365 Data Science Resources Center
Discover over 275 free data science resources to simplify your learning journey. Enhance your data and AI training with comprehensive study materials, career guidance, and expert advice.Course Notes
All Course NotesCourse notes are the perfect complementary online data science study materials. They help you grasp fundamental concepts, refresh your memory when preparing for exams, and get a taste of our learning style.
Committed to your career success and development, we at 365 Data Science support you throughout your learning journey with free resources designed to enhance and guide your education. Our rich selection covers everything from data science notes for beginners to simplified explanations of advanced topics. Choose a subject you wish to master, download our free PDF course notes, and start learning.
Intro to AI Course Notes
Our Intro to AI Course Notes provides a thorough overview of artificial intelligence fundamentals to help you master foundational concepts and get started with advanced techniques. These notes are an essential part of our Intro to AI course and cover a wide range of topics—from machine learning basics to deep learning and AI techniques—offering a solid foundation for anyone interested in AI. Key topics discussed: AI Fundamentals: Natural vs Artificial Intelligence, history, and critical concepts Data in AI: The role of structured and unstructured data and data collection methods AI Techniques: Machine learning (supervised, unsupervised, reinforcement) and deep learning AI Branches: Robotics, computer vision, and generative AI Generative AI and NLP: Development of language models and advancements in NLP These AI lecture notes are a valuable resource for students and professionals that will help you understand the intricacies of artificial intelligence and prepare you for various AI job roles. If you wish to explore AI and understand what it takes to succeed in this rapidly evolving field, download our Intro to AI Course Notes for a comprehensive guide.
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SQL DELETE Statement Notes
Our SQL DELETE Statement notes provide a comprehensive guide to the SQL DELETE Statement—essential for removing unwanted data from your databases. What is the SQL DELETE Statement? The SQL DELETE statement is a fundamental SQL command that deletes existing records in a database. These notes explain the syntax needed to write effective DELETE SQL queries. You’ll learn about the DELETE FROM WHERE conditions command, which allows you to specify the exact SQL rows to delete. Understanding how to delete a row in SQL is crucial for maintaining clean and efficient databases. One of the critical aspects covered in these notes is the ON DELETE CASCADE option, which is part of the foreign key constraint. This option ensures that if a specific value from the parent table’s primary key is deleted, all corresponding records in the child table will also be removed. This feature is essential for maintaining referential integrity within your database. The notes also compare the SQL DELETE command with other SQL commands for removing data, such as TRUNCATE and DROP. The section on TRUNCATE vs DELETE highlights the differences in performance and usage scenarios between these two commands. The DROP vs TRUNCATE vs DELETE section also provides insights into when to use each command for optimal database management.
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SQL UPDATE Statement Notes
Our SQL UPDATE Statement notes offer a comprehensive guide to the SQL UPDATE Statement—essential for modifying existing data in your databases. What is the SQL UPDATE Statement? The SQL UPDATE is a crucial SQL command used to modify existing records in a database. These notes provide detailed information on the syntax needed to write effective SQL UPDATE statements. You learn about the UPDATE table_name SET column_1 = value_1 command, which allows you to update specific columns, and the SQL WHERE condition, which ensures you only update the desired records. Additionally, this guide covers SQL best practices for handling database updates to provide accurate and efficient data management. You can execute precise and controlled updates by mastering the SQL UPDATE syntax and understanding the use of TCL commands. The notes explore the TCL COMMIT and TCL ROLLBACK commands—essential for managing transactions in SQL. The COMMIT statement saves the transaction in the database, while the ROLLBACK clause allows you to revert to the last committed state, ensuring your data remains consistent and accurate.
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SQL Insert Statement Notes
Our SQL INSERT Statement Notes offer a comprehensive guide to the SQL INSERT Statement—essential for adding new data to your databases. What is the SQL INSERT Statement? The SQL INSERT statement is a vital SQL command to add new records to a database. This resource provides detailed information on the syntax needed to write effective INSERT statements. You can learn how to update your database and add new information, including the INSERT INTO statement—allowing you to add data to specific columns—and the INSERT INTO SELECT command, which facilitates inserting data from one table into another. Download these SQL INSERT Statement notes to thoroughly understand one of SQL's most essential commands. With this resource, you'll confidently manage and add data to databases—ensuring your data management practices are efficient and effective.
