3129 Development courses in Edinburgh delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Join Matt Rivers in his Soul Writing for Healing course and learn to release stress, let go of old emotions and become more creative.

Using Mindfulness at Work for Productivity Course Overview This course explores how mindfulness techniques can significantly boost productivity and wellbeing in the workplace. Learners will discover how to cultivate a mindful approach to daily tasks, manage stress effectively, and maintain focus amidst distractions. Through engaging lessons, participants will gain practical strategies such as morning mindfulness habits, single-tasking, and stress reduction methods designed to improve concentration and time management. The course aims to empower individuals to enhance their work performance, establish a healthy work-life balance, and develop resilience to everyday challenges. By completing this course, learners will be equipped with the knowledge and skills to foster a calm, productive mindset that supports sustained professional growth and personal satisfaction. Course Description This comprehensive course covers a broad range of mindfulness strategies tailored for workplace productivity. Beginning with an introduction to mindfulness fundamentals, the curriculum progresses to actionable habits such as morning routines, waking up earlier, and effective journaling. Learners will explore how to overcome procrastination through mindful awareness and implement single-tasking to improve focus and output quality. The course also addresses managing life challenges and reducing stress through mindfulness practices integrated into daily work and evening routines. Participants will benefit from a structured learning experience designed to deepen self-awareness, enhance emotional regulation, and improve time management skills. With a clear, professional approach, this course equips learners to integrate mindfulness seamlessly into their work life, driving both efficiency and wellbeing. Using Mindfulness at Work for Productivity Curriculum Module 01: Introduction Module 02: How to Stay Happy and Productive with Mindfulness Module 03: Five Morning Mindfulness Habits Module 04: Five Tips for Waking Up Earlier Module 05: Finding a Healthy Work Life Balance Module 06: Life Challenges Module 07: Journal Module 08: How to Become Stress Free At Work Module 09: Practicing Mindfulness as a Nighttime Routine Module 10: Single Tasking: What It Is and How It Helps Module 11: The Power of a Brain Dump Module 12: Procrastination and Mindfulness for Productivity (See full curriculum) Who Is This Course For? Individuals seeking to improve concentration and reduce work-related stress. Professionals aiming to enhance productivity and emotional resilience. Beginners with an interest in mindfulness and personal development. Anyone wanting to establish a better work-life balance and wellbeing at work. Career Path Wellbeing Coach or Mindfulness Trainer Human Resources Specialist focusing on employee wellness Office Manager or Team Leader seeking productivity improvement Personal Development Consultant Professionals in high-pressure roles looking to manage stress effectively

Diploma in Python Programming Course Overview The Diploma in Python Programming offers an in-depth exploration of Python, one of the most versatile and in-demand programming languages. This course is designed to provide learners with a strong foundation in Python, covering essential concepts such as data structures, functions, libraries, and file handling. Learners will gain the skills necessary to write Python code to solve real-world problems, enabling them to create applications, automate tasks, and perform data analysis. By the end of the course, learners will have the practical knowledge to use Python effectively for various programming tasks in both professional and personal settings. Course Description This comprehensive course begins with the basics of Python programming, guiding learners through essential concepts such as syntax, data types, and conditional statements. Learners will progress to more advanced topics, including file handling, data storage structures, and error handling. Key modules like the creation of user functions, working with external libraries, and implementing Python in database management provide valuable skills that can be directly applied in the workplace. This course also covers essential tools such as command prompt usage, Jupyter notebooks, and package management in Python. By the end of the course, learners will have developed the confidence and competence to apply Python across various domains, including software development, data analysis, and system automation. Diploma in Python Programming Curriculum Module 01: Introduction to Python Programming Module 02: Getting Started with Python Module 03: Conditional Branching with Python Module 04: Importing External/Internal Library in Python Module 05: Project Rock Paper and Scissors Module 06: Strings Operation in Python Module 07: Date and Time in Python Module 08: File Handling, Read and Write Using Python Module 09: Data Storage Structures: Tuple, List, and Dictionary Module 10: Writing User Functions in Python Module 11: Sending Mail Module 12: Import Tricks in Python Module 13: Import Operating System and Platform Module 14: Exceptions Handling in Python Module 15: Installing Packages and Scheduling in Python Module 16: Database in Python Using SQLite Module 17: Running Programs from Command Prompt and Jupyter Notebook Module 18: Conclusion (See full curriculum) Who is this course for? Individuals seeking to develop a foundational understanding of Python programming. Professionals aiming to enhance their programming skills for career advancement. Beginners with an interest in software development, data analysis, or automation. Anyone looking to pursue a career in programming or technology. Career Path Software Developer Data Analyst Automation Engineer Python Programmer Database Administrator IT Specialist

