863 Data Science courses

PYTHON BASICS: COURSE DESCRIPTION Excellent for beginners, practical, in small groups of max 4 people, 1 Day Online Instructor-led. You could contact us for your prefereed date. Session 1: Python Data Types and Variables: * Primitive types; Characters & Strings; Boolean; Working with variables and its scope; Conversion and casting types in Python. * Operators and Expressions: * Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.. * Exercise: Calculate Movie Tickets for a Party, Are there enough seats in the cinema? Decision Making & Loops * If statement; If - else statement; If- elif - else statement; Nested if - else; * Exercise: Calculate the travel fee to deliver goods * The while, For loop * Jump statements: break, continue; * Nesting loops. * Exercise: Enter a password, if incorrect 3 times, you are blocked. Session 2: Data Structures * Lists. * Tuples. * Exercise: Hangman Game * Exercise: Get a word for the game from a Json File, store the high score in a Dictionary file Session 3: Files and exceptions * Exception Handling, Exception types; Using try and Except. * Files, streams: Open, Traverse, Read and Create Files: Csv, txt and Json Files. * API: Connecting to API’s. Session 4: OOP * Creating and using custom Functions. * Using parameters and return values. * Creating a Class; Creating an Object; Using an Object; Adding Instance variables; * Class Constructors; Parameterized Constructors. * Inheritance. Override. Session 5: Pandas Dataframe Basics * Getting data into a dataframe: Dict to Dataframe, Dataframe to Dict. Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets. * Getting information about the dataframe, * Filter, sort and query a Dataframes, Slicing Dataframes, * Duplicate values,Working with null-values, Sampling. * Exercise: Query the top 1000 grossing movies of the last century Session 6: Built in Functions: String, Math, Random * Python built-in functions: * Strings functions. Maths functions. Random Functions. * Exercise: Find information in prose, to get the sentiment of the prose. * Exercise: Get a word for the game from a txt File * Exercise: Win the lottery INCLUDED: * PCWorkshops's Python Programming Basics Certification * Course notes, exercises and code examples * Revision session after the course -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Python coding, interactive, instructor-led, hands-on, practical. From basics to working with professionals. Python Part Time. Python Coding. -------------------------------------------------------------------------------- Python Beginners Evenings Course COURSE SUMMARY This Python Programming course has become essential to all professional who would like to do code using Python. Learn evenings, work while studying. Work and study Python Part Time. It is a Python beginners course and teaches Python coding from scratch to intermediate. Hands-on, practical. We code some real life examples in this course. Duration: 3 Evenings, once per week for 3 weeks 6pm-8pm, of which the first date only shows on the booking. Virtual Classroom: You could attend in our classroom, or attend online via virtual classroom, by select the appropriate ticket. Who Should Attend? This course is intended for beginner programmers who need to code in Python. Fast-paced, Quite intensive. Course Outline: Session 1: Basics * Python Data Types, Variables, * Operators and Expressions: Arithmetic operators; Relational operators; Assignment operator; Logical operators. * Decision Making: If statement; If - else statement; If- elif - else statement; Nested if - else; * Using Loops: The while, while-else; do-while and the for loop; Enhanced for loop; Jump statements: break, continue; Nesting loops. Session 2: Lists. Dictionaries, Json * Python Data Structures: Lists. Data Structures: 2-d Lists, Tuples. Dictionary, Nested Disctionary. Session 3: Built-in functions: * Python Date Functions. Random. Math Functions. String Functions. Python Regex. Session 4: Database: * Connect to a database, create Database, create a table, insert, delete, update records, query a database and display results. Drop database, drop table. Session 5: Files * Exception Handling in Python: Fundamentals of exception handling; Exception types; Using try and except. * Files, streams: Create, Open, Traverse and Read Files: Csv, txt and Json Files. * API: Connecting to API’s Session 6: M ethods, Classes and Objects * Creating and using Function, Working with parameters, Calling functions. * Object-Oriented Programming (OOP) principles. Modules and Classes, Creating an Object; Using an Object; Adding Instance variables; Controlling accessibility; Naming conventions for class member.s FAQ Do I need Python on my Laptop? Ideally spoken yes, but if not possible, you could use our online platform. Can I pay by BACS? Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com Can I pay with PayPal? Yes, simply email us, a course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com Do I get an invoice? Yes, on request. Can I pay with a PO (Purchase Order Number)? Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com Do I get a certificate? Yes, a PCWorkshops Certificate Can I attend in classroom? Yes, on request, and it would incur an additional charge, depending on your location. We have offices in London, Manchester, Birmingham, Portmouth, Southend-on-Sea, Milton Keynes. Can beginners follow this course? Yes, this is a Python beginner friendly course. We offer Python courses with max 4 people, so we make sure each delegates fully understand the material covered Can I customise the course? Yes, all our Python courses are customisable. We can customise the dates, times, location. course outline. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

This FREE in-person event is structured to introduce SMEs to a variety of AI tools that are useful for improving business processes.