Learn MoreTemplates
All TemplatesAccelerate your workflow with code-ready data science templates from 365 Data Science instructors—designed to enhance efficiency in your programming projects.
Discover a myriad of valuable resources—from a time-saving data science cheat sheet to data analysis and visualization templates in Excel and ready-to-use SQL, R, and Python coding shortcuts.
Whether you’re unsure how to approach a task or work on a time-sensitive project, these templates are designed to streamline your work and enhance productivity.
15 Ways to Visualize Revenue
Our Excel revenue visualization template offers a comprehensive array of data visualization examples—perfect for understanding your data more intuitively. What is the Excel revenue visualization template? This template is an expansive tool for visualizing revenue data in Excel—providing 15 different visualization methods and examples tailored to diverse analytical needs and presentation styles. It includes all kinds of revenue graphs—from straightforward column and pie charts to more complex data visualization methods like waterfall and Pareto charts. Each revenue chart provides a unique perspective on data, making it a versatile tool for data science students, researchers, and enthusiasts needing to present or understand complex data comprehensively. Simply input your data into any of the following revenue charts to visualize your revenue in Excel. Column chart: Revenue development over time Sparklines chart: Revenue development over time Treemap chart: Revenue by product over time Stacked area chart: Demonstrate the contribution of revenue components Clustered column chart: Show revenue split by category and over time Pie chart: Revenue breakdown in a single period Doughnut chart with total: Revenue breakdown in a single period with total Waterfall chart: Compare annual revenue in a year-over-year growth chart Scatter plot: Plot revenue vs marketing spend Bubble chart: Compare the number of products by company and revenue Combo chart: Track revenue development and margins over time Pie of Pie: Provide a macro and a micro breakdown Map chart: Geographical distribution of revenue Pareto chart: Cumulative revenue contribution by revenue category Funnel chart: Website conversion rate You can choose the ideal method to visualize and extract valuable insights from your data, such as revenue growth charts or breakdowns according to categories like location.
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Data Science Shortcuts Cheat Sheet
Discover how to boost your productivity using this data science shortcuts cheat sheet with over 2,000 workarounds in Python IDEs, such as Jupyter, Spyder Rodeo, PyCharm, and Atom, compatible with various operating systems. Amplify your proficiency in R with R Studio shortcuts, streamline MATLAB operations, and manage databases efficiently with SQL shortcuts. Enhance data visualization in Tableau, easily manage Excel spreadsheets, and conduct statistical analyses seamlessly in SPSS and SAS. This data science shortcuts cheat sheet lets you speed up your everyday tasks while achieving your goals.
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Multi-Dimensional Dictionaries in Python
The following is a program implementing a multi-dimensional dictionary in Python. The notebook shows how to retrieve keys and values from the dictionary, how to create a new key:value pair and how to loop through the keys and the values using a for-loop. Some other related topics you might be interested in are One-Dimensional Dictionaries in Python, Dictionary Comprehension in Python, Using Counter - a Dictionary Subclass in Python. The Multi-Dimensional Dictionaries in Python free template is among the topics covered in detail in the 365 Program.
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Methods in Python - Functions Inside Classes in Python
In Python, we can design and create our own objects with the help of classes. In this free notebook, we will design a class and define two functions inside - two methods. In Python, methods are accessed through the dot-notation. They help interact with an object and are an essential part of a class. Some other related topics you might be interested in are Defining classes in Python, The Pass-Statement in Python, Creating a Constructor in Python - the INIT method, Class Variables in Python, Inheritance in Python. The Methods in Python – Functions Inside Classes in Python template is among the topics covered in detail in the 365 Program.
