Overview of Analytics Course in Cambridge

Cambridge University, one of the world's most respected institutions, offers a range of analytics courses through its School of Technology and Computer Science. These courses are aimed at students looking to develop expertise in data analytics, machine learning, and artificial intelligence. One popular program is the MPhil in Advanced Computer Science with Machine Learning and AI. This one-year course is designed for students who want to specialize in cutting-edge computer science techniques such as deep learning, computer vision, and natural language processing. The course covers both theoretical and practical aspects of machine learning, including popular tools and frameworks like TensorFlow and Keras. An individual research project forms a significant part of the course, allowing students to apply their knowledge to real-world problems. Cambridge also offers a Master of Science in Applied Mathematics, Machine Learning, and Data Science. This program is aimed at students with a strong mathematical foundation who want to apply their skills to machine learning and data science. The course covers topics such as linear algebra, statistics, optimization, and stochastic processes, with a strong emphasis on applications in data science. Students can choose from a variety of modules such as deep learning, probabilistic graphical models, and natural language processing. Professional Certificate in Data Science is another course offered by Cambridge. This program is a part-time course that runs over eight months and is designed for professionals who want to upskill in data science. The course covers topics such as data exploration and visualization, statistical inference, and machine learning. It also includes hands-on labs and exercises using popular tools like Python, R, and SQL. Students who complete the course receive a Professional Certificate in Data Science from Cambridge University. For those who are just getting started with data analytics, Cambridge offers a free online course on Data Science Fundamentals. This six-week course covers the basics of data science, including data gathering, cleaning, and exploration, statistical inference, and machine learning. The course is self-paced, and students can take it from anywhere in the world. The course is suitable for anyone who wants to get started with data science, regardless of their background. Cambridge University's analytics courses are taught by world-class professors and researchers who are at the forefront of the cutting-edge techniques in data science, machine learning, and artificial intelligence. The school's emphasis on both theoretical and practical aspects of data analytics makes it an excellent choice for students who want to develop a strong foundation in the field. The courses are designed to be accessible to students with a range of backgrounds, from those just starting to those with more advanced skills. In conclusion, for students looking to specialize in data analytics, machine learning, and artificial intelligence, Cambridge University offers a range of excellent programs, from the MPhil in Advanced Computer Science with Machine Learning and AI to the online Data Science Fundamentals course. The programs are taught by world-class professors and researchers and provide students with both theoretical and practical skills. Whether you are a beginner or an experienced data professional, Cambridge's analytics courses have something to offer.
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Internship

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Live Project

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Certificate

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Career Assistance

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Expiry Period

Lifetime
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Skillfloor Course Training Process

Internship Certificate

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Completing this Analytics Course certifies your proficiency in data analysis, statistical methods, and essential analytics tools. This certification shows you can transform raw data into actionable insights, improving decision-making in various business contexts. It indicates your understanding of data visualization, predictive modelling, and data-driven strategy development, providing you with the skills to excel in analytics. This credential reflects your dedication to continuous learning and commitment to leveraging data for strategic advantage.

Analytics Tools Covered

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Why Choose SKILLFLOOR for Analytics in Cambridge?

Why Course Training in Skillfloor

Syllabus

- 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

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