Excel vs PyTorch: Which to Learn First?

Data Analytics · Excel vs PyTorch

Excel vs PyTorch: Which to Learn First?

AK Ankit Kaushik · Lead Trainer, Data Engineering & AI | Updated 7 November 2026 | 15 min read Free · Career Guide

For most beginners, the path runs Excel, then SQL, then Python, and finally PyTorch for deep learning.

Quick summary — Excel vs PyTorch

In Excel vs PyTorch, most beginners should learn Excel first. Excel builds data literacy — formulas, pivot tables and charts — and is used in almost every office job. PyTorch is a Python library for building deep learning models and needs Python, NumPy and maths first. Learn Excel early; learn PyTorch when your goal is machine learning.

In this guide you will learn:

What each tool is — spreadsheet vs deep learning library.
Who needs Excel — the skills and roles it supports.
Who needs PyTorch — prerequisites and use cases.
Path templates — learning orders for different goals.
Tips — avoiding the most common beginner mistake.

SECTION 01Two very different tools

Comparing Excel and PyTorch is like comparing a calculator with a car engine factory. Excel helps people organise and analyse data directly. PyTorch helps programmers build and train neural networks. The right first choice depends entirely on where you want to go.

Point	Excel	PyTorch	Takeaway
Type	Spreadsheet application	Python deep learning library	Different categories
Users	Almost every office role	ML engineers and researchers	Excel has wider reach
Prerequisites	Basic computer skills	Python, NumPy, linear algebra	PyTorch needs more groundwork
Typical output	Reports, summaries, charts	Trained neural network models	Different results entirely

Basic Excel skills to learn first:
=SUM(B2:B100)                     total sales
=AVERAGE(B2:B100)                 average sale
=COUNTIF(C2:C100,"North")         orders from North
=SUMIFS(B:B,C:C,"North",D:D,"Q1") North sales in Q1
=XLOOKUP(F2,A:A,B:B,"not found")  look up a value
Then: PivotTables, charts, conditional formatting

import torch

x = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
w = torch.tensor([[0.5], [0.25]], requires_grad=True)
y = x @ w                   # matrix multiply
loss = y.sum()
loss.backward()             # compute gradients
print(y)
print(w.grad)               # gradient of loss w.r.t. w

starter-skills.txt

Key insight: If the PyTorch code above feels confusing, that is normal — it assumes Python and matrix basics. Excel assumes almost nothing.

SECTION 02Who should learn Excel first (most beginners)

Excel is the fastest way to build data literacy: understanding tables, totals, averages, filters and charts. These ideas carry over to SQL, Python and machine learning later. Excel skills are also requested across many entry-level roles.

Goal	Why Excel first	Key Excel skills	Next tool
Data analyst	Daily reporting and quick analysis	Pivots, XLOOKUP, charts	SQL, then Power BI
Business or MIS role	Most reports live in spreadsheets	SUMIFS, data validation	Power Query
Future ML engineer	Learn to think about data clearly	Sorting, filtering, summaries	Python and pandas
Any office job	Universal workplace skill	Formatting, formulas, charts	Depends on role

PivotTable practice with sales data
1. Select data > Insert > PivotTable
2. Rows: Region   Columns: Quarter
3. Values: Sum of Sales
4. Add a slicer for Product
5. Insert > PivotChart > Clustered column
Question to answer: Which region grew fastest?

import pandas as pd

df = pd.read_excel("sales.xlsx")
pivot = pd.pivot_table(df, index="Region", columns="Quarter",
                       values="Sales", aggfunc="sum")
print(pivot)
# Excel pivots teach the idea; pandas automates it later

pivot-practice.txt

Tip: Learn PivotTables early. The same 'group and summarise' idea appears in SQL GROUP BY, pandas and even model evaluation.

SECTION 03Who should learn PyTorch (and when)

PyTorch is the right tool when your goal is deep learning — image recognition, language models, speech or research. It is widely used in research and industry. But it rewards preparation: learners who start it without Python and maths usually struggle.