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. -------------------------------------------------------------------------------- Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: * We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project * We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.  Course Outline: Supervised Machine Learning: * Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine * Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: * Clustering Algorithms: K-means clustering, Hierarchical Clustering * Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) * Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: * Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting * Reinforcement learning Algorithms: Q-Learning * Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: * The first part of a Machine Learning project understands the data and the problem at hand. * Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: * Python Machine Learning Certificate on completion   * Python Machine Learning notes * Practical Python Machine Learning exercises and code examples * After the course, 1 free, online session for questions or revision Python Machine Learning. * Max group size on this Python Machine Learning is 4. -------------------------------------------------------------------------------- REFUND POLICY No Refunds

This FREE in-person event is structured to introduce SMEs to a variety of AI tools that are useful for improving business processes.

This Python BootCamp is Instructor-led, Practical. In the12-week Python course, learn start to in-depth, leading to a good Python career. -------------------------------------------------------------------------------- PYTHON BOOTCAMP: This 12-week Python Boot Camp is a practical, instructor-lead program, covering Python from start to in-depth. You will be fully fluent and confident as a Python programmer. If you have more questions goto https://pcworkshopslondon.co.uk/contact.html  [http://pcworkshopslondon.co.uk/contact.html], Or contact us on training@pcworkshopslondon.co.uk [https://pcworkshopslondon.co.uk/] Customise dates, course outline, arrange installments [https://pcworkshopslondon.co.uk/contact.htm] This course will give you enough practical experience and practical projects to code, to give you full confidence to enter into the coding profession.    Duration: 3 months: * 1 Python class per week, * Plus pratical work, * Plus personal trainer-mentor for 1-1 training, * Plus e-learning materials. Final project : Practical to upload to GitHub and show-case Date and times, choose: * Fridays in London or Online , 10am - 5pm, * or Saturdays in London or Online , 12noon-6pm, * or negotiate your date Study level: Start from beginners level to in-depth, ready to work professionally. Virtual attendance:  online instructor-led  Download: Anaconda.com Pre-requisites: General computer literacy. Oracle Qualification: PCWorkshops Python Programmer Certificate Payments:  You may apply to pay in installments for this Python Training course COURSE OUTLINE Week 1 - 4: Essentials 1. Python Coding Basics 2. Object Oriented programming: Python Object Orientated programming (OOP) 3. UX Principles: UX Principles and applying it on Front-ends with TKinter 4. Specialised topics: Dates, Localization, Strings, Maths Operation , Random Number, Lambdas Week 5 - 10 : All about data 1. Python Data Structures: Lists, Tuples, Sets, Exceptions, I/O Fundamentals , Reading and Writing file 2. Database: Database principles and SQL. Database Project: Python database connections and creating a database driven project 3. Data Analytics: Numpy. Pandas for data analytics and data queries. 4. Data Analytics: Pandas data cleaning and restructuring, interacticting with Excel, Csv, Json,etc. 5. Data visualisation: MatPlotLib 6. Prediction: Machine Learning Basics Week 11 and 12: The final touch 1. Python Concurrency and Multi-threading: Threads vs. Processes, Threading Module, Threading Event, Stop a Thread, Daemon Threads, Thread-safe Queue, Thread Pools, Locks 2. Python Unit Testing 3. Optional : Replace Week 11 or 12 with Scraping using Python, ot Tkinter Front-ends INCLUDED: * PCWorkshops Python Course Certificate on completion. * Python Course notes. * Practical Course exercises, Code Examples, Online Materials. * After the course, continuous assistance with practical and preparation for exams * Max group size on this is 4. * Portfolio: Post your Python project online. * Exam preparation and interview questions MORE ABOUT THE ONLINE CLASSROOM *  Attend from your internet connection *  Instructor-led, get instant answers to your questions *  Fully interactive *  Work clearly explained with demonstrations and examples *  Practical exercises to be tried out by delegate WHAT YOU WILL GAIN: * Skills & knowledge: Python coding knowledge and understanding with good practical application   * Certification: Internal PCWorkshops Python certificate * Portfolio: You will have an online portfolio of Python projects  * Experience: Our comprehensive practical makes you job ready -------------------------------------------------------------------------------- REFUND POLICY No Refunds

