Is Uncodemy Good for Learning Data Science in 2026?

Course Review · Data Science · 2026

Is Uncodemy Good for Learning Data Science in 2026?

AK Ankit Kaushik · Lead Trainer, Data Engineering & AI | Updated 11 November 2026 | 18 min read Free · Career Guide

A 90-day learning path inside the Uncodemy Data Science course — Month 1: Python & SQL, Month 2: ML & Gen AI, Month 3: Projects & Apply.

Quick summary — is Uncodemy good for data science in 2026?

Yes, for disciplined learners who want a structured, affordable path into data science with live mentorship and placement support. This guide breaks down every day, week, and month of the course — with checklists, goals, and tracking to help you decide.

In this guide you will learn:

Month 1: Python & SQL — daily and weekly learning plan.
Month 2: ML & Gen AI — daily and weekly learning plan.
Month 3: Projects & Apply — daily and weekly learning plan.
Tracking templates — track your progress.
Accountability tips — stay on track.

SECTION 01Month 1: Python & SQL (Days 1-30)

The first month is about building your programming and data foundation. Here's your weekly learning plan:

Week	Focus	Daily Actions	Weekly Goal
Week 1	Python Basics	Syntax, loops, functions, data types	Write 10 Python scripts
Week 2	Pandas & NumPy	DataFrames, arrays, data cleaning	Clean a real dataset
Week 3	SQL Fundamentals	SELECT, JOIN, GROUP BY, subqueries	Write 20 SQL queries
Week 4	Statistics Basics	Mean, median, variance, distributions	Analyze a dataset with stats

Month 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 1: Python basics
   - Week 2: Pandas and NumPy
   - Week 3: SQL queries
   - Week 4: Statistics basics
12:00 PM — Lunch break
1:00 PM — Practice exercises (Kaggle, real datasets)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Do I understand Python syntax?
□ Can I write loops and functions?
□ Have I written 10 scripts?

Week 2 Review:
□ Can I load data with Pandas?
□ Can I clean a dataset?
□ Have I used NumPy?

Week 3 Review:
□ Can I write SELECT, JOIN, GROUP BY?
□ Can I write subqueries?
□ Have I written 20 SQL queries?

Week 4 Review:
□ Do I understand mean, median, variance?
□ Can I interpret a distribution?
□ Have I analyzed a real dataset?

month1-planner.md

Key insight: Month 1 is about building your foundation. Uncodemy's Data Science course runs 8–9 months with 150+ hours of learning across 90+ live sessions, covering 15+ tools including Python, SQL, Pandas, and NumPy [citation:8]. A student from a non-technical background noted that the training "helped me understand Python, machine learning, and data visualization easily" [citation:15].

SECTION 02Month 2: ML & Gen AI (Days 31-60)

The second month is about machine learning and the Gen AI module. Here's your weekly learning plan:

Week	Focus	Daily Actions	Weekly Goal
Week 5	Supervised Learning	Regression, classification, evaluation	Train a model
Week 6	Unsupervised Learning	Clustering, dimensionality reduction	Cluster a dataset
Week 7	Gen AI Module	ChatGPT, Copilot, LLM-assisted workflows	Build an AI-assisted project
Week 8	Build Project 1	End-to-end ML project with documentation	Complete Project 1

Month 2 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 5: Supervised learning
   - Week 6: Unsupervised learning
   - Week 7: Gen AI module
   - Week 8: Project 1
12:00 PM — Lunch break
1:00 PM — Practice (Kaggle, build models)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read documentation (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Do I understand regression?
□ Can I train a classifier?
□ Can I evaluate with accuracy/ROC-AUC?

Week 6 Review:
□ Do I understand clustering?
□ Can I use PCA?
□ Have I clustered a dataset?

Week 7 Review:
□ Do I understand ChatGPT/Copilot?
□ Can I use LLM-assisted workflows?
□ Have I built an AI-assisted project?

Week 8 Review:
□ Is Project 1 complete?
□ Is it documented?
□ Is it on GitHub?

month2-planner.md

Key insight: Month 2 is about building evidence. A Trustindex review noted: "The curriculum covered all the essential Machine Learning topics, from data preprocessing to model evaluation. The practical approach made the concepts much easier to grasp" [citation:1]. The Gen AI module is a newer addition [citation:8].

SECTION 03Month 3: Projects & Apply (Days 61-90)

The third month is about polishing your portfolio, preparing for interviews, and applying strategically. Here's your weekly learning plan:

Week	Focus	Daily Actions	Weekly Goal
Week 9	Build Project 2	Capstone project with real dataset	Complete Project 2
Week 10	Interview Prep	Mock interviews, case studies, SQL	10 mock interviews
Week 11	Target & Apply	5 applications daily, research companies	25 quality applications
Week 12	Evaluate & Decide	Evaluate offers, negotiate	Accept a role

Month 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 9: Project 2
   - Week 10: Interview prep
   - Week 11: Applications and research
   - Week 12: Evaluate offers
12:00 PM — Lunch break
1:00 PM — Networking and follow-ups
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Interview prep (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Is Project 2 complete?
□ Does it use a real dataset?
□ Is it on GitHub?

Week 10 Review:
□ Have I practiced SQL under time pressure?
□ Have I done mock interviews?
□ Can I explain my projects clearly?

Week 11 Review:
□ Have I sent 25 quality applications?
□ Have I researched target companies?
□ Have I followed up?

Week 12 Review:
□ Have I evaluated offers?
□ Have I negotiated?
□ Have I accepted a role?

month3-planner.md

Key insight: Month 3 is about execution. Uncodemy publishes detailed placement stories. Manjeet Pathak landed a Data Scientist role at Scry Analytics (4.8 LPA) after 55 days of searching [citation:2]. Rahul Gupta landed at Amgen Inc (3.6 LPA) [citation:5]. Aarav Sharma landed at Expedia (3.6 LPA) [citation:7]. Sagar Thakur landed at Fractal (4.8 LPA) [citation:11].

SECTION 04Tracking templates

Use these templates to track your 90-day progress:

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Deep work session 1 (3 hours)
☐ Deep work session 2 (2 hours)
☐ Practice exercises completed
☐ Project work completed
☐ Exercise completed
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3.

Weekly Tracker:
Week: _____ (Month 1 / 2 / 3)

Goals for this week:
1. 
2. 
3. 

Daily Progress:
Mon: ☐☐☐
Tue: ☐☐☐
Wed: ☐☐☐
Thu: ☐☐☐
Fri: ☐☐☐
Sat: ☐☐☐
Sun: ☐☐☐

Weekly Wins:
1. 
2. 
3. 

Challenges & Solutions:
1. 
2. 

Next Week's Goals:
1. 
2. 
3.

Monthly Tracker:
Month: _____ (1 / 2 / 3)

Monthly Goal:
_______________________________________________

Week 1 Progress: ☐☐☐
Week 2 Progress: ☐☐☐
Week 3 Progress: ☐☐☐
Week 4 Progress: ☐☐☐

Monthly Wins:
1. 
2. 
3. 

Skills Gained:
1. 
2. 
3. 

Portfolio Items:
1. 
2. 
3. 

Applications Sent: _____
Interviews: _____
Offers: _____

Next Month's Goal:
_______________________________________________

tracking-templates.md

SECTION 05Accountability tips

Here are tips to stay accountable during your 90-day learning journey:

Find an accountability partner — share your weekly goals with someone who will check in.
Track daily wins — write down 3 wins each day, no matter how small.
Join a community — connect with other data science learners.
Celebrate milestones — reward yourself at 30, 60, and 90 days.
Review weekly — every Sunday, review your progress and plan the week ahead.

Pro tip: Consistency beats intensity. A student review noted: "Getting a job still depends largely on personal practice and consistency" [citation:1].

SECTION 06Interview Q&A — Uncodemy Data Science

Q1What is the duration of Uncodemy's Data Science course?

The program runs 8–9 months with 150+ hours of learning across 90+ live sessions. It covers 15+ tools and includes a dedicated Gen AI module [citation:8].

Q2What do students say about the course?

Reviews on Trustindex (5.0 rating from 547 reviews) praise the practical approach, mentor support, and placement guidance [citation:1]. A Sulekha reviewer from a non-technical background said the training "helped me understand Python, machine learning, and data visualization easily" [citation:15].

Q3What are the placement outcomes?

Uncodemy publishes detailed placement stories: Manjeet Pathak at Scry Analytics (4.8 LPA) [citation:2], Rahul Gupta at Amgen (3.6 LPA) [citation:5], Aarav Sharma at Expedia (3.6 LPA) [citation:7], and Sagar Thakur at Fractal (4.8 LPA) [citation:11].

Q4Are there any concerns about Uncodemy?

Employee reviews on AmbitionBox are mixed (3.7–4.1/5), with some reviewers alleging "fake commitments to students" and "worst management" [citation:9][citation:12]. These are employee opinions, not student reviews, but they are worth noting. Trustpilot's transparency page shows only 3 organic reviews in the last 12 months .

Q5Is the Uncodemy Data Science course worth the money?

For disciplined learners who complete the projects and actively use the placement support, yes. The course is structured, covers relevant tools, and includes Gen AI. But it is not a guarantee — your effort matters most. As one reviewer put it: "getting a job still depends largely on personal practice and consistency" [citation:1].

SECTION 07Test yourself — Uncodemy Data Science quiz

Five questions. No sign-up.

0 / 5

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

SECTION 08Frequently asked questions

What is the duration of Uncodemy's Data Science course?

8–9 months with 150+ hours of learning across 90+ live sessions. The course covers 15+ tools and includes a Gen AI module [citation:8].

What tools are covered?

Python, SQL, Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Power BI, Tableau, Matplotlib, Seaborn, Jupyter, Google Colab, ChatGPT, Copilot, and MLflow [citation:8].

Does the course include placement support?

Yes — resume guidance, mock interviews, and job referrals are included. The placement stories show students landing roles at Scry Analytics, Amgen, Expedia, and Fractal [citation:2][citation:5][citation:7]. However, outcomes depend on personal effort.

Are there any negative reviews?

Employee reviews on AmbitionBox are mixed, with some alleging "fake commitments to students" [citation:9][citation:12]. These are employee opinions, not student reviews. Trustpilot shows only 3 organic reviews in the last 12 months .

Who is this course best for?

Beginners, fresh graduates, and career switchers who want a structured, affordable path into data science with live mentorship and hands-on projects. No prior coding background is required — Python and SQL are taught from scratch [citation:8].

What I would do in the first 30 daysLayoff · 14 min Why sending 500 resumes won't workJob Search · 14 min Rebuild your resume for the AI job marketResume · 15 min Which skills to learn before applyingSkills · 14 min 90-day career recovery roadmapRecovery · 16 min How AI can help you prepare for interviewsAI · 11 min

Classroom & online · Noida / Delhi NCR

Explore the Uncodemy Data Science course

Our Data Science with Gen AI covers Python, SQL, Machine Learning, Gen AI, and more — with live mentorship, projects, and placement support.

8–9 months · 150+ hours · 15+ tools

Explore the course Download the syllabus

150+ hours, 90+ live sessions
15+ tools & Gen AI module
5 guided projects + capstone
Placement assistance

Is Uncodemy Good for Learning Data Science in 2026?