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Informatics Practices ( IP ) - Introduction & Scope :

Informatics Practices ( IP ) is a research oriented discipline for learners of all levels and all ages. It derives most of it's pedagogy from the main specialisation of Computer Science ( CS ). Be it Distributed Databases, Python Coding & Python Programming, Internet of Things ( IOT ), Networking components, Information Technology ( IT ), Computer Science ( CS ), Data Science ( DS ), SAAS, IAAS, PAAS, SDLC based Tools and Platforms, the subject of Informatics Practices ( IP ) has significant applications everywhere. It emphasises upon the data crunching capabilities, data processing efficiencies and data translation effectiveness strategies.

 

Raw data once meaningfully processed becomes Information ( by leveraging Information Technology IT , Computer Science CS , Programming - Java, Python , Go , R, Kotlin and others ) and upon further combination with other disciplines like Statistics, Mathematics, Science, Operations Research and other related specialisations, offer deep insights into various patterns and inter - dependencies among various participating entities. Thus, Informatics Practices ( IP ) is a critical research oriented discipline that needs to be deeply researched upon, as well as further developed.

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Following is the general Central Board of Secondary Education - CBSE / NCERT prescribed outline of the syllabus for subject Informatics Practices ( IP ) - Class 11th ( Class XI - 11 ) :

Unit 1: Introduction to Computer System
Introduction to computers and computing: evolution of computing devices,

components of a computer system and their interconnections, Input / Output devices.
Computer Memory:

Units of memory,

Types of memory – primary and secondary,

data deletion, its recovery and related security concerns.
Software:

Purpose and Types – system and application software, generic and specific purpose software.
Unit 2: Introduction to Python
Basics of Python programming,

Python interpreter - interactive and script mode,

the structure of a program, indentation, identifiers,

keywords, constants, variables, types of operators,

precedence of operators, datatypes,

mutable and immutable data types,

statements, expressions, evaluation of expressions,

comments, input and output statements,

data type conversion, debugging,

control statements: if - else, for loop
Lists: list operations - creating, initializing,

traversing and manipulating lists, list methods and built-in
functions.: len(), list(), append(), extend(), insert(), count(),

find(), remove(), pop(), reverse(), sort(), sorted(), min(), max(), sum()
Dictionary: concept of key-value pair,

creating, initializing, traversing,

updating and deleting elements,
dictionary methods and built-in functions: len(), dict(), keys(),

values(), items(), get(), update(), clear(), del()
Unit 3: Data Handling using NumPy
Data and its purpose, importance of data,

structured and unstructured data, data processing cycle,

basic statistical methods for understanding data - mean,

median, mode, standard deviation and variance.
Introduction to NumPy library, NumPy arrays and their advantage,

NumPy attributes, creation of NumPy arrays;

from lists using np.array(), np.zeros(), np.ones(), np.arange(),

indexing, slicing, and iteration;
concatenating and splitting array;
Arithmetic operations on one dimensional and two dimensional arrays.
Calculating max, min, count, sum, mean,

median, mode, standard deviation,

variance on NumPy arrays.
Unit 4: Database concepts and the Structured Query Language
Database Concepts: Introduction to database concepts and its need,

Database Management System. 
Relational data model: concept of attribute, domain,

tuple, relation, candidate key, primary key, alternate key, foreign key.
Structured Query Language: Data Definition Language,

Data Query Language and Data Manipulation Language,

Introduction to MySQL: Creating a database, using database,

showing tables using MySQL,
Data Types : char, varchar, int, float, date
Data Definition Commands: CREATE, DROP, ALTER (Add and Remove primary key, attribute).
Data Query Commands: SELECT-FROM- WHERE, LIKE, BETWEEN, IN, ORDER BY, using arithmetic,
logical, relational operators and NULL values in queries, Distinct clause
Data Manipulation Commands: INSERT, UPDATE, DELETE.
Unit 5: Introduction to the Emerging Trends
Artificial Intelligence AI, Machine Learning ML,

Natural Language Processing, Immersive experience ( AR, VR ),
Robotics, Big data and its characteristics,

Internet of Things ( IoT ), Sensors, Smart cities,

Cloud Computing and Cloud Services ( SaaS, IaaS, PaaS );

Grid Computing, Block chain technology. 

