65 Courses

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills.

This course aims to teach you how to use Python for machine learning and data science.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it's often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. ADDITIONAL COURSE DETAILS: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. AN OVERVIEW OF PYTHON * Why Python? * Python in the Shell * Python in Web Notebooks (iPython, Jupyter, Zeppelin) * Demo: Python, Notebooks, and Data Science GETTING STARTED * Using variables * Builtin functions * Strings * Numbers * Converting among types * Writing to the screen * Command line parameters FLOW CONTROL * About flow control * White space * Conditional expressions * Relational and Boolean operators * While loops * Alternate loop exits SEQUENCES, ARRAYS, DICTIONARIES AND SETS * About sequences * Lists and list methods * Tuples * Indexing and slicing * Iterating through a sequence * Sequence functions, keywords, and operators * List comprehensions * Generator Expressions * Nested sequences * Working with Dictionaries * Working with Sets WORKING WITH FILES * File overview * Opening a text file * Reading a text file * Writing to a text file * Reading and writing raw (binary) data FUNCTIONS * Defining functions * Parameters * Global and local scope * Nested functions * Returning values ESSENTIAL DEMOS * Sorting * Exceptions * Importing Modules * Classes * Regular Expressions THE STANDARD LIBRARY * Math functions * The string module DATES AND TIMES * Working with dates and times * Translating timestamps * Parsing dates from text * Formatting dates * Calendar data PYTHON AND DATA SCIENCE * Data Science Essentials * Pandas Overview * NumPy Overview * SciKit Overview * MatPlotLib Overview * Working with Python in Data Science ADDITIONAL COURSE DETAILS: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Python for Data Science: Hands-on Technical Overview (TTPS4873) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Become a data scientist with the in-demand skills of Python, JavaScript, and Microsoft SQL. This comprehensive course will teach you the essential skills you need to succeed.

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Python Programming from Scratch with MySQL Database is a beginner-friendly course designed to teach you everything you need to know to start with Python programming and MySQL databases. Using these powerful tools, you'll learn how to build dynamic web applications and websites.

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

Duration 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create data-driven applications. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution. Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. LESSON 1: SELECTING AN OBJECT-ORIENTED PROGRAMMING APPROACH FOR PYTHON APPLICATIONS * Topic A: Implement Object-Oriented Design * Topic B: Leverage the Benefits of Object-Oriented Programming LESSON 2: CREATING OBJECT-ORIENTED PYTHON APPLICATIONS * Topic A: Create a Class * Topic B: Use Built-in Methods * Topic C: Implement the Factory Design Pattern LESSON 3: CREATING A DESKTOP APPLICATION * Topic A: Design a Graphical User Interface (GUI) * Topic B: Create Interactive Applications LESSON 4: CREATING DATA-DRIVEN APPLICATIONS * Topic A: Connect to Data * Topic B: Store, Update, and Delete Data in a Database LESSON 5: CREATING AND SECURING A WEB SERVICE-CONNECTED APP * Topic A: Select a Network Application Protocol * Topic B: Create a RESTful Web Service * Topic C: Create a Web Service Client * Topic D: Secure Connected Applications LESSON 6: PROGRAMMING PYTHON FOR DATA SCIENCE * Topic A: Clean Data with Python * Topic B: Visualize Data with Python * Topic C: Perform Linear Regression with Machine Learning LESSON 7: IMPLEMENTING UNIT TESTING AND EXCEPTION HANDLING * Topic A: Handle Exceptions * Topic B: Write a Unit Test * Topic C: Execute a Unit Test LESSON 8: PACKAGING AN APPLICATION FOR DISTRIBUTION * Topic A: Create and Install a Package * Topic B: Generate Alternative Distribution Files

Duration 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create data-driven applications. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution. Python© continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. LESSON 1: SELECTING AN OBJECT-ORIENTED PROGRAMMING APPROACH FOR PYTHON APPLICATIONS * Topic A: Implement Object-Oriented Design * Topic B: Leverage the Benefits of Object-Oriented Programming LESSON 2: CREATING OBJECT-ORIENTED PYTHON APPLICATIONS * Topic A: Create a Class * Topic B: Use Built-in Methods * Topic C: Implement the Factory Design Pattern LESSON 3: CREATING A DESKTOP APPLICATION * Topic A: Design a Graphical User Interface (GUI) * Topic B: Create Interactive Applications LESSON 4: CREATING DATA-DRIVEN APPLICATIONS * Topic A: Connect to Data * Topic B: Store, Update, and Delete Data in a Database LESSON 5: CREATING AND SECURING A WEB SERVICE-CONNECTED APP * Topic A: Select a Network Application Protocol * Topic B: Create a RESTful Web Service * Topic C: Create a Web Service Client * Topic D: Secure Connected Applications LESSON 6: PROGRAMMING PYTHON FOR DATA SCIENCE * Topic A: Clean Data with Python * Topic B: Visualize Data with Python * Topic C: Perform Linear Regression with Machine Learning LESSON 7: IMPLEMENTING UNIT TESTING AND EXCEPTION HANDLING * Topic A: Handle Exceptions * Topic B: Write a Unit Test * Topic C: Execute a Unit Test LESSON 8: PACKAGING AN APPLICATION FOR DISTRIBUTION * Topic A: Create and Install a Package * Topic B: Generate Alternative Distribution Files