Organisational Data Science Course
Organisational Data Science Course
Required English proficiency：IELTS 6.0 / TOEFL iBT 80－ / CEFR B2 equivalent
|Essential Math and Statistics
The basic mathematical knowledge required for data science, focusing on probability theory (including conditional probability, mutually exclusivity, and Bayes’ theorem), distributions (including normal distributions and central limit theorems, binomial distributions, and alternative distribution models), and variables (including mean, variance, and standard deviation).
A summary of the various coding languages, and a look into how to program key functions and variables in Python, including lists, tuples, dictionaries, and Boolean and conditional variables.
|Predictive Analytics: Linear Modeling
How we can use linear algebra to make predictions about the behavior of a complex system, or analyse data sets. Looking at linear regression, multiple linear regression (MLR), and logistic regression.
|Predictive Analytics: Machine Learning
How we can use artificial intelligence (AI) to predict increasingly more accurate results using historical data, without specifically programming more for accurate results. Includes looking at the differences between supervised and unsupervised learning, and the various methods of machine learning, such as clustering (random forest, SVM, and k-means), dimensionality reduction, transfer learning, reinforcement learning, neural nets and deep learning, and natural language processing.
|Visualizing Data and Exploratory Analysis
How we display data, and how to use a data visualization package, using real data to map trends in real life scenarios. A deep dive into how both data and analysis can be affected by biases and systematic errors, and how these can greatly affect visualizations.
Learning the principles of data mining – or how we process large data sets to extract trends and patterns. Looking at how these patterns obtained can be used in business intelligence and real time analytics. Looking at how data mining techniques can be integrated with machine learning.
Understanding the various tiers of data warehousing, and how it can be used as a central hub for information. How it differs from databases and data lakes, and when each of these data storage structures would be appropriate.
Understanding how we collate and analyze the more complex data sets of big data, both structured and unstructured. Introduction to tools such as Hadoop, Spark, and Flink.
How to weave data and analytics into text, or how to present data in a way that tells an accurate story of what is occurring. Learning how to utilize the techniques taught in previous sessions to collect specific data to understand a concept, and how to relay that to other people without expertise.
How cloud computing has started to dominate the fields of managing, storing, distributing and processing data. Learning Cloud platforms such as AWS and Microsoft Azure, and core concepts such as IaaS (Infrastructure as Service), PaaS (Platform as Service), and SaaS (Software as Service).
Attendance to the course
Organisational Data Science Online Course
The tutors have degrees from Cambridge University Judge Business School, Oxford University, or London School of Economics and they are all excellently qualified to teach Data Science.
Cambridge Data Science Course is a course to learn about topics such as Big Data, Data Mining, Crypto Currencies, Data Visualization, GDPR, Digital Encryption, and Artificial Intelligence to drive logical decision-making, with the support of the University of Cambridge Mathematics Faculty and Cambridge Judge Business School.
Data Science has grown rapidly in importance and plays a pivotal role in helping organisations leverage data, turning raw information into actionable insights. It is an interdisciplinary field, using both statistics and computer sciences, to help solve some of the world’s most serious problems.
Let us take an example from the picture of Rabbit-duck illusion below.
One and the same picture can be a rabbit or a duck, depending on how it is construed.
Here, an objective analysis is required in order to prove that it is a rabbit or that it is a duck. It is not convincing or persuasive just to say that it ‘looks like’ a rabbit or a duck.
Data analysis and statistics have become a focal point of public attention, with their broad range of use cases across industries and applications, from commercial, retail, legal, medical and even political fields. Data Science has become an inevitable pillar within many organisations, including those of the giants, such as Google, Facebook, Microsoft and Amazon.
・For one-to-one lesson course, tuition fee is 750GBP (Deposit 100GBP is included) per student.
・For a group (5 students at minimum) lesson course, tuition fee is 300GBP (Deposit 100GBP is included) per student.
・Required English proficiency：IELTS 6.0 / TOEFL iBT 80－ / CEFR B2 Equivalent（No need to submit your score.）
・On the Certificate of Attendance, name of the student, name of the course, dates of the course, lesson hours, and the grades are written.
・The teacher will start the lesson and finish the lesson on time.
・The student won’t have a make-up lesson if he/she gets late for the lesson or were absent from the lesson.
・Prepare your PC has a good internet access.
・Make sure you attend the lesson in a quiet environment.
・More than 1 course will be delivered at the same time on the same dates. The student won’t be able to change the course once his/her course has started.