1_Big_data_lp_l1_v1

Long-term plan unit:

 10.3A  Information Systems

School:

Date:

Teacher name:

Grade: 10

Number present:

Grade:

The topic of the lesson:

Bigdata

Learning objectives(s) that this lesson is contributing to

10.3.1.4 to assess the advantages and disadvantages of usage of Bigdata

Assessment criteria

●        Know the Bigdata

●        Know the positive and negative sides of Bigdata

●        Be able to analyze Bigdata

Success criteria

All learners will be able to know:

The notion of Bigdata

The advantages and disadvantages of Bigdata

Language objectives

Subject vocabulary and terminology:

Data analysis, data collection, storage, data, relational database

Useful phrases for dialogue / writing

Bigdata is …

Unstructured data is…

Analysis Methods ...

Technologies of work with Bigdata …

Value links

Group work , co-operation, academic honesty

Cross curricular links

English

Previous learning

 -

Course of the lesson

Planned stages of the lesson

Planned activities at the lesson

Resources

Beginning

0-10

Warm up:

 Students formulate a topic, lesson objectives, and assessment criteria to achieve the objectives of the lesson.

Glossary discussion

Big data is a tool for processing both structured and unstructured data in order to use it for specific tasks and purposes.

Unstructured data is information that does not have a predefined structure or is not organized in a specific order.

Discuss with children what they mean by this term.

Assumptions:

Big Data is data which is more than 100GB (500GB, 1TB, etc.)

Big Data is data that cannot be processed in Excel.

Big Data is data that cannot be processed on a single computer.

https://rb.ru/howto/chto-takoe-big-data/

Middle

11-25

Theoretical material

Big data - a series of approaches, tools and methods for processing structured and unstructured data of huge volumes and significant diversity for obtaining human-perceptible results that are effective in conditions of continuous growth and distribution over numerous nodes of the computer network formed in the late 2000s.

Teacher asks to give examples of Big Data (4 minutes for discussion)

Examples:

• Logs of user behavior on the Internet

• GPS signals from vehicles for the transport company

• Data taken from sensors in the Large Hadron Collider

• Digitized books in the Russian State Library

• Information on transactions of all bank customers

• Information on all purchases in a large retail network, etc.

Analysis techniques and methods applicable to Big data by McKinsey:

• Data Mining;

• Crowdsourcing;

• Mixing and integrating data;

• Machine learning;

• Artificial neural networks;

• Predictive analytics;

• Simulation;

• Spatial analysis;

• Statistical analysis;

• Visualization of analytical data.

Teacher discusses the techniques with learners

https://habr.com/ru/company/dca/blog/267361/

25-30

Theoretical material

Principles of working with big data

Based on the definition of Big Data, you can formulate the basic principles of working with such data:

1. Horizontal scalability. Since there can be as much data as possible - any system that involves processing big data should be expandable. The data volume increased by 2 times - the amount of iron in the cluster increased by 2 times and everything continued to work.

2. Fault tolerance. The principle of horizontal scalability implies that there can be many machines in a cluster. For example, Yahoo's Hadoop Cluster has more than 42,000 machines (this link can be used to look at cluster sizes in different organizations). This means that some of these machines will be guaranteed to fail. Methods of working with big data should take into account the possibility of such failures and continue to work without any significant consequences.

3. Local data. In large distributed systems, data is distributed across a large number of machines. If the data is physically located on the same server, and processed on the other - the cost of data transfer may exceed the cost of processing itself. Therefore, one of the most important design principles for BigData solutions is the principle of data locality - if possible, we process data on the same machine where we store them.

30-37

Practical task

Students are asked to find information on MapReduce. What Map Reduce is and how it is related to the topic.

Learners can come together in pairs / groups.

End

 38-40

Reflection

The learners speak in a circle with one sentence, choosing the beginning of a phrase from the reflective screen on the board:

today i found out ...

it was interesting…

it was difficult…

I was doing the job ...

I realized that ...

Now I can…

I felt that ...

I learned ... I did it ...

I could ...

I'll try…

I was surprised ...

The board

Differentiation – how do you plan to give more support? How do you plan to challenge the more able learners?

Assessment – how are you planning to check students’ learning?

Health and safety regulations

The teacher has strong support for weak students.

Most students will: argue and understand, draw conclusions when solving situational tasks.

Some learners will: be able to use one of the ways to create websites

Assessment is carried out at each stage of the lesson:

- according to the evaluation criteria, analyze the successes and difficulties that will allow the teacher to look at the lesson through the eyes of students, to analyze it in terms of value for each student.

In order to prevent fatigue in the lesson provides active activities, group work.