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Practicals :

Programming in Python
1.To find average and grade for given marks.
2.To find the sale price of an item with a given cost and discount (%).
3.To calculate perimeter/circumference and area of shapes such as triangle, rectangle, square and circle.
4.To calculate Simple and Compound interest.
5.To calculate profit-loss for a given Cost and Sell Price.
6.To calculate EMI for Amount, Period and Interest.
7.To calculate tax - GST / Income Tax.
8.To find the largest and smallest numbers in a list.
9.To find the third largest/smallest number in a list.
10. To find the sum of squares of the first 100 natural numbers.
11. To print the first ‘n’ multiples of a given number.
12. To count the number of vowels in a user entered string.
13. To print the words starting with a particular alphabet in a user entered string.
14. To print the number of occurrences of a given alphabet in a given string.
15. Create a dictionary to store names of states and their capitals.
16. Create a dictionary of students to store names and marks obtained in 5 subjects.
17. To print the highest and lowest values in the dictionary.
Numpy Program
18. To create an array of 1D containing numeric values 0 to 9.
19. To create a NumPy array with all values as 0.
20. To extract values at odd numbered positions from a NumPy array.
21. To create a 1-D array having 12 elements usinf arange(). Now, convert this array into a 2-D array with size 4X3.
22. To perform basic arithmetic operations on 1D and 2D array .
6.3 Data Management: SQL Commands
23. To create a database
24. To create a student table with the student id, class, section, gender, name, dob, and marks as
attributes where the student id is the primary key.
25. To insert the details of at least 10 students in the above table.
26. To delete the details of a particular student in the above table.
27. To increase marks by 5% for those students who have Rno more than 20.
28. To display the entire content of the table.
29. To display Rno, Name and Marks of those students who are scoring marks more than 50.
30. To find the average of marks from the student table.
31. To find the number of students, who are from section ‘A’.
32. To add a new column email in the above table with appropriate data type.
33. To add the email ids of each student in the previously created email column.
34. To display the information of all the students, whose name starts with ‘AN’ (Examples: ANAND, ANGAD,..)
35. To display Rno, Name, DOB of those students who are born between ‘2005- 01-01’ and ‘2005-12-31’.
36. To display Rno, Name, DOB, Marks, Email of those male students in ascending order of their names.
37. To display Rno, Gender, Name, DOB, Marks, Email in descending order of their marks.
38. To display the unique section available in the table.
 

Reference :
NCERT Informatics Practices - Text book for class - XI

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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Informatics Practices ( IP ) - Class 12th ( Class XII - 12 ) :

Unit 1: Data Handling using Pandas and Data Visualization
Data Handling using Pandas -I
Introduction to Python libraries- Pandas, Matplotlib.
Data structures in Pandas - Series and data frames.
Series: Creation of series from ndarray, dictionary,

scalar value; mathematical operations; series attributes,
head and tail functions; selection, indexing and slicing.
Data Frames: creation of data frames from dictionary of series,

list of dictionaries, text / CSV files, display,
iteration. Operations on rows and columns: add add ( insert / append) ,

select, delete ( drop column and row ),
rename, Head and Tail functions, indexing using labels,

Boolean indexing; joining, merging and concatenation of data frames.
Importing / Exporting Data between CSV files and Data Frames. ( for practical only )
Data handling using Pandas – II
Descriptive Statistics : max, min, count, sum, mean, median,

mode, quartile, Standard deviation, variance.
Data Frame operations: Aggregation, group by, Sorting,

Deleting and Renaming Index, Pivoting.
Handling missing values – dropping and filling.
Importing / Exporting Data between MySQL database and Pandas.
Data Visualization : Purpose of plotting,

drawing and saving of plots using Matplotlib (line plot, bar graph,
histogram, pie chart, frequency polygon, box plot and scatter plot).
Customizing plots: color, style (dashed, dotted), width;

adding label, title, and legend in plots.
Unit 2: Database Query using SQL
Math functions: POWER (), ROUND (), MOD ().
Text functions: UCASE () / UPPER (), LCASE () / LOWER (),