Learn MoreInfographics
All InfographicsNavigate your way through the sea of weighty data and AI content with the help of our 365 Data Science infographics. Simplify and enhance your learning experience through memorable visual representations—available to download for free.
Explore a treasure trove of resources:
- Data science infographics for field navigation
- AI infographics for simplifying complex topics
- ChatGPT infographics with prompts
- Data analytics infographics for role guidance
These visuals are designed to make complex information easily understandable, enhancing your learning experience.
World of Open-Source Generative AI Infographic
The rise of artificial intelligence and its generative capabilities have transformed how we develop, deploy, and interact with AI solutions. Our World of Open-source Generative AI infographic provides a comprehensive overview of critical open-source AI tools and resources shaping the future of AI. From foundational models to orchestration tools, this visually appealing infographic details the open-source generative AI landscape—highlighting the advantages and considerations for each category. In this free downloadable AI infographic, you’ll explore the following elements critical to open-source generative AI. Foundation Models: Understand the role of large, pre-trained models like LLaMA and GPT-2 and how they’re used as bases for further adaptation. Datasets: Learn about open-source datasets crucial for training and validating AI models. Vector Databases: Discover how these databases facilitate quick and scalable similarity searches. Orchestration Tools: See how tools streamline the construction of LLM-powered applications. Evaluation Tools: Assess AI model performance and reliability with cost-effective solutions like DeepEval, deepchecks, arize, and Langdock. Community and Ethics: Engage with the vibrant AI community and understand the ethical frameworks guiding open-source AI development.
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Gradient Descent Infographic
Our Gradient Descent Infographic provides an in-depth overview of an essential method widely applied in machine learning. What is Gradient Descent? Gradient Descent is an optimization algorithm that finds the local minimum of a function. It’s used in machine learning for cost function minimization. Gradient descent is essential to various machine learning models used by data scientists and machine and deep learning engineers. The infographic offers a well-rounded definition of gradient descent, machine learning applications, and the method's intuition. It further outlines the step-by-step process of the gradient descent algorithm—starting with initial coefficient values and repeating the process until converging on a minimum. The infographic also highlights the gradient descent assumptions and compares the pros and cons of stochastic gradient descent—a variant that updates the coefficients more frequently.
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Regularization Infographic
Our regularization infographic provides a comprehensive overview of an essential machine-learning technique. Regularization is a technique that helps prevent models from overfitting by introducing constraints into the loss function. For instance, in logistic regression, regularization techniques can be used to optimize the model's performance. The infographic provides a clear, concise definition of regularization, highlighting its role in balancing overfitting and underfitting. It illustrates how adding noise and improving generalization enhance model performance on new data. It also gives in-depth look at various regularization methods, including L1 regularization (Lasso regularization) and L2 regularization (Ridge regularization). It explains their formulas and how they influence coefficient adjustment in model training. Additionally, the infographic explores Elastic Net, a method blending Ridge and Lasso regularization, along with a 2-stage regularization process.
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Logistic Regression Infographic
This logistic regression infographic provides a clear and comprehensive overview of a standard statistical method used to predict binary outcomes. Unlike simple linear regression, logistic regression excels at deciphering the connection between multiple independent variables and one dependent variable. It simplifies the comprehension of intricate data relationships, making it a perfect gateway to machine learning. Logistic regression handles non-linear relationships effectively, delivering robust results without intricate hyperparameter adjustments.
Learn MoreCareer Guides
All Career GuidesSave yourself hours of browsing the internet and reading fragmented, outdated information. Our meticulously curated data and AI career guides assist you at every stage of your journey, providing the knowledge, support, and expert advice to find a job that aligns with your needs and interests.
We thoroughly research the in-demand skills, required qualifications, job outlook, and career progression opportunities in data and AI. Whether you want to pursue a career as a data analyst, data scientist, or AI professional, you’ll find the necessary job guides and resources to kickstart your journey. Leverage our free PDF career guides as your roadmap to designing your professional development.
