NumPy arrays in 1D, 2D and 3D
Why NumPy exists, creating arrays directly and with methods, random arrays and data types, and how arrays differ from Python lists.
Free learning initiative
A free Data Visualization with Python crash course, starting with the NumPy and Pandas every chart is built on
Every Python chart starts as an array or a DataFrame. Jobaaj Learnings is making this crash course free for every student — it covers that groundwork: NumPy arrays, broadcasting, Pandas Series and DataFrames, the data a chart is drawn from.
No payment step. Open the first lesson straight away.
import numpy as np, pandas as pd
df = pd.DataFrame({"units": [12, 30, 7], "price": [250, 90, 400]})
df["revenue"] = df.units * df.price
df[df.revenue > 2800]
df.revenue.sum()
Rows
3
one DataFrame
New column
revenue
vectorised
Total
8,500
df.revenue.sum()
df.shape(3, 3)
Boolean filterrevenue > 2800
Rows kept2
Loop writtennone
Illustrative figures, not a recommendation
Our learners now work at
What you'll learn
Get data ready the way analysts actually do in Python
Before any chart there is data to hold, slice, filter and reshape. This course covers that part — NumPy for fast arrays and Pandas for labelled data — so a plot later is one line, not a mess of loops.
Indexing, slicing and views
Pick out values with indexes, slices and boolean masks — and learn why a slice is a view, and when you need a copy.
Vectorised maths and broadcasting
Basic operations, the dot product, comparisons and logical operators on whole arrays, then broadcasting and turning 1D data into 2D.
Reshape, flatten, shuffle and sort
Change an array’s shape without touching its data, flatten it, shuffle it and sort it — then test yourself with the NumPy quiz.
Pandas Series
Create Series four ways, read their attributes, use built-in functions, sort in place, customise the index, and use get(), value_counts(), apply() and map().
DataFrames
Create DataFrames, work with the index and columns, understand axis=0 and axis=1, do maths across columns and add new ones.
Why it's free
We built this to be learned from, not sold.
A lot of people who need a skill like this are exactly the people least able to pay for it — students between semesters, freshers before a first job, anyone rebuilding after a break. So we took the fee off.
01
The whole course, not a preview
Every lesson is open from the start. There's no locked second half and no upgrade waiting three modules in.
02
Free because it should be, not for a window
This isn't a promotional price or a trial. There's no clock on it and no fee arriving later.
03
Built to the same standard as the rest
Nothing was trimmed to make it free. It is the same material, at the same depth, as everything else we teach.
04
Yours to keep
Come back to a lesson next month or next year. Access doesn't expire and nothing gets pulled behind a paywall.
Where it takes you
The Python toolkit every data role uses
NumPy and Pandas are the first two imports in almost every analytics, data science and machine learning notebook.
Students and freshers
The most common reason people take this — Pandas questions are a staple of data analyst technical rounds.
Data Analyst
Clean, filter and summarise datasets in Python before they are charted or reported.
Junior Data Scientist
Prepare the features every model is trained on — all of it in NumPy and Pandas.
Business Analyst
Answer questions from large files that are too big for a spreadsheet.
ML / AI Engineer (entry)
NumPy arrays are the format every machine-learning library expects.
Research / Quant Analyst
Work with numeric data at scale with vectorised maths.
AccentureDeloitteEYKPMGMu SigmaFractalTiger AnalyticsLatentViewTredenceZS Associates
Salary bands are indicative market ranges for these roles in India, not an outcome promised by this course.
Sixty-second check
What’s your NumPy & Pandas level, really?
Five questions on the things that actually trip people up. Answer them and you'll get a read on where you stand — and which part of the course to start from.
Certificate
Finish the course, get certified
Submit your assignment and Jobaaj Learnings issues a verifiable certificate you can share anywhere.
Official and verifiable
Issued and signed by Jobaaj Learnings, with a record an employer can check.
Easy to share
Add it to your résumé or post it to LinkedIn the day it's issued.
Evidence, not decoration
It says you finished the work and submitted the assignment — which is what it's worth showing for.
Learner journeys
Where people took it from here
Learners who moved into data analyst and developer roles after upskilling with Jobaaj Learnings.
H
Hanshika
Data Analyst, DRRT
Came from a science background with no data experience and moved into a data analyst role.
A
Arihant
Data Analyst, Codiant
Moved from business analyst intern to data analyst at Codiant.
P
Pramod
Data Analyst, EY
Went from fresher to data analyst at EY.
S
Sufiyan
Data Analyst, Invergence Analytics
Went from fresher to data analyst at Invergence Analytics.
K
Khushi
Data Analyst, Virtual Bytes
Restarted her career after a five-year break and moved into a data analyst role.
K
Karuna
SQL Developer, Capgemini
Moved from technical support engineer to SQL developer at Capgemini.
Keep going
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ExploreFAQ
Questions people ask before starting
Yes. Jobaaj Learnings provides it free of cost. There’s no fee to start, no trial period that ends, and no payment step anywhere in the flow.
None. The course starts from the very beginning and builds up from there, so you can begin with no prior experience in the subject.
Yes. Every lesson is recorded, so you can pause, rewind and come back whenever it fits around classes or work. There’s no schedule to keep up with.
No — and it’s worth knowing before you start. This course covers the groundwork every Python chart is built on: NumPy arrays and Pandas Series and DataFrames. Plotting libraries such as Matplotlib and Seaborn are not in these lessons.
You should be comfortable with variables, loops and lists. If you are not there yet, take the Python core concepts crash course first — it is also free and ends where this one begins.
There is a quiz after the NumPy, Series and Pandas sections, plus an assignment lesson in the NumPy section. Everything runs in a free notebook, so you can type along with each video.
Create and reshape NumPy arrays, select data with indexing, slicing and boolean masks, use vectorised maths and broadcasting instead of loops, and build, filter and transform Pandas Series and DataFrames — the prepared data any chart is drawn from.
Write to training@jobaaj.com and we’ll get back to you.
Free · Jobaaj Learnings
Ready to work with real data in Python?
This crash course is completely free. Open the first lesson and create your first NumPy array.
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