RBSE SS Informatics Practices study plan

Write CREATE TABLE statements with correct data types (CHAR, VARCHAR, INT, DECIMAL, DATE) and constraints (PRIMARY KEY, NOT NULL, UNIQUE, DEFAULT) for a three-table schema of your own design

Execute INSERT, UPDATE and DELETE statements and verify each change with a SELECT COUNT(*) check

Construct SELECT queries using WHERE with comparison, BETWEEN, IN, LIKE and IS NULL operators, plus multi-key ORDER BY with ASC/DESC

Debug ten deliberately broken queries by identifying syntax and logical errors and rewriting each one correctly

Build Series objects from lists, dictionaries, NumPy arrays and scalars, and set custom index labels on each

Build DataFrame objects from dictionaries of lists, lists of dictionaries and a two-dimensional array with explicit row and column labels

Apply label-based (loc) and position-based (iloc) indexing and slicing to extract rows, columns and individual scalar values

Perform element-wise arithmetic between two Series and explain how index alignment produces NaN values

Iterate over a DataFrame using items(), iterrows() and itertuples(), and state which is fastest for row access

Apply the aggregate functions COUNT, SUM, AVG, MIN, MAX with DISTINCT and with NULL handling, and predict results before running them

Group rows with GROUP BY and filter groups with HAVING, distinguishing clearly between WHERE and HAVING in written notes

Write INNER, LEFT, RIGHT and self-joins across your three tables using table aliases and equi-join conditions

Use string functions (UPPER, LOWER, CONCAT, SUBSTR, LENGTH), numeric functions (ROUND, MOD, POWER) and date functions (NOW, DATE_FORMAT, DATEDIFF) inside SELECT and WHERE clauses

Write three single-row subqueries and one correlated subquery and explain the row order of evaluation

Import a CSV file with read_csv, controlling the separator, header row, index column and column data types, then export a filtered result with to_csv and confirm the file reopens cleanly

Detect and handle missing data using isnull(), dropna() and fillna(), and detect and remove duplicate rows with duplicated() and drop_duplicates()

Merge two DataFrames with merge() on a shared key using inner, left, right and outer joins, and compare the row counts produced

Compute descriptive statistics with describe(), mean(), median(), mode(), std() and value_counts() on at least three numeric columns

Sort and rank a DataFrame with sort_values() and rank(), then reset and set the index

Design a small database of at least three tables and populate it with 20 or more realistic rows per table

Write 12 analytical SQL queries against that database covering filtering, grouping, aggregation and at least one join

Re-implement two of those SQL queries in Pandas using read_sql or an equivalent CSV export, and confirm the outputs match

Produce a groupby-based Pandas analysis computing at least four aggregate measures per category

Plot a line chart, a bar chart, a histogram and a scatter plot with matplotlib pyplot using the project dataset

Add titles, axis labels, legends, grid lines and custom colours to every figure, and save each as a PNG file

Choose the appropriate chart type for five stated analytical questions and justify each choice in one sentence

Interpret each plot in 40 or more words, naming the trend, outlier or correlation visible and its practical meaning

Classify networks by scale (PAN, LAN, MAN, WAN) and by topology (star, bus, ring, mesh, tree), sketching each topology and stating one advantage and one limitation

Describe the function of hubs, switches, routers, gateways and modems, and map each to the OSI or TCP/IP layer where it operates

Explain the TCP/IP model, the roles of TCP and UDP, HTTP request-response flow, and three network security measures such as firewalls, encryption and strong authentication

Define digital footprint, cybercrime categories, IPR, open-source versus proprietary licensing, and e-waste, giving one concrete Indian example for each

Assemble the project report with title, objective, data source, methodology, SQL and Pandas code listings, charts, findings and conclusion

Re-run every SQL query and Pandas script from weeks 1 to 5 and fix any that now fail

Complete one full-length timed mock paper covering all seven domains and score it

Summarise each wrong answer into a one-line correction card for final revision