Method: searched YouTube with yt-dlp (2-3 phrasings per topic),
pulled auto-captions, read the transcripts of the top candidates.
Durations are from yt-dlp metadata. &t= links jump to
where the key explanation starts.
| # | Topic | Winner | Length | Covers |
|---|---|---|---|---|
| 1 | Kernel vs shell/GUI, what OS does | https://www.youtube.com/watch?v=RN4gwf1_Ggg&t=20s | 6:38 | kernel, shell (CLI/GUI), chef/waiter analogy |
| 2 | Process vs thread | https://www.youtube.com/watch?v=1myWEH8IGt4&t=24s | 2:49 | process = own memory, threads share memory |
| 3 | Virtual memory / paging | https://www.youtube.com/watch?v=qeOBEOBJREs&t=6s | 1:55 | RAM to disk swap, paging, bigger-than-RAM |
| 4 | Device drivers + interrupts | drivers: https://www.youtube.com/watch?v=gWXah3BmV5M ; interrupts: https://www.youtube.com/watch?v=O0mRZeUjEhk | 1:32 + 2:50 | driver = translator; interrupt = signal, handler |
| 5 | CPU scheduling FCFS/SJF/RR | https://www.youtube.com/watch?v=1BGD9GFC4Bo&t=130s | 7:14 | all three, coffee-shop analogy, time quantum |
| 6 | Deadlock + 4 conditions | https://www.youtube.com/watch?v=fMrtygDGOho | 2:01 | all 4 conditions, pen/book analogy, prevention |
| 7 | Windows 11 tools | Roll Back: https://www.youtube.com/watch?v=wp6ootRHTLg ; Restore: https://www.youtube.com/watch?v=zeS_Xi3DkpQ&t=35s ; TM/EV/Cleanup: https://www.youtube.com/watch?v=uLWZnUJjeio&t=26s | 2:12 / 3:31 / 7:20 | see below |
| 8 | Linux ps/top/kill, chmod/chown | https://www.youtube.com/watch?v=WHUR4keF87M ; https://www.youtube.com/watch?v=mq33BHj08vQ | 3:58 + 4:19 | ps, top, kill; chmod, chown |
| 9 | OS security | https://www.youtube.com/watch?v=B0kB8IxL8Kw ; https://www.youtube.com/watch?v=4i7Vu4nfrAo ; https://www.youtube.com/watch?v=7BPNSMddrFg&t=16s | 2:51 + 4:40 + 1:45 | see below |
No single short video covers both well. Two short ones: - Drivers: “Drivers: Talking to Hardware”, The Software Mentor, 1:32. https://www.youtube.com/watch?v=gWXah3BmV5M . Driver = translator between general OS commands and a device’s own commands; one per device; missing or buggy driver breaks the device. Runner-up: Tech Index “What is a driver? (Software)” 3:16 https://www.youtube.com/watch?v=rRdwqCTqA1o (also covers outdated or corrupt drivers, ties to Roll Back Driver). - Interrupts: “Computer Interrupts Explained”, GCSE GURU, 2:50. https://www.youtube.com/watch?v=O0mRZeUjEhk . Interrupt = signal from hardware or software that makes the OS pause and run the interrupt handler; examples (printer jam, key press); handler queues by priority; explains the multitasking illusion. Runner-up with chef analogy and polling contrast: Chantal Cox “Polling/Interrupt/DMA differences” 3:00 https://www.youtube.com/watch?v=LNPBr3WvuNg . - Gap: neither video links driver to interrupt handler (the driver provides the handler). That is one sentence to remember.