Prerequisite	Why needed	How to check readiness	Typical time to build
Python	PyTorch code is Python	Write functions and classes comfortably	A few months of practice
NumPy	Tensors behave like arrays	Use broadcasting and slicing	A few weeks
Linear algebra	Models are matrix operations	Multiply a matrix by a vector	A few weeks of revision
ML basics	Training, validation, overfitting	Train a scikit-learn model	A few weeks to months

import torch
from torch import nn

# Learn y = 3x + 2 from data
x = torch.linspace(-1, 1, 100).unsqueeze(1)
y = 3 * x + 2 + 0.1 * torch.randn_like(x)

model = nn.Linear(1, 1)
opt = torch.optim.SGD(model.parameters(), lr=0.1)
loss_fn = nn.MSELoss()

for epoch in range(200):
    opt.zero_grad()
    loss = loss_fn(model(x), y)
    loss.backward()
    opt.step()

print(model.weight.item(), model.bias.item())  # close to 3 and 2

Ready for PyTorch if you can:
[ ] Write a Python class with methods
[ ] Use NumPy arrays without loops
[ ] Explain train / validation / test split
[ ] Explain what a gradient is (in simple words)
[ ] Train a scikit-learn model and read its metrics

train_linear.py

Key insight: Time estimates vary widely by person. Use readiness checks, not the calendar, to decide when to start PyTorch.

SECTION 04Learning path templates by goal

Pick the template that matches your goal. Excel appears early in all of them.

Goal: Data analyst job
1. Excel - formulas, pivots, charts
2. SQL - SELECT, JOIN, GROUP BY
3. Power BI or Tableau - dashboards
4. Python + pandas - automation and analysis
5. Portfolio: 3 dashboards + 1 Python analysis
PyTorch: optional, later

Goal: Machine learning / deep learning engineer
1. Excel basics (short) - data literacy
2. Python - core language
3. NumPy, pandas, matplotlib
4. Maths - linear algebra, probability, calculus basics
5. scikit-learn - classic ML
6. PyTorch - tensors, training loops, CNNs
7. Portfolio: 2 ML + 2 DL projects

Goal: Just exploring data and AI
1. Excel - analyse something you care about
2. A short Python course
3. Try a no-install notebook with a simple model
4. Decide: analytics path or ML path

excel-pytorch-paths.md

SECTION 05Tips for beginners choosing between them

Avoid the classic beginner trap — starting at the hardest end:

Start where you can win quickly — early progress in Excel builds confidence for harder tools.
Do not skip Python — PyTorch without Python basics leads to copy-paste learning.
Use real data you care about — cricket scores, expenses or prices make practice enjoyable.
Learn concepts, not only clicks — understand why a pivot or a gradient works.
Revisit your goal often — your path can change as you discover what you enjoy.

Pro tip: Spend your first month in Excel on a dataset you love. If you then want more power, Python is the natural next step — and PyTorch comes after that.

SECTION 06Interview Q&A — Excel vs PyTorch

Q1What is PyTorch?

PyTorch is an open-source Python library for building and training neural networks, with tensors, automatic differentiation and GPU support.

Q2What is a tensor?

A tensor is a multi-dimensional array of numbers, like a NumPy array, which PyTorch can move to a GPU and track for gradients.

Q3What is a PivotTable?

A PivotTable summarises data by grouping rows and columns and calculating values like sums or counts, without writing formulas.

Q4Why is Excel still important for data roles?

Excel is widely used for quick analysis, sharing and reporting, and it builds data intuition that transfers to SQL and Python.

Q5What does loss.backward() do?

It computes gradients of the loss with respect to model parameters, which the optimiser uses to update them.

SECTION 07Test yourself — Excel vs PyTorch quiz

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 08Frequently asked questions

Should I learn Excel or Python first?

For most beginners, Excel first for data basics, then Python. If you already code, you can move to Python and pandas sooner.

Is PyTorch good for beginners?

PyTorch is beginner-friendly for people who already know Python and ML basics. Complete beginners should build those foundations first.

Is Excel enough to get a data job?

Excel alone can help with some MIS and reporting roles, but most analyst roles also expect SQL and a BI tool such as Power BI. Excel is the right start, not the finish.

Do data analysts need PyTorch?

Usually not. Analysts mainly use Excel, SQL, BI tools and Python. PyTorch is for deep learning work.

PyTorch or TensorFlow: which should I learn later?

Both are widely used. Choose one, learn it well, and the concepts will transfer if you need the other.

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Excel vs PyTorch Which Should Beginners Learn First?