The field of analytics has grown in significance over the years, with companies depending extensively on data to inform their decision-making processes. Manchester city is home to various higher education institutions that offer comprehensive analytics courses. The University of Manchester offers a Master’s in Business Analytics. The course aims to equip graduates with skills in data analysis, data cleaning, data visualization, and machine learning algorithms for business decision making. The course structure comprises ten compulsory modules and a dissertation at the end of the program. Some of the modules offered include Statistical Modelling, Data Mining and Machine Learning, and Business Intelligence. Students also have the opportunity to undertake a project on an applied analytics topic, working with an industry partner to apply practical analytics skills in a real-world context. Another institution offering analytics courses in Manchester is Manchester Metropolitan University, which provides an MSc in Data Science. The course is primarily for individuals looking to gain knowledge and skills in data science, machine learning, and AI. Students can choose from various modules depending on their interests, including data visualization, natural language processing, and big data processing. They also undertake a practical project that involves working with industry partners. Moreover, the University of Salford provides a Master's in Data Science and Analytics program, the course is designed to give students the practical skills required to become a data scientist. The course is taught within a world-class Data Science and Digital Technologies Department with global research reputations in artificial intelligence, cybersecurity, natural language processing, and machine learning. Some modules on offer include machine learning, data mining, computer vision, and big data analytics. The courses' benefits are the opportunity to use statistical methods, data mining, and machine learning techniques to understand data and produce insights, all with the aim of supporting data-based decision-making processes. Graduates from these courses are highly sought after across all sectors, including finance, retail, healthcare, and government. Skills in analytics are highly valued in the industry, with demand for data analytics professionals continuously growing. Manchester has a reputation for being a vibrant city, with many attractions and social venues that ensure a pleasant and enjoyable experience for students. The cost of living in Manchester is relatively affordable compared to other cities in the UK, making it an excellent choice for students looking to study and live independently on a budget. With a rapidly developing digital economy, the city presents an excellent opportunity for graduates with analytics degrees to find employment. In conclusion, Manchester presents exciting opportunities for students looking to study analytics and data science. The universities in the city offer excellent courses that provide students with practical skills and knowledge that will ready them for a career in the rapidly growing digital economy. With affordable living costs, vibrant city ambiance and growing demand for data professionals, Manchester is ideal for students looking to excel in the field of analytics.
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- Overview of data analysis and its importance in business
- Types of analytics: Descriptive, Predictive, Prescriptive
- Role of data in decision-making processes
- Introduction to common tools: Tableau, PowerBI, Excel
- Ethical considerations in data collection and analysis
- Data sources: Primary and secondary data
- Data collection methods (surveys, web scraping, databases)
- Data cleaning techniques (handling missing values, outliers)
- Data transformation and feature engineering
- Data storage concepts (structured vs. unstructured data)
- Descriptive statistics: Mean, median, mode
- Data visualization basics (histograms, scatter plots)
- Identifying data patterns and trends
- Outlier detection and handling methods
- Correlation and causation analysis
- Inferential statistics and probability theory
- Hypothesis testing (t-tests, chi-square tests, ANOVA)
- Measures of central tendency and variability
- Confidence intervals and margin of error
- Regression analysis: Linear and logistic regression
- Principles of effective data visualization
- Types of charts and their uses (bar, line, pie, heatmaps)
- Designing dashboards for different audiences
- Interactive visualization techniques
- Data storytelling for impactful presentations
- Time series analysis and forecasting methods
- Clustering and segmentation analysis
- Decision trees and classification techniques
- Introduction to machine learning in business analytics
- Model evaluation and selection
- Basics of SQL for data manipulation
- Creating databases and relationships
- Aggregating data with SQL (GROUP BY, JOIN)
- Data modeling for business intelligence (star and snowflake schemas)
- Case study: Building a business model with SQL
- Connecting and preparing data in Tableau
- Creating basic visualizations (charts, maps)
- Advanced Tableau functions (LOD calculations, table calculations)
- Building interactive dashboards and stories
- Publishing and sharing visualizations on Tableau Server/Online
- Introduction to PowerBI workspace and components
- Data import and transformation with Power Query
- Data modeling and relationships in PowerBI
- Creating and customizing visualizations
- Publishing and collaborating on PowerBI Service
- Selecting a real-world dataset for analysis
- Defining business questions and objectives
- Conducting data analysis and visualization
- Presenting findings in a comprehensive dashboard
- Peer review and feedback on project