MID () / SUBSTRING () / SUBSTR (), LENGTH (),
LEFT (), RIGHT (), INSTR (), LTRIM (), RTRIM (), TRIM ().
Date Functions: NOW (), DATE (), MONTH (), MONTHNAME (), YEAR (), DAY (), DAYNAME ().
Aggregate Functions: MAX (), MIN (), AVG (), SUM (), COUNT (); using COUNT (*).
Querying and manipulating data using Group by, Having, Order by.
Operations on Relations - Union, Intersection, Minus, Cartesian Product,

JOIN ( Cartesian Join, Equi Join, Natural Join )
Unit 3: Introduction to Computer Networks
Introduction to networks, Types of network: LAN, MAN, WAN.
Network Devices: modem, hub, switch, repeater, router, gateway
Network Topologies: Star, Bus, Tree, Mesh.
Introduction to Internet, URL, WWW and its applications - Web, email, Chat, VoIP.
Website: Introduction, difference between a website and webpage,

static vs dynamic web page, web server and hosting of a website.
Web Browsers: Introduction, commonly used browsers,

browser settings, add-ons and plug-ins, cookies.
Unit 4: Societal Impacts
Digital footprint, net and communication etiquettes,

data protection, intellectual property rights ( IPR ),
plagiarism, licensing and copyright, free and open source software ( FOSS ),

cybercrime and cyber laws, hacking, phishing, cyber bullying, overview of Indian IT Act.
E-waste: hazards and management.
Awareness about health concerns related to the usage of technology. 

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Practical :
Data Handling
1. Create a pandas series from a dictionary of values and an ndarray
2. Given a Series, print all the elements that are above the 75th percentile.
3. Create a Data Frame quarterly sales where each row contains the item category, item name, and
expenditure. Group the rows by the category, and print the total expenditure per category.
4. Create a data frame based on ecommerce data and generate descriptive statistics ( mean, median, mode, quartile, and variance )
5. Create a data frame for examination result and display row labels, column labels data types of each column and the dimensions
6. Filter out rows based on different criteria such as duplicate rows..
7. Find the sum of each column, or find the column with the lowest mean.
8. Locate the 3 largest values in a data frame.
9. Subtract the mean of a row from each element of the row in a Data Frame.
10. Replace all negative values in a data frame with a 0.
11. Replace all missing values in a data frame with a 999.
12. Importing and exporting data between pandas and CSV file
13. Importing and exporting data between pandas and MySQL database
Visualization
14. Given the school result data, analyse the performance of the students on different parameters, e.g
subject wise or class wise.
15. For the Data frames created above, analyze and plot appropriate charts with title and legend.
16. Take data of your interest from an open source ( e.g. data.gov.in ), aggregate and summarize it. Then
plot it using different plotting functions of the Matplotlib library.
Data Management
17. Create a student table with the student id, name, and marks as attributes where the student id is the
primary key.
18. Insert the details of a new student in the above table.
19. Delete the details of a particular student in the above table.
20. Use the select command to get the details of the students with marks more than 80.
21. Create a new table ( order ID, customer Name, and order Date ) by joining two tables ( order ID,
customer ID, and order Date ) and ( customer ID, customer Name, contact Name, country ).
22. Create a foreign key in one of the two tables mentioned above
23. Find the min, max, sum, and average of the marks in a student marks table.
24. Find the total number of customers from each country in the table ( customer ID, customer Name,
country ) using group by.
25. Create a new table ( name, date of birth ) by joining two tables ( student id, name ) and ( student id,
date of birth ).
26. Write a SQL query to order the ( student ID, marks ) table in descending order of the marks.
7.4 Introduction to Computer Networks
27. Download, install and configure browser.
 

Reference :
NCERT Informatics Practices - Text book for class - XII

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