Syllabus Guide · Big Data · 2026
Uncodemy Big Data Course Syllabus Explained in Detail
Quick summary — Uncodemy Big Data syllabus
Big Data is a module within Uncodemy's Data Science program, covering Hadoop, Spark, and cloud services. This guide breaks down every day, week, and month of the Big Data portion — with checklists, goals, and tracking to keep you on course.
In this guide you will learn:
- Month 1: Hadoop & HDFS — distributed storage and processing.
- Month 2: Spark & MapReduce — in-memory processing and batch jobs.
- Month 3: Cloud & Projects — cloud services and capstone.
- Tracking templates — track your progress.
- Accountability tips — stay on track.
SECTION 01Month 1: Hadoop & HDFS (Days 1-30)
The first month is about building your Big Data foundation. Hadoop and HDFS are the bedrock of distributed data processing. Here's your weekly learning plan:
| Week | Focus | Daily Actions | Weekly Goal |
|---|---|---|---|
| Week 1 | Big Data Fundamentals | What is Big Data, 5 Vs, ecosystem overview | Understand the Big Data landscape |
| Week 2 | HDFS | NameNode, DataNode, replication, block storage | Set up a single-node HDFS |
| Week 3 | MapReduce Basics | Mapper, Reducer, shuffle and sort | Write a word count program |
| Week 4 | Hive & Pig | SQL-like queries, scripting | Query a dataset with Hive |
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: Big Data fundamentals
- Week 2: HDFS
- Week 3: MapReduce basics
- Week 4: Hive and Pig
12:00 PM — Lunch break
1:00 PM — Hands-on labs (Hadoop setup, HDFS commands)
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 the 5 Vs of Big Data?
□ Do I know the Hadoop ecosystem?
□ Can I explain why distributed processing matters?
Week 2 Review:
□ Do I understand NameNode and DataNode?
□ Can I set up a single-node HDFS?
□ Can I run basic HDFS commands?
Week 3 Review:
□ Do I understand Mapper and Reducer?
□ Can I write a word count program?
□ Do I understand shuffle and sort?
Week 4 Review:
□ Can I write Hive queries?
□ Do I understand Pig scripting?
□ Have I queried a dataset?
SECTION 02Month 2: Spark & MapReduce (Days 31-60)
The second month is about Spark — the modern in-memory processing engine that replaced much of MapReduce. Here's your weekly learning plan:
| Week | Focus | Daily Actions | Weekly Goal |
|---|---|---|---|
| Week 5 | Spark Basics | RDDs, DataFrames, lazy evaluation | Run Spark locally |
| Week 6 | Spark SQL | DataFrames, SQL queries, schema inference | Query a dataset with Spark SQL |
| Week 7 | Spark Streaming | DStreams, structured streaming | Build a simple streaming job |
| Week 8 | Build Project 1 | End-to-end Spark pipeline | 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: Spark basics
- Week 6: Spark SQL
- Week 7: Spark Streaming
- Week 8: Project 1
12:00 PM — Lunch break
1:00 PM — Hands-on labs (Spark jobs, SQL queries)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read Spark docs (30 min)
10:00 PM — Wind down
Week 5 Review:
□ Do I understand RDDs?
□ Can I use DataFrames?
□ Have I run Spark locally?
Week 6 Review:
□ Can I query with Spark SQL?
□ Do I understand schema inference?
□ Have I queried a dataset?
Week 7 Review:
□ Do I understand DStreams?
□ Can I build a streaming job?
□ Have I processed live data?
Week 8 Review:
□ Is Project 1 complete?
□ Is it documented?
□ Is it on GitHub?
SECTION 03Month 3: Cloud & Projects (Days 61-90)
The third month is about cloud services and completing your capstone project. Here's your weekly learning plan:
| Week | Focus | Daily Actions | Weekly Goal |
|---|---|---|---|
| Week 9 | Cloud Big Data Services | AWS EMR, S3, Glue, Azure HDInsight | Run a job on cloud |
| Week 10 | Build Project 2 | End-to-end cloud Big Data pipeline | Complete Project 2 |
| Week 11 | Interview Prep | Mock interviews, Hadoop/Spark concepts | 10 mock interviews |
| Week 12 | Target & Apply | 5 applications daily, research companies | 25 quality applications |
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: Cloud Big Data services
- Week 10: Project 2
- Week 11: Interview prep
- Week 12: Applications and research
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:
□ Can I use AWS EMR?
□ Do I understand S3 and Glue?
□ Have I run a job on cloud?
Week 10 Review:
□ Is Project 2 complete?
□ Does it use cloud services?
□ Is it on GitHub?
Week 11 Review:
□ Have I practiced Hadoop/Spark concepts?
□ Have I done mock interviews?
□ Can I explain my projects?
Week 12 Review:
□ Have I sent 25 quality applications?
□ Have I researched target companies?
□ Have I followed up?
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)
☐ Hands-on lab 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:
_______________________________________________
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 Big Data learners.
- Celebrate milestones — reward yourself at 30, 60, and 90 days.
- Review weekly — every Sunday, review your progress and plan the week ahead.
SECTION 06Interview Q&A — Uncodemy Big Data Syllabus
Q1Does Uncodemy offer a standalone Big Data course?
Based on the available information, Big Data is covered as a module within Uncodemy's PG Program in Data Science, not as a standalone course [citation:1]. The program covers "Big Data and Cloud Technologies" including Hadoop, Spark, and cloud services.
Q2What does the Big Data module cover?
The Big Data module covers Hadoop, Spark, and cloud services for large-scale data management [citation:1]. This includes distributed storage (HDFS), distributed processing (MapReduce, Spark), and querying tools (Hive, Spark SQL).
Q3How long is the full PG Program?
The PG Program in Data Science typically spans 6 to 12 months, depending on the mode of study (full-time or part-time) and your learning pace [citation:1]. The Data Science course in Kolkata runs 8–9 months with 150+ hours [citation:6].
Q4What is the fee for the Data Science program?
Fees vary by location and program. The Data Science course in Kolkata is listed at ₹21,500 + 18% GST = ₹25,370 total [citation:6]. A third-party listing mentions ₹50,000 for a 5–6 month program in Bhubaneswar [citation:8]. Confirm the exact fee for your chosen program during counselling.
Q5Is Big Data still relevant in 2026?
Yes. While the term "Big Data" has faded from hype cycles, the underlying skills — distributed processing, Spark, cloud data services — are more relevant than ever. Modern data engineering roles require these skills for handling large-scale datasets [citation:1].
SECTION 07Test yourself — Uncodemy Big Data Syllabus quiz
Five questions. No sign-up.
0 / 5Pick an answer to see why it is right or wrong.
SECTION 08Frequently asked questions
Is Big Data a standalone course at Uncodemy?
Based on available information, Big Data is a module within the PG Program in Data Science, not a standalone course [citation:1].
What tools are covered in the Big Data module?
Hadoop, Spark, and cloud services for large-scale data management [citation:1]. This includes HDFS, MapReduce, Hive, Spark SQL, and cloud platforms.
How long does the PG Program take?
6 to 12 months, depending on full-time or part-time study [citation:1].
What is the fee?
Kolkata Data Science course: ₹21,500 + GST = ₹25,370 [citation:6]. Other locations may vary. Confirm during counselling.
Is Spark covered?
Yes. Spark is listed as a core Big Data tool in the program [citation:1].
SECTION 09Related reads
Classroom & online · Noida
Explore the Uncodemy PG Program in Data Science
Our PG Program in Data Science covers Big Data, Hadoop, Spark, cloud, and more — with projects and placement support.
6–12 months · 150+ hours- Big Data & Cloud Technologies
- Hadoop, Spark, and more
- Live projects
- Placement assistance