82 Sciences courses about Learning in Leicester delivered Online

Building Data Science Products? Think Business First Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Building and Scaling a Data Science Culture As your data and AI teams scale from one to thousands of employees, you will encounter roadblocks along the way. From handling messy data to productionization and customer adoption, these obstacles can delay or even derail otherwise strong teams. Drawing on experiences gleaned from hundreds of clients, Michael Li presents a framework that successful companies have embraced to build and scale their data teams. The talk goes over how organizations progress along three maturity curves: Analytical, Operational, and Organizational. As enterprises strive to move along each of these maturity curves, they must solve various organizational challenges and develop new capabilities and skills in order to become data-driven organizations. We will provide key takeaways for managers and executives for each step of the maturity curves. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Building and Scaling a Data Science Culture As your data and AI teams scale from one to thousands of employees, you will encounter roadblocks along the way. From handling messy data to productionization and customer adoption, these obstacles can delay or even derail otherwise strong teams. Drawing on experiences gleaned from hundreds of clients, Michael Li presents a framework that successful companies have embraced to build and scale their data teams. The talk goes over how organizations progress along three maturity curves: Analytical, Operational, and Organizational. As enterprises strive to move along each of these maturity curves, they must solve various organizational challenges and develop new capabilities and skills in order to become data-driven organizations. We will provide key takeaways for managers and executives for each step of the maturity curves. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Embarking on the 'International Development Studies Course' is akin to embarking on a grand journey through the intricate maze of global progression. At its core, this course provides a comprehensive dissection of the myriad concepts that constitute the realm of international development. From the foundational theories that underpin development to the influential actors, economic strategies, social dimensions, and the ever-crucial element of environmental sustainability, every module is meticulously curated. Furthermore, the course doesn't shy away from exploring the multifaceted relationship between globalisation, ethical considerations, and the pressing issue of inequality. Learning Outcomes Comprehend the fundamental theories and concepts that drive international development. Identify key players and institutions influencing global developmental trajectories. Evaluate economic strategies and their implications in the global developmental context. Understand the social, political, and governance aspects pivotal to development agendas. Analyse the implications of globalisation, ethical dilemmas, and disparities in the world of development. Why buy this International Development Studies Course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this International Development Studies Course for? Individuals keen on understanding global growth paradigms and their complexities. Those with an academic or professional interest in economics, politics, and social studies. Aspiring researchers and academics focused on development studies. Policy makers and strategists aiming to influence global developmental agendas. Environmental enthusiasts wishing to understand sustainability within the developmental framework. Career path Development Researcher: £30,000 - £45,000 Policy Analyst: £35,000 - £50,000 Sustainability Advisor: £40,000 - £60,000 Globalisation Specialist: £45,000 - £65,000 Governance Consultant: £50,000 - £70,000 International Development Consultant: £55,000 - £75,000 Prerequisites This International Development Studies Course does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Understanding Development Concepts Understanding Development Concepts 00:20:00 Module 02: Actors and Institutions in International Development Actors and Institutions in International Development 00:22:00 Module 03: Economic Approaches to Development Economic Approaches to Development 00:19:00 Module 04: Social Dimensions of Development Social Dimensions of Development 00:20:00 Module 05: Political and Governance Perspectives Political and Governance Perspectives 00:21:00 Module 06: Environmental Sustainability and Development Environmental Sustainability and Development 00:22:00 Module 07: Globalisation, Inequality, and Development Ethics Globalisation, Inequality, and Development Ethics 00:22:00

The International Development Diploma Level 3 provides a comprehensive understanding of key concepts, challenges, and actors involved in international development. The course covers topics such as global health, governance, gender, poverty, economic development, environment, migration, and theories of development. Learning Outcomes: Analyze the measurement and ethical considerations in international development practices. Understand the relationship between governance and development and its impact on societies. Explore the intersection of global health issues and their significance in development efforts. Examine the role of gender in development and strategies for promoting gender equality. Investigate global poverty, inequality, and their implications for development initiatives. Gain insights into economic development strategies and their effects on different economies. Recognize the importance of environmental sustainability in the context of development projects. Explore the role of innovation and technology in driving positive development outcomes. Understand the impact of international migration on development patterns and challenges. Study various theories of development and their relevance to global development processes. Identify key international development actors and their roles in shaping development agendas. Why buy this International Development Diploma Level 3? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the International Development Diploma Level 3 there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This International Development Diploma Level 3 course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This International Development Diploma Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This International Development Diploma Level 3 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This International Development Diploma Level 3 is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Module 01: Measurement and Morality in International Development Measurement and Morality in International Development 00:19:00 Module 02: Governance and Development Governance and Development 00:28:00 Module 03: Global Health and Development Global Health and Development 00:27:00 Module 04: Gender and Development Gender and Development 00:25:00 Module 05: Global Poverty, Inequality and Development Global Poverty, Inequality and Development 00:26:00 Module 06: Economic Development Economic Development 00:30:00 Module 07: Environment and Development Environment and Development 00:24:00 Module 08: Innovation and Technology Innovation and Technology 00:28:00 Module 09: International Migration and Development International Migration and Development 00:22:00 Module 10: Theories of Development and Globalisation Theories of Development and Globalisation 00:27:00 Module 11: An International Development Actor An International Development Actor 00:29:00 Assignment Assignment - International Development Diploma Level 3 00:00:00

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Embark on a journey into the world of Library Science. Acquire knowledge in cataloguing, research techniques, and digital archiving, empowering you to excel in this dynamic field.

Discover a comprehensive Neurodiversity Glossary, your go-to resource for understanding neurodiversity, ADHD, autism, dyslexia, and more. Easily accessible and user-friendly, this glossary helps you navigate and master essential terms. Perfect for leaders looking to support neurodivergent individuals in their teams.

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