ps aux,
top, kill -15 (SIGTERM, graceful) vs
kill -9 (SIGKILL, forced).No single short video covers every item. Three that together do: - Authentication vs authorization, MFA, least privilege: “Authentication vs Authorization: What’s the Difference?”, Infosec Simplified, 2:51. https://www.youtube.com/watch?v=B0kB8IxL8Kw . Airport analogy (passport = authentication, boarding pass = authorization); MFA = something you know / have / are; least privilege under authorization. - UAC, BitLocker, firewall, standard vs admin accounts, Defender: “CompTIA A+ 1202 Windows Security Settings Rapid Review (Obj 2.2)”, Techvault Academy, 4:40. https://www.youtube.com/watch?v=4i7Vu4nfrAo . UAC = security guard asking for admin credentials; BitLocker = full-disk encryption, unreadable if stolen; Windows firewall controls ports/apps; use a standard account and elevate only when needed. Last 1 min covers Active Directory and Group Policy (skip). Shorter UAC + BitLocker only: Admin365 2:32 https://www.youtube.com/watch?v=9VboMJWiYuQ . - Patching + MFA: “Security Maintenance Through Patching, Hardening, And MFA”, Full IT Operations Courses, 1:45. https://www.youtube.com/watch?v=7BPNSMddrFg&t=16s . Patches close known holes, turn off unneeded services, MFA adds a second check. Dry narration, but accurate and short.
Method: searched via yt-dlp on the mini (the Mac fails with SSL errors), pulled auto-captions, read transcripts of 2-4 candidates per topic. Caveat: YouTube started returning HTTP 429 mid-run. These were NOT transcript-read: Techdose linear-vs-binary (sSYQ1H9-Vks), Segmentation fault bubble-vs-selection (tM-emzdOlX4), udiprod insertion-vs-bubble (TZRWRjq2CAg), Aaron Jack data structures (sVxBVvlnJsM), Kantan Coding log n / n log n (wjDY5RbILno, K3NluEdHkao). Judged by title only, so not chosen.
| # | Topic | Winner | Len | URL | Covers |
|---|---|---|---|---|---|
| 1 | Big-O basics | Bro Code, Learn Big O notation in 6 minutes | 6:25 | https://www.youtube.com/watch?v=XMUe3zFhM5c&t=154s | O(1), log n, n, n log n, n^2, drop small terms, steps vs data |
| 2a | Array / list cost | Tech With Tim, Array Time Complexity | 6:32 | https://www.youtube.com/watch?v=B2KusJcbVIg&t=121s | index O(1), insert/remove O(n) |
| 2b | Hash table | Michael Sambol, Hash tables in 4 minutes | 3:52 | https://www.youtube.com/watch?v=knV86FlSXJ8&t=0s | avg lookup O(1), worst O(n), dict = hash table |
| 2c | Stack vs queue | Turing Blackboard, Stack vs Queue in 2 Minutes | 2:01 | https://www.youtube.com/watch?v=ElBl0iDcnQA&t=0s | LIFO/FIFO, push/pop, enqueue/dequeue, uses |
| 2d | Balanced BST O(log n) | none verified | - | - | not found |
| 3 | Linear vs binary search | KnowledgeCatch, Linear Search vs Binary Search | 3:35 | https://www.youtube.com/watch?v=AhPD3N3vUcM&t=31s | linear O(n) any data, binary needs sorted, halving, when to use which |
| 4 | Insertion sort | Hello Byte, Insertion Sort in 3 Minutes | 3:01 | https://www.youtube.com/watch?v=JSEk1vr3IAU&t=0s | method, best O(n) sorted, worst O(n^2), in place |
| 5 | Merge sort | Michael Sambol, Merge sort in 3 minutes | 3:03 | https://www.youtube.com/watch?v=4VqmGXwpLqc&t=148s | divide/merge, n log n, temp arrays |
| 6 | Quick sort | Michael Sambol, Quick sort in 4 minutes | 4:24 | https://www.youtube.com/watch?v=Hoixgm4-P4M&t=203s | pivot/partition by swaps, avg n log n, worst n^2 |
| 7 | Bubble / selection | Coding with Lewis, first 3 min only | 10:48 (watch 0:00-3:08) | https://www.youtube.com/watch?v=rbbTd-gkajw&t=0s | bubble n^2 (best n), selection n^2 always, insertion small lists |
| 8 | Choosing a sort | Shweta Jindal, Comparison of sorting techniques | 11:55 (watch 5:40-11:20) | https://www.youtube.com/watch?v=gkM-_ldy52I&t=340s | time, space, in-place, stable, per sort |
Why: only short video I read that names all five classes in the topic list, with a plain-English reason for dropping small terms (“n+1 becomes n”, 3 steps is not O(3), so O(1)). The linear-vs-quadratic comparison (n=1000 gives 1,000 vs 1,000,000 steps) is at 277s. Misses: no explicit “nested loop = n^2” rule (only says insertion/selection/bubble sort are n^2). Calls the 5 in “5n” case only implicitly. Runner-up: Fireship g2o22C3CRfU (1:40, https://www.youtube.com/watch?v=g2o22C3CRfU). Accurate and has “loop within a loop = quadratic”, binary search = log, index lookup = O(1). No n log n and no clear “drop constants” (only “simplify to fastest-growing worst case”). Good 100-second primer, not enough alone. Also: Michael Sambol __vX2sjlpXU (5:13) is the best on rules: drop constants, drop low-order terms, sequential statements add, nested loop gives n^2, if/else takes the worst branch. It has no log n or n log n. Pair with Fireship if Bro Code feels slow.