Machine Learning Engineer Career Guide 2024
Discover your pathway to becoming a machine learning engineer with our comprehensive career guide—designed to equip you with the essential knowledge and skills needed to land your dream job in this field. This guide provides an in-depth overview of the machine learning engineer career—detailing everything from the key roles and responsibilities to the essential skills and qualifications required. Here’s what’s included: Discover what machine learning engineers do daily, which can help you determine if this is the right career for you. Gain insights into the current ML engineer job market, learn about various career paths, and understand the educational and professional milestones needed to excel. Learn the machine learning engineer requirements and discover how to create an impressive portfolio with this comprehensive guide. Receive practical advice on: Crafting a standout resume Preparing for machine learning engineer interviews Effectively networking to enhance your job prospects With sections dedicated to beginners and experienced professionals, our guide ensures you have all the resources needed to pursue a successful machine learning career path. Download this machine learning engineer career guide now to discover how you can shape your future in one of the most promising careers in the tech industry.
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Machine Learning Engineer Cover Letter Template
Our machine learning engineer cover letter template provides a structured and effective way to showcase your qualifications for roles in machine learning and artificial intelligence. Designed for clarity and impact, this template highlights your experience, skills, and achievements—ensuring your cover letter stands out. It includes a professional introduction, detailed sections to outline your relevant expertise, and a strong closing statement that conveys your enthusiasm for the role. With this machine learning engineer cover letter, you can confidently apply to any position, presenting yourself as a well-qualified candidate ready to make an impact. Download this cover letter template for free to boost your job-search this year.
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How to Learn AI. A Beginner's Guide
Recent technological developments have spurred both excitement about the world of opportunities and fear of becoming obsolete. While the adoption of artificial intelligence has led to the automation of many tasks, new roles continue to emerge daily. Upskilling is the only way to stay current in this AI-driven world, and those who know how to adapt and leverage new technologies will thrive in the future job market. Our comprehensive How to Learn AI guide helps you navigate the dynamic work environment by introducing you to a future-proof strategy for getting started with AI.
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Data Analyst Career Guide
Understanding and interpreting data has become more crucial as we progress into the digital age. Data analysts use their skills to analyze complex datasets and provide valuable insights that drive informed decision-making and promote business growth. Our data analyst career guide explores what it takes to become a successful data analyst, including the role description, necessary qualifications and skills, and the data analyst job outlook for 2023. The true value of the guide is in its extensive section concerning your application process from resume to interview. You’ll learn how to structure your resume, write a winning cover letter, and provide exceptional answers to data analyst interview questions.
Learn MorePractice Exams
All Practice ExamsDiscover a plethora of online exams that will test your current knowledge and ability to solve data science problems. Evaluate your skills online at no cost with SQL mock tests, Excel and NumPy exam questions, and more.
Whether you’re studying for an exam, preparing for an interview, or looking to check your knowledge level, our data science practice exams will ensure you’re up to the task. Timed to simulate a real-life experience, the data science test questions verify your theoretical understanding and technical skills.
Identify areas for improvement to enhance your skills, critical thinking, and problem-solving abilities—bringing you closer to achieving your goals.
Excel Mechanics
Imagine if you had to apply the same Excel formatting adjustment to both Sheet 1 and Sheet 2 (i.e., adjust font, adjust fill color of the sheets, add a couple of empty rows here and there) which contain thousands of rows. That would cost an unjustifiable amount of time. That is where advanced Excel skills come in handy as they optimize your data cleaning, formatting and analysis process and shortcut your way to a job well-done. Therefore, asses your Excel data manipulation skills with this free practice exam.
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Formatting Excel Spreadsheets
Did you know that more than 1 in 8 people on the planet uses Excel and that Office users typically spend a third of their time in Excel. But how many of them use the popular spreadsheet tool efficiently? Find out where you stand in your Excel skills with this free practice exam where you are a first-year investment banking analyst at one of the top-tier banks in the world. The dynamic nature of your position will test your skills in quick Excel formatting and various Excel shortcuts
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Hypothesis Testing
Whenever we need to verify the results of a test or experiment we turn to hypothesis testing. In this free practice exam you are a data analyst at an electric car manufacturer, selling vehicles in the US and Canada. Currently the company offers two car models – Apollo and SpeedX. You will need to download a free Excel file containing the car sales of the two models over the last 3 years in order find out interesting insights and test your skills in hypothesis testing.