Course Overview The British Sign Language (BSL) Level 1 & 2 course offers a comprehensive foundation for learners keen to develop effective communication skills within the Deaf community. This course covers key vocabulary, everyday conversations, and cultural understanding across two recognised qualification levels. Through engaging modules, learners will build confidence in signing across a range of topics including greetings, family, transport, work, health, and leisure activities. The course provides a flexible learning experience, enabling learners to study at their own pace while gradually advancing from basic to more complex sign language structures. By the end of the course, learners will be able to confidently engage in conversations, describe people, express opinions, and interpret dialogues with greater fluency. Whether for personal growth, professional development, or community involvement, this course equips learners with the communication tools needed to create more inclusive and supportive environments for Deaf individuals. Course Description The British Sign Language (BSL) Level 1 & 2 course is structured to guide learners through a progressive journey, starting with the alphabet, fingerspelling, and simple daily interactions, before advancing to complex dialogues and storytelling. Key topics include family life, the home environment, weather, transport, work, hobbies, shopping, health, and holidays. Learners will engage with a variety of video dialogues—both with and without voice-over and subtitles—designed to enhance visual communication skills and comprehension. This learning experience not only builds practical sign language ability but also deepens understanding of Deaf culture, encouraging effective and respectful communication. With a focus on clarity, repetition, and gradual development, learners will acquire the skills needed to hold structured conversations, ask and respond to questions, and share detailed personal or professional information in BSL. The course aims to support learners in becoming confident and culturally aware communicators across different social and professional contexts. Course Modules Module 01: British Sign Language (BSL) Level 1 Online Course Module 02: Alphabet Fingerspelling and Names Practice Module 03: Greetings Module 04: Family, Question Forms and Family Story Module 05: Rooms in the House Vocabulary Module 06: Colours Module 07: Questions and Statements about the Home Module 08: Animals Module 09: Numbers and Money Module 10: Time and Months of the Year Module 11: Describing Ages Module 12: Weather Module 13: Transport Module 14: Directions Module 15: Hobbies Module 16: Work Module 17: Food and Drink Module 18: Dialogues (No Voice, No Subtitles) Module 19: Dialogues (With Voice and Subtitles) Module 20: Bonus: 5 Stories (With Voice and Subtitles) Module 21: Bonus: 5 Stories (No Voice Over) Module 22: Level 2 British Sign Language Module 23: Family Vocabulary Module 24: Describing People and Animals Module 25: Activities at School Module 26: Jobs and Activities at Work Module 27: Time Module 28: Activities in the Home Module 29: Leisure Activities Module 30: Opinions, Likes and Dislikes Module 31: Illnesses and Health Module 32: Eating and Drinking Module 33: Shopping and Spending Module 34: Travel and Holidays Module 35: Level 2 Dialogues (Without Voice and Subtitles) Module 36: Level 2 Dialogues (With Voice Over and Subtitles) Module 37: 5 Stories (No Voice Over and No Subtitles) Module 38: 5 Stories (With Voice Over and Subtitles) Module 39: Level 2 BSL Dialogues and Stories (See full curriculum) Who is this course for? Individuals seeking to communicate confidently with the Deaf community. Professionals aiming to enhance communication skills in inclusive workplaces. Beginners with an interest in sign language and Deaf culture. Teachers, healthcare workers, and public service providers. Parents, family members, or friends of Deaf individuals. Career Path BSL Interpreter (after further study and qualifications) Communication Support Worker Deaf Support Assistant Special Educational Needs (SEN) Teaching Assistant Community Support Worker Customer Service Advisor in accessible services Healthcare and Social Care Assistant Voluntary roles supporting Deaf organisations

The course covers investigation and risk assessment of asbestos-contaminated soils and sites, including waste classification and land remediation. It will cover the current HSE and EA legislation and guidance, assessing risk to health from asbestos in soils and how to assess the land, analysis types and interpretation, and remedial actions.

We discuss ideas and projects that use the arts to bring positive change to people who are or have been homeless. We are holding monthly global video meetings to give people in our network, or simply interested in arts and homelessness, a chance to get together remotely. The meetings are relaxed and fun and a place to share news, ideas and creativity. This year we are adding some amazing training sessions and professional development to the schedule. We are also cahnging from two sessions to just the one at 2pm every last wednesday of the month. Come and enjoy the space, learn some new things and maske some new friends.

Survivor and whistleblower of multigenerational human trafficking exposes systemic, organised abuse (human trafficking) in places that are purposed for helping vulnerable people– including victims of trafficking and refugees in the UK. Providing next-level education on the societal structures which enable this abuse, what precisely has led to a global legacy of abuse, and what is needed to correct it. Highly engaging and motivating event to empower the everyman to do their part in abolishing human trafficking. If you want a world without abuse, you have a role in creating it. Find out what that is today. The victims can't wait for tomorrow.

Join us for "Go from Zero to Hero: Mindset & Confidence" webinar and learn how to transform your mindset and boost your confidence.

Conversation with........................... June O' Sullivan & Mona Sakr.