Why: the only one that directly compares them and says when to use each (cheat sheet at 189s: linear for unsorted/small, binary for sorted/large). Says binary only works on sorted data; “1000 contacts = at most 10 checks”, “1M items vs ~20 steps”. Misses: does not show the middle index math. Runner-up for the middle calc: Sambol fDKIpRe8GW4 (4:00): mid = average of left and right index, round down (consistently), worked example, loop code, O(log n) via tree height. Fireship MFhxShGxHWc (2:20) also explains “open in the middle, discard half”, needs sorted array, linear loop vs log time.
Why: card-hand analogy, worked example, then best case O(n) (already sorted, n-1 comparisons), worst/average O(n^2), space O(1)/in place. Misses: never says outright “use for small or nearly sorted lists”; the best-case-O(n) line implies it. Coding with Lewis rbbTd-gkajw at 156s says “very small list… great option”. Runner-up: Sambol JU767SDMDvA (2:19). Clear animation, worst case n^2 only, no best case.
Why: divide step, merge into temporary arrays, n log n (n for the merge loop, log n for tree height). Misses: does not say stable or that it needs O(n) extra memory beyond “temporary arrays”. Those two points are covered in topic 8 (gkM-_ldy52I at 466-590s: merge sort is not in place, is stable). udiprod es2T6KY45cA (5:34) is a robot-and-balls animation of merge vs quick sort. The captions are sparse (animation), so I could not verify the middle. It shows merge sort making no random choices and quick sort being lucky/unlucky. Good visual, weak for facts.
Why: pivot, partition with left/right swaps in the same array, recursion, pivot choice (median of three), worst case n^2, average n log n. Misses: does not say “in place” or “fastest in practice” in words. Computerphile XE4VP_8Y0BU (3:23) shows why a sorted list with last-element pivot gives n^2 (136-169s) but is messy and the transcript cuts off with unrelated text at the end.
Best combined segment: Coding with Lewis rbbTd-gkajw 0:00-3:08. Bubble: worst n^2, best n if already sorted. Selection: n^2 best and worst, “always beats bubble a bit”. Insertion follows. Captions are garbled (ASR) and the video is 10:48, so stop at 3:08. Accurate but two separate videos: Sambol bubble xli_FI7CuzA (2:10) and selection g-PGLbMth_g (2:43), both n^2 with nested-loop reason. Use these if you want the algorithms shown step by step.
Why: only read video that tabulates best/worst/average time, extra space, in-place, stable and online for bubble, selection, insertion, merge, quick, heap, and ends with “pick by stability, space, speed”. Content: bubble/selection/insertion n^2 worst; merge n log n always; quick avg n log n, worst n^2 on already-sorted input; bubble and insertion best case n; stable = bubble, insertion, merge; not stable = selection, quick, heap; merge sort uses extra space. Weak: long, accent/ASR-heavy, lecture style. Says quick sort needs O(n) space and lumps it with non-in-place; the exam wants “quick sort is in place” (true, uses only stack space). Do not take that line from it. Runner-up: Coding with Lewis rbbTd-gkajw (10:48). Friendlier, covers more sorts, mentions small-list use for insertion. Its merge-sort complexity wording is garbled and it gives no stable/in-place data. Honest take: nothing short and clean exists in what I could read. A short cheat-sheet fallback: insertion = small/nearly sorted; merge = guaranteed n log n, stable, extra memory; quick = fastest average, in place, n^2 worst; bubble/selection = n^2, teaching only.