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Confidence Intervals
Confidence Intervals refers to the probability of a population parameter falling between a range of certain values. In this free practice exam, you lead the research team at a portfolio management company with over $50 billion dollars in total assets under management. You are asked to compare the performance of 3 funds with similar investment strategies and are given a table with the return of the three portfolios over the last 3 years. You will have to use the data to answer questions that will test your knowledge in confidence intervals.
Learn MoreCheck out the best online resources for data science according to our students and expert team of instructors—available to download and use for free.
Introduction to Data Science Disciplines
The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.
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Intrо to Machine Learning
State-of-the-art machine learning algorithms have opened a whole new realm of possibilities for businesses to optimize their processes and create new product/service features, that maximize competitive advantages. Used in recommendation systems, fraud detection, spam filtering, self-driving cars, to name a few, these advanced algorithms are only getting more popular by the day. Take this free machine learning practice exam and test your knowledge on supervised, unsupervised and reinforcement machine learning, and their applications.
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Excel Basics
Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.
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Useful Tools in Excel
What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.
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Fundamentals of Descriptive Statistics
Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.
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Intro to Jupyter Notebooks
Jupyter is a free, open-source interactive web-based computational notebook. As one of the most popular coding environments for Python and R, you are inevitably going to encounter Jupyter at some point in you data science journey, if you have not already. Therefore, in this free practice exam you are a professor of Applied Economics and Finance who is learning how to use Jupyter. You are going to be tested on the very basics of the Jupyter environment like how to set up the environment and some Jupyter keyboard shortcuts.
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Fundamentals of Inferential Statistics
While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.
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Jupyter Notebook Shortcuts
In this free practice exam you are an experienced university professor in Statistics who is looking to upskill in data science and has joined the data science apartment. As on of the most popular coding environments for Python, your colleagues recommend you learn Jupyter Notebook as a beginner data scientist. Therefore, in this quick assessment exam you are going to be tested on some basic theory regarding Jupyter Notebook and some of its shortcuts which will determine how efficient you are at using the environment.
Learn MoreMost Popular Data Science Resources
Check out the best online resources for data science according to our students and expert team of instructors—available to download and use for free.
Introduction to Data Science Disciplines
The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.
Learn MoreIntrо to Machine Learning
State-of-the-art machine learning algorithms have opened a whole new realm of possibilities for businesses to optimize their processes and create new product/service features, that maximize competitive advantages. Used in recommendation systems, fraud detection, spam filtering, self-driving cars, to name a few, these advanced algorithms are only getting more popular by the day. Take this free machine learning practice exam and test your knowledge on supervised, unsupervised and reinforcement machine learning, and their applications.
Learn MoreExcel Basics
Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.
Learn MoreUseful Tools in Excel
What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.
Learn MoreFundamentals of Descriptive Statistics
Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.
Learn MoreIntro to Jupyter Notebooks
Jupyter is a free, open-source interactive web-based computational notebook. As one of the most popular coding environments for Python and R, you are inevitably going to encounter Jupyter at some point in you data science journey, if you have not already. Therefore, in this free practice exam you are a professor of Applied Economics and Finance who is learning how to use Jupyter. You are going to be tested on the very basics of the Jupyter environment like how to set up the environment and some Jupyter keyboard shortcuts.
Learn MoreFundamentals of Inferential Statistics
While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.
Learn MoreJupyter Notebook Shortcuts
In this free practice exam you are an experienced university professor in Statistics who is looking to upskill in data science and has joined the data science apartment. As on of the most popular coding environments for Python, your colleagues recommend you learn Jupyter Notebook as a beginner data scientist. Therefore, in this quick assessment exam you are going to be tested on some basic theory regarding Jupyter Notebook and some of its shortcuts which will determine how efficient you are at using the environment.
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