Data Science is one of the hottest professions of the decade and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. The program consists of 10 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualisation, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including: Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, predicting successful rocket landing, dashboard and interactive mapping

Project Description: Clearwater State University offers a wide variety of degree programs, from online degrees to a doctorate in education. Programs are offered in the streams of the arts, education, business & nursing. Some key strategic goals of the University are: 1) Increase enrolment of students 2) Improve retention, progression and graduation rates 3) Recruit better academically qualified undergraduate and graduate students 4) Increase external funding and recognition Skills used: All skills used in different stages of the project are mentioned below: Data Understanding, Data Cleaning, and Visualisation: R language, Excel, Tableau Data Modelling: Python, Machine Learning(Ensembles Include: Boosted trees, Random Forest and Ensemble Stacking using Pystacknet) StoryTelling: Powerpoint, Python Highlights: 1) Exploratory Data Analysis showing key associations 2) Segment Analysis showing key drivers of attrition 3) Results of the statistical model along with business interpretations and recommended interventions

This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Applied Statistics” in Queen Mary University of London (QMUL), London, United Kingdom. The project covered the descriptive statistical analysis, data analysis and visualisations in R programming for the “njgolf” dataset. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.

Workshop materials for NUSC Summer School 2025 focused on Advanced Data Analysis with R. Develop your statistical analysis and data visualisation skills alongside modelling skills through the R programming environment and related tools.

This repository is designed to provide a comprehensive introduction to R programming, tailored for data analysis and statistical computing. It includes a collection of tutorials, examples, and exercises that guide users through fundamental R concepts, data manipulation, visualisation, and advanced techniques.

The workshop aims to equip social scientists and humanities researchers with the fundamental skills needed to conduct research using R and is designed for absolute beginners. You need no prior knowledge of R. The first day will cover basic R programming and data wrangling and the second day will be on data visualisation and statistical analysis.

The workshop aims to equip social scientists and humanities researchers with the fundamental skills needed to conduct research using R and is designed for absolute beginners. You need no prior knowledge of R. The first day will cover basic R programming and data wrangling and the second day will be on data visualisation and statistical analysis.

SQL , R platform (R Development Core Team, 2017) and Python are the most widely used programming languages at Cefas for data exploration, visualisation, modelling and statistical analysis. The methods developed using these programming languages can be made accesible using Github, packages, R Markdown and Python Jupiter Notebooks for the creation of interactive documents or web user interface using R Shiny apps . These web products allow users to interact with science methods and outputs, without any requirement for software or a need to understand complex code. R Shiny allows scientists to create web data tools without deep web programming knwoledge and therefore establishing a straight communication with the scientific communitiy , decision makers and other stakeholders. Cefas Open Science Framework (OSF) Seedcorn projects aims to develop a set of descriptive , good practice documents and training material designed for scientist, IT experts, project managers and business developer. The OSF includes a catalogue of recommended open source software available in Cefas and provides the required systems to store, analyse, publish and access scientific methods and data.