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - DESIGN A DATA INGESTION STRATEGY FOR MACHINE LEARNING PROJECTS * Identify your data source and format * Choose how to serve data to machine learning workflows * Design a data ingestion solution 2 - DESIGN A MACHINE LEARNING MODEL TRAINING SOLUTION * Identify machine learning tasks * Choose a service to train a machine learning model * Decide between compute options 3 - DESIGN A MODEL DEPLOYMENT SOLUTION * Understand how model will be consumed * Decide on real-time or batch deployment 4 - DESIGN A MACHINE LEARNING OPERATIONS SOLUTION * Explore an MLOps architecture * Design for monitoring * Design for retraining 5 - EXPLORE AZURE MACHINE LEARNING WORKSPACE RESOURCES AND ASSETS * Create an Azure Machine Learning workspace * Identify Azure Machine Learning resources * Identify Azure Machine Learning assets * Train models in the workspace 6 - EXPLORE DEVELOPER TOOLS FOR WORKSPACE INTERACTION * Explore the studio * Explore the Python SDK * Explore the CLI 7 - MAKE DATA AVAILABLE IN AZURE MACHINE LEARNING * Understand URIs * Create a datastore * Create a data asset 8 - WORK WITH COMPUTE TARGETS IN AZURE MACHINE LEARNING * Choose the appropriate compute target * Create and use a compute instance * Create and use a compute cluster 9 - WORK WITH ENVIRONMENTS IN AZURE MACHINE LEARNING * Understand environments * Explore and use curated environments * Create and use custom environments 10 - FIND THE BEST CLASSIFICATION MODEL WITH AUTOMATED MACHINE LEARNING * Preprocess data and configure featurization * Run an Automated Machine Learning experiment * Evaluate and compare models 11 - TRACK MODEL TRAINING IN JUPYTER NOTEBOOKS WITH MLFLOW * Configure MLflow for model tracking in notebooks * Train and track models in notebooks 12 - RUN A TRAINING SCRIPT AS A COMMAND JOB IN AZURE MACHINE LEARNING * Convert a notebook to a script * Run a script as a command job * Use parameters in a command job 13 - TRACK MODEL TRAINING WITH MLFLOW IN JOBS * Track metrics with MLflow * View metrics and evaluate models 14 - PERFORM HYPERPARAMETER TUNING WITH AZURE MACHINE LEARNING * Define a search space * Configure a sampling method * Configure early termination * Use a sweep job for hyperparameter tuning 15 - RUN PIPELINES IN AZURE MACHINE LEARNING * Create components * Create a pipeline * Run a pipeline job 16 - REGISTER AN MLFLOW MODEL IN AZURE MACHINE LEARNING * Log models with MLflow * Understand the MLflow model format * Register an MLflow model 17 - CREATE AND EXPLORE THE RESPONSIBLE AI DASHBOARD FOR A MODEL IN AZURE MACHINE LEARNING * Understand Responsible AI * Create the Responsible AI dashboard * Evaluate the Responsible AI dashboard 18 - DEPLOY A MODEL TO A MANAGED ONLINE ENDPOINT * Explore managed online endpoints * Deploy your MLflow model to a managed online endpoint * Deploy a model to a managed online endpoint * Test managed online endpoints 19 - DEPLOY A MODEL TO A BATCH ENDPOINT * Understand and create batch endpoints * Deploy your MLflow model to a batch endpoint * Deploy a custom model to a batch endpoint * Invoke and troubleshoot batch endpoints

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.

This workshop presents recent advances in econometrics, including the use of advanced data science tools such as machine learning. It aims to foster the exchange of ideas among international and national economics and econometrics communities. The workshop provides a unique platform to explore the interplay between econometric theory and data science, and their applications to various estimation and inference problems in economics. Speakers: Timothy Armstrong Debopam Bhattacharya Nathan Canen Karim Chalak Toru Kitagawa Tatiana Komarova Sokbae "Simon” Lee Kirill Ponomarev Adam Rosen Yike Wang Organizers: Mingli Chen, Aureo de Paula, and Andrei Zeleneev

This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework.