demonstrate that you can calculate the correct slope and intercept coefficient using a simple linear regression model in Python.

Simple Linear Regression Code Challenge in Python

Simple Linear Regression

Use Python (or NumPy if you really *have* to) to calculate the dot product of two vectors represented as Python Lists.

Vector Dot Product Code Challenge

Vector Dot Product

Can you write a function that will generate a matrix (2D Python List or NumPy Array) of integers - with an indicated dimensionality? We'll tell you the number of rows and columns that the matrix should have, you do the rest.

Random Matrix Generator Code Challenge in Python

Random Matrix

Code Challenges

The accuracy score is a metric used to evaluate classification models. It is essentially the percentage of correct predictions made by the model: Accuracy = (Correct Predictions / Total Predictions) * 100

Accuracy Score in Classification

Accuracy Score

An activation function is a math function that determines the output of a neuron in a neural network. Common activation functions include: sigmoid, relu, tanh, and softmax.

Activation Function in Neural Networks

Activation Function

An algorithm is a step-by-step procedure designed to accomplish a particular task. Typically these unambiguous instructions are for computers or a humans to follow to perform a task or solve a problem.

Algorithm in Data Science

Algorithm

Artificial General Intelligence (AGI) refers to artificial intelligence that exhibits cognitive abilities comparable to humans across a wide range of domains. This is in contrast so-called "narrow" AI that may exhibit human-level capabilities, but only at specific tasks or in highly controlled environments.

Artificial General Intelligence (AGI)

Artificial Intelligence (AI) is a branch of computer science that involves creating intelligent machines that can perform tasks that –used to– require human intelligence to accomplish. It encompasses several subfields, including: machine learning, natural language processing, computer vision, and robotics.

Artificial Intelligence

Artificial Narrow Intelligence (ANI) is a term used to describe artificial intelligence (AI) systems that are designed and programmed to perform a specific task or set of tasks.

Artificial Narrow Intelligence (ANI)

An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's biological neural networks. It's a fundamental concept in machine learning and artificial intelligence.

Artificial Neural Network (ANN)

In Python, the assignment operator is represented by the = symbol, which is used to assign a value to a variable. The basic syntax of the assignment operator is: variable_name = value

Assignment Operator Python

Assignment Operator (Python)

Backpropagation is an algorithm used for training neural networks. The goal is to minimize the error of the network's output by adjusting the weights of the connections between neurons.

Backpropagation Algorithm - Neural Networks

Backpropagation

In the contexts of statistics and machine learning, "bias" refers to the systematic error or deviation of a model's predictions or estimates from the true values.

Bias in Machine Learning

Bias

The Bias-Variance Tradeoff deals with the balance between two types of errors: bias and variance –with the goal of developing models that generalize well to new, unseen data.

Bias-Variance Tradeoff

Big data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed with traditional data processing tools. The term "big data" encompasses not only the size of the data but also its variety, velocity, and complexity.

What is Big Data?

Big Data

Business Analysts work to understand the business processes, identify areas for improvement, and propose solutions to meet business objectives. They serve as a liaison between different stakeholders, including business leaders, clients, and IT teams, to ensure that projects align with business goals.

Business Analyst (BA)

Business Analytics and Data Science are related fields that involve extracting insights from data to support decision-making processes, but they have distinct focuses and methodologies.

Business Analytics (BA)

Business Intelligence (BI) refers to the set of strategies, tools, technologies, and processes used to collect, analyze, and present data in a format that helps businesses make informed decisions. BI primarily focuses on querying, reporting, data visualization, and data analytics to provide historical, current, and predictive views of business operations.

Business Intelligence (BI)

A categorical variable is a type of variable in statistics that represents categories or groups. They can be further divided into two main types: nominal and ordinal.

Categorical Variable

Clustering in machine learning is a technique used to group similar data points together based on certain characteristics or features. The goal of clustering is to identify natural groupings in the data without any prior knowledge of the group memberships.

Clustering

The command line is a text-based interface used to interact with a computer's operating system or command-line interface (CLI). It allows users to issue commands and perform various tasks by typing in text commands, rather than using a graphical user interface (GUI).

Command Line

Computer Vision is a field within data science and computer science that focuses on enabling machines to interpret and make decisions based on visual data.

Computer Vision

Unlike discrete variables, which can only take distinct, separate values, continuous variables can assume an infinite number of values within a specific interval. They can take on any real value within a given range.

Continuous Variable

A cost function, also known as a loss function or objective function, quantifies the difference between the predicted values of a model and the actual values in the training data.

Cost Function

Cross-validation is a resampling technique used in machine learning to assess the performance and generalizability of a model. It helps ensure that the model's performance metrics are not overly dependent on the specific random partitioning of the data.

Cross-Validation

Data Analysis is the exhilarating process of extracting meaning from piles of seemingly arbitrary numbers and turning them into information that is useful for decision making.

Data Analysis

A data analyst is a professional who collects, cleans, and analyzes data to help businesses make better decisions. They extract meaning from data and communicate their findings to business owners, corporate decision-makers, and other stakeholders.

Data Analyst

Data Science is an interdisciplinary field that involves the use of statistical, computational, and machine learning techniques to extract insights and knowledge from data. It combines elements from various fields such as statistics, mathematics, computer science, and domain expertise.

What is Data Science?

Data Science

A Data Scientist's work often involves applying machine learning algorithms and statistical models to solve business problems, make predictions, or uncover patterns within data.

