Update 03-Program-structure-overview.md batch7 (#396)

* Update 03-Program-structure-overview.md batch7

* Update 03-Program-structure-overview.md

* Update 03-Program-structure-overview.md

docs/_docs/Starters Academy (LDSSA)/03-Program-structure-overview.md

@@ -6,91 +6,66 @@ order: 3
 
 ## Structure
 
-A Starters Academy is made of one Bootcamp, 6 to 7 specialisations, and 1 capstone. 
+The Starters Academy is made of one Bootcamp, 5 mandatory and 1 optional specialisations, and 1 capstone. 
 
 ## Bootcamp 
-The bootcamp is the launching event of the Academy, where students have to learn and execute a large 
-number of small learning units, and includes one hackathon. 
-
-## Hackathon 
-A hackathon is a 1 day event where students, in teams, have to execute and present a well-posed 
-project. In order to keep the students motivated, there is a single number value that they are 
-optimising for, and they compete for position on a leaderboard. Teams are assigned at random, as 
-students needs to learn also how to be able to work with different types of people. 
+The bootcamp is the launching event of the Academy, where students attend online classes with Academy instructors.
 
 ## Specialisation 
-A specialization is a set of 3 big learning units and a hackathon, centred on a particular topic 
-(e.g. time series). 
+A specialization is a set of learning units and a hackathon, centred on a particular topic 
+(e.g. time series). The first specialization consists of 14 mandatory and 2 optional small learning units. The other specializations have 3 big learning units.
+All specializations end with a hackathon.
+
+## Hackathon 
+A hackathon is a 1 day event where students, in teams, solve a data science problem. The students receive a dataset and
+have to set up and optimize a model. The teams competes for the best model performance as measured by a single score. 
+Teams are assigned at random, as students also need to learn how to work with different types of people. 
 
 ## Capstone
 The capstone is the final project of the Starters Academy. It requires each student to individually 
 execute a project, which will be graded. See more details under 
 [Capstone Project](../05-Capstone-Project).
 
 ## Learning units
-
-A Learning Unit is a concept developed in the Academy. It is composed of the following: 
+A Learning Unit is composed of the following: 
 
 ### 1. Learning Notebooks (Jupyter Notebooks)
-The high granularity teaching should be done in the Learning Notebooks. These should be sufficient 
-to teach the materials on their own. We should assume that notebooks will be used in future for 
-reference by the students, and therefore shouldn't assume that they remember a presentation or any 
-other immediate context. 
-
-It is critical that Learning Notebooks also teach only the new concepts, and refer to previously 
-learned concepts to the respective learning units. As happens everywhere in the Academy, there 
-should be no scope bloat, and the Learning Notebook should explain only the materials from the 
-Learning Unit, and under no circumstances include curiosities or un-requested material. 
-
-Optionally, a final section called _"If you want to learn more (optional)"_ can be added to guide 
-interested students in their independent studies.  
+The high granularity teaching should happen in the Learning Notebooks. These should be sufficient 
+to teach the materials on their own and serve as a reference. The Learning Notebooks teach only new concepts, 
+and refer to previously learned concepts to the respective learning units. 
+A final section called _"If you want to learn more (optional)"_ is included in some notebooks to guide 
+interested students in further studies.  
 
 ### 2. Exercises with expected output (Jupyter Notebooks) 
 Exercises are notebooks that cover the material taught in the same learning unit's Learning Notebooks. 
 They are based on the Coursera idea of providing an expected output and ensuring the student only 
 needs to worry about a highly granular piece of material. 
 
-Most exercises will demand an exact answer (having therefore to control for random seeds, etc), 
-while exercises which more concerned with workflow may have a slightly more open objective, such 
-as exceeding a certain performance metric. Exercises should be done individual, and will be graded 
-by an auto-grader. This auto-grader should test on the general case, as getting the expected output 
-right can be done with brute-force (e.g. returning the expected output). 
+Most exercises will demand an exact answer while exercises which more concerned with workflow may have 
+a slightly more open objective, such as exceeding a certain performance metric. 
+Exercises should be done individually, and are graded by an auto-grader.
 
 Example of exercise with expected output: 
 ![Example of exercise with expected output](https://image.ibb.co/chQ7Kn/Screen_Shot_2018_04_01_at_3_03_42_PM.png)
 
 Any learning unit requires a grade of at least 80% to be considered passed, with an unlimited number 
-of attempts (but which has an end date for attempts). 
+of attempts (but with a deadline). 
 
-### 3. Presentation (optional)  
-Presentations only happen in the Small Learning Units, as they are the ones that are taught in person. 
+### 3. Presentation 
+Presentations only happen in Specialisation 1 in the form of virtual classes.
 Presentations are short, motivating introductions to the topic, which should explain only the 
-trickiest concepts, without going into every detail of what is being taught. 
+concepts and provide insights without going into implementation details.
 
 The main objective of a presentation is to ensure that the students understand "why" they are 
 learning a topic, rather than teaching the full topic. 
 
 ## Big vs small learning units 
-Learning units must be highly modular, and serve to teach highly related concepts. There are two 
+Learning units are modular and serve to teach highly related concepts. There are two 
 types of Learning Units: 
 
 ### 1. Small Learning Units (SLU) 
-SLUs are taught during the Bootcamp, and take 1 hour. They are the only Learning Units to contain 
-in-person presentations. 
-The expected time split for students is: 
-- 15 minutes of presentation
-- 20 minutes of teaching material
-- 25 minutes of exercise. 
+SLUs are taught in Specialisation 1 and are accompanied by a presentation. The expected time split for students is 1 - 2 hours per unit.
 
 ### 2. Big Learning Units (BLU) 
-BLUs form the backbone of the Academy after the Bootcamp ends. They do not contain presentations, as they will 
-be done exclusively in remote mode. The total time a student is expected to spend on a BLU ranges starts at a 
-minimum of 5 hours, with an expected time split for students is: 
-
-- 2 hours of teaching materials 
-- 3 hours of exercise   
-
-However, it can easily extend to 10 hours, which is the average reported by previous students throughout
-different specializations.
-
-A BLU should completely prepare the students for the Hackathon. 
+BLUs form the backbone of the Academy after the Bootcamp ends. They do not contain presentations, as they are
+done in remote mode. The total time a student is expected to spend on a BLU is 5 - 10 hours.