Method: yt-dlp search (2-3 phrasings per topic), then read the actual caption transcript of each finalist. Durations are exact. Caveat: partway through, YouTube started returning HTTP 429 / “confirm you’re not a bot” to this machine and the mini. Every pick below was transcript-verified. A few extra candidates could not be fetched; they are flagged “Unverified”. No pick is title-only.
URL format: https://www.youtube.com/watch?v=ID&t=SECONDSs jumps to the key part.
| ## 1. Data types, mutable vs immutable, casting, / // % No single short video covers all four sub-points well. Best set (4 short videos, about 17 min total): |
| | Sub-point | Winner | Dur | Link | |—|—|—|—| | int/float/str/bool/list/tuple/dict/set | Python Data Types Tutorial for Beginners (Amit Shivde) | 7:40 | https://www.youtube.com/watch?v=CQyO60uHcZM&t=0s (list 207s, tuple 251s, set 292s, dict 357s) | | mutable vs immutable | Easy Syntax in Python: Mutable vs Immutable Data Types (BekBrace) | 2:58 | https://www.youtube.com/watch?v=kjsSaOoUFGo&t=0s | | casting int(“42”) | Python type cast (Bro Code) | 5:09 | https://www.youtube.com/watch?v=2WZvl1R4A6g&t=18s | | / vs // vs % | Python Math Operators Explained (CodeFlow Daily) | 1:38 | https://www.youtube.com/watch?v=0knYZsIZp_c&t=0s | |
| - Types (Amit): all 8 types, one example each, type() check, says list = mutable, tuple = not, set = unique, dict = key:value. Misses: no casting, no operators. Delivery is slow but accurate. - Mutable/immutable (BekBrace): names mutable types (list, dict, set) vs immutable (int, float, bool, str, tuple), then shows list[0]=15 works and tuple[0]=115 raises TypeError. Exactly the exam idea. Misses: no string demo. Runner-up: Corey Schafer 5qQQ3yzbKp8 (7:06), strong string/list demo with memory ids, but 2.4x longer. - Casting (Bro Code): int()/float conversion, “3” * 3 gives “333” while int(“3”) * 3 gives 9, reassign to make it permanent. Misses: str()/bool() barely, no ValueError. Runner-up: Caleb Curry XDpY9aFOZ3M (5:06), has int(“5”), str(5), int(“yo dog”) error, and “hey” + 5 failing. Take Caleb if you want the error cases. - Operators (CodeFlow Daily): / gives 3.4, // gives 3, % gives 2, with a 17 chocolates / 5 friends analogy. Misses: negative-number floor behavior. Runner-up: Legion Script 4n_uBh8cEyM (5:00), / vs // vs % on 10/2 and 11/2, plus negatives. |
| ## 2. List methods, negative index, slicing, IndexError Two videos. |
| | Sub-point | Winner | Dur | Link | |—|—|—|—| | append/extend/insert/pop/remove | Python List Methods Explained for Beginners (CodeLucky) | 5:22 | https://www.youtube.com/watch?v=nhkiHfebmhs&t=20s | | negative index, slicing [1:3], IndexError | Python Lists: Indexing & Slicing (Real Python) | 6:57 | https://www.youtube.com/watch?v=KAXvMbD1Zac&t=22s (IndexError 77s, negative 89s, slicing 133s) | |
| - CodeLucky: each method defined with an example, notes each returns None and edits in place, remove deletes only the first match and raises ValueError if absent, pop returns the removed item and raises on a bad index, ends with a comparison table (211s). Misses: no explicit “append([3,4]) nests a list” demo. Narration is synthetic but accurate. - Runner-up for methods: Colleen Lewis (UIUC CS105) mpKWTnGH9Jw (6:30). Best append-vs-extend demo (append(list2) adds the list as ONE last element, 43-110s), insert past the end, remove error, pop(100) error. Rambles more. - Real Python: zero-based index, too-high index raises IndexError “list index out of range”, -1 is the last item, a[2:5] is up to but not including 5, omitted ends, stride. Runner-up (IndexError only): Coders Bring Change JZOY-5JF8S8 (1:33). |