Data Scientist

Early stopping is a regularization technique used in training neural networks. The goal of early stopping is to prevent overfitting and improve the generalization of the model.

Early Stopping

Exploratory Data Analysis (EDA)  involves the exploration, visualization, and summary of data to understand its main characteristics, identify patterns, detect anomalies, and test initial hypotheses.

Exploratory Data Analysis

Exploratory Data Analysis (EDA)

A False Negative represents an error that occurs when a test result or a classification model wrongly indicates a negative outcome when the actual situation is positive.

False Negative

A False Positive refers to an error that occurs when a test result or a classification model wrongly indicates a positive outcome when the actual situation is negative.

False Positive

Google Colaboratory, also known as Colab, is a free cloud-based platform provided by Google that allows users to write and execute Python code in a browser environment.

What is Google Colaboratory?

Google Colaboratory

Gradient Descent is an iterative optimization algorithm used to minimize the cost function or loss function in machine learning and optimization problems. It's how neural networks "learn".

Gradient Descent

A Hidden Layer refers to any layer of nodes between the input layer and the output layer in a neural network. They enable the model to represent complex data relationships.

Hidden Layer

Hyperparameters are like settings for the machine learning algorithm that are set before the training process begins. They typically remain constant while the model is trained.

Hyperparameter

Image recognition involves the identification and categorization of objects or patterns within images. The goal is to develop models that can automatically label images based on their content.

Image Recognition

In data science, imputation refers to the process of replacing missing or null values in a dataset with substituted values. Missing data is a common and can occur due to various reasons.

Imputation

In k-fold cross validation the dataset is split into multiple subsets or "folds" and then systematically each using each subset as both a training set and a validation set.

K-fold Cross Validation

K-means clustering is an unsupervised learning algorithm for assigning data points to one of a predetermined number of clusters based on the proximity of points to the cluster's average location or "centroid".

K-Means Clustering

Linear regression finds the best-fitting linear equation that describes the relationship between a dependent variable  "y" and one or more independent variables "x".

Linear Regression

Logistic regression is used primarily for binary classification, where the goal is to predict the probability of a binary outcome or event (such as Yes/No, True/False, 1/0).

Logistic Regression

A machine learning engineer specializes in designing, building, and deploying machine learning models and systems. This role combines elements of software engineering, data science, and applied mathematics.

Machine Learning Engineer (MLE)

In statistics, the "mean" is a measure of central tendency AKA the "average" of a set of values. It is calculated by summing up all values and then dividing by the number of observations.

Mean

A neural network is a type of machine learning algorithm consisting of interconnected layers of nodes or "neurons". It is said to mimic the learning processes of the human brain.

Neural Network

Notebooks are computational documents used in Data Analysis, Data Science, and general Scientific Computing that allow for textual explanation and documentation to be presented alongside code (usually Python code).

What are Notebooks in Data Science?

Notebook

One-hot encoding is used to represent categorical variables as a set of binary vectors. It is common when dealing with categorical features in machine learning models.

One-Hot Encoding

Operand

In Python, an "operator" is a symbol or a special set of characters that represents a specific action or operation to be performed on one or more operands (values or variables).

Operator (Python)

The print function in Python is used to output text, variables, or expressions to the console or standard output device (like the output section of a code cell in a notebook).

Print Function (Python)

Python is a programming language that emphasizes readability, simplicity, and ease of use. Python has gained widespread popularity in data science, and artificial intelligence.

Python Programming Language

Python

A quantile is a statistical measure that divides a dataset or probability distribution into equal-sized parts, expressing the value below which a certain proportion of the data falls.

Quantile

A variable's quartiles are a set of three values that divides it into four equal parts.  The second of the three quartiles (Q2) is also known as the "median".

Quartile

A Random Forest is an ensemble of many different decision trees which –on aggregate– are able to make more accurate and robust predictions than any single decision tree.

Random Forest

Recall, also known as sensitivity or the true positive rate, calculates the ratio of true positive predictions to the total number of actual positive instances.

Recall

A scalar refers to a single numerical value, which can be a real number or a complex number. Scalars are used to scale vectors, matrices, and other linear transformations.

Scalar (Linear Algebra)

Scalar

Snake Case is a naming convention (for naming variables, functions, files, etc). Where all letters are lowercase and words are separated by underscores. For example: snake_case_example

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Python for Beginner Data Scientists

Python Variables

The Print Function

Numbers and Math in Python

What is Machine Learning?

Strings in Python

Comments in Python

Functions in Python

String Formatting with F-Strings

Conditionals, Booleans, and If Statements

Intro to Python Lists

Intro to Python Lists - Exercises

Lists as a Sequence of Values

Coming Soon...

Code Challenges

Test your Python and Data Science knowledge

FizzBuzz

Mean of a List

Median of a List

Variance of a List

Standard Deviation of a List

Sample Covariance

Correlation Coefficient

Simple Linear Regression

Vector Dot Product

Random Matrix

Code Tutorials

Concise demonstrations of Python and Data Science concepts.

Python Basics Tutorials

Python List Functions

All Python Operators (with examples)

Why Learn Python?

What is the Best Software for Learning Python?

How to Declare a Variable in Python

Variable Naming Rules and Conventions

Use Descriptive Variable Names

Integers and Floats

Familiar Arithmetic (math) Operators

Pandas Basics Tutorials

How to iterate over (loop over) the rows of a Pandas dataframe

Data Science Encyclopedia

Use the encyclopedia to deepen your understanding of specific terms and topics.

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