30 Data Science courses in Potters Bar

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

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 course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

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.

Basic Python syntax and principles of Object Orientated Programming. Most attendees are in-work IT Professional. Private individuals are also very welcome. Evening courses also running. Our Style: Hands-on, Practical Location: Online, Instructor-led Download: anaconda.com Duration: 6 weeks, 1 evening per week, 6pm - 8pm Times: arrange a time for your time zone

Python Course, code the Hangman. Basic Python Course covers in one hour the coding of an interesting app. Any one very welcome. Our Style: Hands-on, Practical Location: Online, Instructor-led Download: anaconda.com Duration: 1 Hour Times: arrange a time for your time zone

Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online. Learn to code a Password Validator in 1 hour. 

ChatGPT, along with other AI tools, aims not to replace the human touch in management, but to enhance it. By addressing repetitive, daily tasks, these tools free up managers to concentrate on core responsibilities like strategic decision-making, team development, and innovation. As we move further into the digital age, integrating tools such as ChatGPT isn't a luxury; it's the future of proactive leadership. In this guide, we'll delve into 10 practical ways through which AI can elevate your efficiency and refine the quality of your work. Gain familiarity with prominent AI tools in the market Efficiently compose and respond to emails Generate concise summaries of complex reports and data. Obtain quick insights, data, and research across varied topics Streamline the writing of articles, training notes, and posts Craft interview tests, form relevant questions, and design checklists for the hiring process 1 Streamlining emails An inbox can be a goldmine of information but also a significant time drain for managers. Here's how to optimise it: Drafting responses: Give the AI a brief, and watch it craft a well-structured response. Sorting and prioritising: By employing user-defined rules and keywords, ChatGPT can flag important emails, ensuring no vital communication slips through the cracks. 2 Efficient report writing Reports, especially routine ones, can be time-intensive. Here's a smarter approach: Automate content: Supply key data points to the AI, and let it weave them into an insightful report. Proofreading: Lean on ChatGPT for grammar checks and consistency, ensuring each report remains crisp and error-free. 3 Rapid research From competitor insights to market trends, research is a pivotal part of management. Data synthesis: Feed raw data to the AI and receive succinct summaries in return. Question-answering: Pose specific questions about a dataset to ChatGPT and extract swift insights without diving deep into the entire content. 4 Reinventing recruitment Hiring can be a lengthy process. Here's how to make it more efficient: Resume screening: Equip the AI to spot keywords and qualifications, ensuring that only the most fitting candidates are shortlisted. Preliminary interviews: Leverage ChatGPT for the initial rounds of interviews by framing critical questions and evaluating the responses. 5 Enhancing training Especially for extensive teams, training can be a monumental task. Here's how ChatGPT can assist: Customised content: Inform the AI of your training goals, and it will draft tailored content suitable for various roles. PowerPoint design: Create visually appealing slide presentations on any topic in minimal time.

This course covers the essential Excel data skills. Our course is interactive, instructor-led. This Excel Data course is on offered on intermediate level of Excel and concentrates on the Data Analytical capabilities of Excel. These concepts are daily used by data analysts and is a basic essential skill for data professionals. By the end, you'll be able to use Excel for analytics, data summaries, trends and statistics. You will create Excel data analytical spreadsheets and samples that you can use as examples at work.