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

Course Intro

Python proficiency is required if you want to become a professional data scientist. Learn why Python is the most important language for both beginner data enthusiasts and professionals.

Why Learn Python for Data Science?

Why Learn Python?

Don't make the mistake of trying to install complex tools if you're a beginner. Use cloud-based notebook editors like Google Colab to immediately start learning and writing Python for Data Science –with no setup required!

What Software Should I Use for Learning Python for Data Science?

What is the Best Software for Learning Python?

Get acquainted with Google Colaboratory as a tool for writing Python for Data Science. This lesson features an overview of "notebooks" in general in addition to some Google Colab specifics.

Intro to Google Colaboratory Notebooks for Data Science

Intro to Google Colaboratory

Temzee Lessons are made to look and feel like an IPython notebook. They included video content and video transcripts about learning Python and Data Science –and also Python and Data Science exercises to practice what you're learning, and give you feedback on your code.

Intro to Temzee Lessons

Whenever we create a variable we put the name of the variable first, then a single equals sign, and then the value that we want to store to that variable on the right. The technical name for this is called "declaring a variable" or is also known as "variable assignment".

How to Declare a Variable in Python

Practice creating variables with legal variable names and saving integer values to them. Creating and working with variables is the most fundamental programming skill in any language.

Declare Variables Exercises

Having descriptive variable names gives helpful context about your Python code, makes it more readable, and is easier to maintain. Readable code is more important than concise code or even code that runs fast.

Use Descriptive Variable Names in Python

Use Descriptive Variable Names

Variables in Python can only be made up of letters, numbers, and underscores. Variable names cannot start with a number. Reserved Keywords cannot be used as variable names. Variables are case sensitive,  and should follow the snake_case convention.

Python Variable Naming Rules and Conventions

Variable Naming Rules and Conventions

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

All data must be represented numerically before it can be passed to a machine learning algorithm, so mastering working with numeric datatypes like integers and floats is one of the most fundamental skills for prospective Data Scientists

Integers and Floats in Python

Integers and Floats

Practice creating variables of both datatypes: float and int, and show that you can use the type() function to check a function's stored datatype.

Integer and Float Coding Exercises in Python

Integers and Floats Exercises

Python excels at number crunching, the most basic form of that is simple math operations like: addition, subtraction, multiplication, division, and exponentiation. We can also use parentheses to enforce order of operations.

Familiar Arithmetic (Math) Operators in Python

Familiar Arithmetic (math) Operators

Practice using python's most commonly used arithmetic operators to do basic math calculations.

Math Operators Exercises

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

Strings

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

Python For Loops

For Loops

2D Lists - Python

2D Lists

Python Built-in Modules

Fitting Machine Learning Models

Classifying Penguins with Vanilla Python

Python for Beginner Data Scientists

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

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

Snake Case (Python)

Snake Case

The t-distribution is a probability distribution for estimating population parameters when the sample size is small and the population standard deviation is unknown.

T-distribution

A t-test is a statistical test used to compare the means of two groups and determine whether there is a significant difference between them.

T-test

Tableau is widely used data visualization and business intelligence (BI) software that allows users to connect, visualize, and share data to facilitate understanding and decision-making.

Tableau

The "target" is what a predictive model aims to predict or estimate based on the input data –also called the dependent variable, response variable, outcome variable, or label.

Target

In the context of machine learning and deep learning, a tensor is a multi-dimensional array or a mathematical object that generalizes the concept of scalars, vectors, and matrices.

Tensor

A Tensor Processing Unit (TPU) is a specialized hardware accelerator designed by Google for machine learning workloads, particularly those associated with deep neural networks.

Tensor Processing Unit (TPU)

TensorBoard is a web-based visualization tool that comes with TensorFlow. It helps users understand, optimize, and debug machine learning models during the training process.

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