| ## 3. Functions: params vs args, defaults, return/None, method vs function No single short video covers it. Set: |
| | Sub-point | Winner | Dur | Link | |—|—|—|—| | parameters vs arguments | Arguments and Parameters (Caleb Curry) | 2:11 | https://www.youtube.com/watch?v=i_ZEaE5vfE0&t=0s | | default parameters | Python Tutorial 7 - default parameters (Data Science Tutorials) | 2:54 | https://www.youtube.com/watch?v=KmYcvG4FfGk&t=54s | | return | Python return statement (Bro Code) | 3:31 | https://www.youtube.com/watch?v=ZnBQfF5JFDM&t=19s | | method vs function, sort() vs sorted(), None | Differences Between sorted() and sort() (Pybites) | 1:55 | https://www.youtube.com/watch?v=2W8Z6O7jsGE&t=0s | |
| - Caleb: arguments = what you pass in, parameters = the variables inside the function that receive them. Clean and correct. - Defaults: explains the “missing required positional argument” error and the fix with a default value. Captions are rough but content is correct. Alternative, not transcript-verified: Bro Code m2uURZxex3c (6:07). - Bro Code return: multiply(6,8) returns 48, print vs store in a variable. Misses: never says a function with no return gives None. The None idea appears in Pybites: sort() returns None and mutates in place, sorted() returns a new list and leaves the original alone, so sorted = built-in function, sort = list method. Runner-up: anthonywritescode JZYWtmxaCeM (4:59), more depth than a beginner needs. - Honest gap: no verified video defining “function vs method” (method = function attached to an object, called with a dot). Pybites gives the practical example. |
## 4. Modules / packages: import numpy as np, from x import y, pip
install Winner: Python Modules Explained: pip install vs import (Neuron
UCI, ISOM2600), 6:39,
https://www.youtube.com/watch?v=o0jW15Ob4Fk&t=73s - Covers: module
vs package vs library (51s), install once vs import every session (73s),
pip is a terminal command, import math +
math.sqrt, as alias (225s),
from math import sqrt (244s), NameError when the call does
not match the import form. Only candidate hitting all of it. Misses:
examples use math, not numpy; academic tone. - Runner-up (shortest for
pip + alias): Einar Stensson 2C15mwAQnCE (4:30)
https://www.youtube.com/watch?v=2C15mwAQnCE&t=17s, pip install
pandas, import pandas as pd, pip uninstall. Misses
from x import y. - Bro Code XcfxkHrHTVE (6:36): good on
import / as / from-import and the name-clash warning, but no pip. |
| ## 5. NumPy basics: np.array, shape, ndim, dtype, element-wise math, arange, zeros, reshape No single short video verified covering all. Set: |
| | Sub-point | Winner | Dur | Link | |—|—|—|—| | shape / ndim / size / dtype | Numpy Array Attributes (Aero) | 2:15 | https://www.youtube.com/watch?v=LkEgS03BTJ4&t=0s | | zeros, arange, reshape | NumPy zeros(), ones(), arange(), linspace(), reshape() (Serdar Yalkabov) | 7:34 | https://www.youtube.com/watch?v=EmLBAzvpFuw&t=42s (arange 147s, reshape 278s) | | array vs list element-wise math | numpy array (PyPros) | 2:58 | https://www.youtube.com/watch?v=tdWLrmnayBI&t=61s | |
| - Aero: shape returns a tuple (3,4), ndim, size = 12 = 3x4, dtype int64/float64. Quick and exact. - Serdar: np.zeros((2,3)), np.arange(8) ends at 7 (stop exclusive), step, reshape to (2,3) and the -1 trick, flatten. 7:34 because it also covers ones/linspace; use the timestamps. Shorter for just arange+reshape: Codigo Logo oMo4ymIJjmc (2:41), thin, no zeros. - PyPros: converts lists to arrays, miles/gallons divides element-wise (lists cannot), plus a boolean-mask filter. Does NOT literally show array2 vs list2 (list*2 repeats the list). That gotcha is not covered in any verified video. Runner-up for element-wise: Ryan Noonan L1nRBSKdDl4 (4:07). - Unverified (transcript fetch blocked): Bro Code “Start using NumPy in 5 minutes” Gc-dwloxNFE (5:08) https://www.youtube.com/watch?v=Gc-dwloxNFE . Possibly the best single-video answer here; check it first. |
| ## 6. NumPy indexing: slicing, 2D, a[:,0], [::-1], boolean mask, mean/sum/max/argmax | Sub-point | Winner | Dur | Link | |—|—|—|—| | 2D slicing, a[row,col], column select, reversing | Slicing in NumPy is easy! (Bro Code) | 11:07 | https://www.youtube.com/watch?v=6WaBryk4fkE&t=270s (negative step 273s, columns 304s, row+col combos 521s) | | boolean mask a[a > 23] | NumPy Boolean Indexing Tutorial (CodeLucky) | 3:46 | https://www.youtube.com/watch?v=A60JGQu5c-E&t=19s | | sum/mean/max/argmax | NumPy aggregate functions are easy! (Bro Code) | 4:02 | https://www.youtube.com/watch?v=8pUBr05OQfs&t=40s (argmin/argmax 131s, axis 168s) | |
| - Bro Code slicing: rows, negative index, start:stop:step (stop exclusive), step -1 reverses, the same for columns, a[0,0], quadrant picks. Long, but the only one hitting reverse + 2D + columns. Shorter, partial: Data Thinkers h3Yd1nVkflU (3:55), 2D a[row,col], negative index, block slices, no reversal. Take it if you only need the 2D-index concept. - CodeLucky mask: builds mask r>30 giving [F,F,T,T], then r[mask], then inline r[r>30], & and | with parentheses. Exactly the exam pattern. Runner-up: GormAnalysis 8wSjVFvFOis (3:42), good but moves into row/column boolean selection, harder. - Bro Code aggregates: sum 55, mean 5.5, std/var, min/max, argmin/argmax return the INDEX, axis=0 vs axis=1. Complete. Argmax-only extra: Koolac 66fJfvuMJw4 (2:28). |
| ## 7. pandas subsetting + inspection No single short video verified covering all. Set: |
| | Sub-point | Winner | Dur | Link | |—|—|—|—| | filter rows df[df[‘x’]==v], select cols df[[‘a’,‘b’]], & conditions | Boolean Index Filtering with Pandas (Non Techie) | 7:48 | https://www.youtube.com/watch?v=eeAJ-08NdRU&t=43s | | loc vs iloc | Selecting Data Pandas loc vs iloc (Greg Kamradt) | 4:43 | https://www.youtube.com/watch?v=8rvneUBXDN8&t=0s | | describe | Pandas describe (xind xrci) | 1:49 | https://www.youtube.com/watch?v=MaTAhxRsshE&t=0s | | isnull().sum() | Pandas methods (pdpy) | 9:02 total | https://www.youtube.com/watch?v=Ny5riE5s9Hc&t=186s (only the isnull().sum() segment, about 1 min) | |
- Non Techie: df[‘city’]==‘Ahmedabad’ gives a True/False Series,
df[that] filters rows, passing a LIST of columns selects columns,
combining conditions needs parentheses and &. Padded intro.
Runner-up (deeper theory): Reuven Lerner v5pJfu-yn-0 (8:23), mask index
and broadcasting; better for understanding why it works, not for speed.
- Greg Kamradt: loc = labels, iloc = integer positions (zero-based),
single cell, whole column with :, slices. Runner-up: Rice
yF6Jrzz7E5s (5:20), the real gotcha: after sorting, the index is out of
order, so loc[2] and iloc[2] return different rows. - describe:
count/mean/std/min/quartiles/max for numeric columns; use min/max to
spot outliers; differing counts hint at missing values. -
isnull().sum(): df.isnull() returns booleans, .sum() gives nulls per
column. Video is 9 min; use the timestamp. Stand-alone candidates found
but NOT verified: DATA SCIENCE LOVERS 5_YsUIjsEl4 (3:26), Statistics
Globe LvdaSXZkOR8 (6:14). - head() and info(): no verified video.
Unverified candidates: Data Science with Adel PhSTuxde7PE (5:16), Udemy
w4cZnVUTnYg (10:47). Status: head/info unresolved. |
| ## 8. What “data profiling” means Winner: Data Profiling Explained in Under 5 Minutes (Jesper Lowgren), 4:36, https://www.youtube.com/watch?v=074G1EhcDu8&t=70s - Definition (“analyzing and summarizing the characteristics of a dataset to understand its structure, quality and content”), then three types: structure (tables/keys, column data types), quality (completeness = nulls, accuracy = invalid values, consistency = duplicates), content (patterns, frequency/distribution, relationships, summary stats, anomalies), then a 6-step process. Misses: never says “outliers” by name (says anomalies), no Python. - Runner-up (shortest): Abhilash Marichi HtaYjVwW-Mo (3:43) https://www.youtube.com/watch?v=HtaYjVwW-Mo&t=0s . Defines it as understanding data characteristics and quality; column stats (min, max, mean, std dev, frequency), data types, length, uniqueness/duplicates, null counts. Leans on IBM tooling and data-warehouse context. |
| Topic | Winner URL | Duration | Covers |
|---|---|---|---|
| 1a data types | https://www.youtube.com/watch?v=CQyO60uHcZM | 7:40 | all 8 types, mutable note |
| 1b mutable/immutable | https://www.youtube.com/watch?v=kjsSaOoUFGo | 2:58 | list vs tuple error demo, type lists |
| 1c casting | https://www.youtube.com/watch?v=2WZvl1R4A6g&t=18s | 5:09 | int(“3”), “3”*3 vs 9 |
| 1d / // % | https://www.youtube.com/watch?v=0knYZsIZp_c | 1:38 | all three, remainder |
| 2a list methods | https://www.youtube.com/watch?v=nhkiHfebmhs&t=20s | 5:22 | append, extend, insert, pop, remove |
| 2b index/slice/IndexError | https://www.youtube.com/watch?v=KAXvMbD1Zac&t=22s | 6:57 | negative idx, [2:5], IndexError |
| 3 functions | i_ZEaE5vfE0, KmYcvG4FfGk&t=54s, ZnBQfF5JFDM&t=19s, 2W8Z6O7jsGE | 2:11 + 2:54 + 3:31 + 1:55 | params/args, defaults, return, sort vs sorted (no single video) |
| 4 modules | https://www.youtube.com/watch?v=o0jW15Ob4Fk&t=73s | 6:39 | module/package, pip vs import, as, from-import |
| 5 NumPy basics | LkEgS03BTJ4, EmLBAzvpFuw&t=42s, tdWLrmnayBI&t=61s | 2:15 + 7:34 + 2:58 | shape/ndim/dtype, zeros/arange/reshape, element-wise (array*2 gotcha not found; try Gc-dwloxNFE) |
| 6 NumPy indexing | 6WaBryk4fkE&t=270s, A60JGQu5c-E&t=19s, 8pUBr05OQfs&t=40s | 11:07 + 3:46 + 4:02 | 2D/column/reverse slicing, mask, sum/mean/max/argmax |
| 7 pandas | eeAJ-08NdRU&t=43s, 8rvneUBXDN8, MaTAhxRsshE, Ny5riE5s9Hc&t=186s | 7:48 + 4:43 + 1:49 + ~1:00 | filter/select cols, loc/iloc, describe, isnull().sum(); head/info unresolved |
| 8 data profiling | https://www.youtube.com/watch?v=074G1EhcDu8&t=70s | 4:36 | structure/quality/content, nulls, duplicates, distribution |