223.3K
ECComment roadmap to get sent my free and complete data engineering roadmap!
@eczachly
#Data Engineering Roadmap
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27Reels em Alta
(12)
138.2K
VEThe Only Data Engineering Roadmap you will ever need
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#technology #trending #jobsearch #parttime #techconsulting #tech #hacks #behavioral #nodaysoff #veeconsistent #linkedin #emails #dataengineering
@vee_daily19
1.2M
THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
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Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
@the.datascience.gal
300.5K
MUYou DO NOT need to learn everything to become a Data Engineer.
People often prepare for mid-level roles while applying for entry-level roles.
Here’s what actually mattered for me in the beginning when switching from testing to a data engineer role.
1. SQL(non-negotiable): You’ll need to know the basics and complexities of sql along including subqueries and window functions.
If you’re not strong in SQL, you won’t be able to move forward in interviews.
2. Python concepts basics like lists, dictionaries, sets and basic problem solving.
You can solve questions in other languages too but I’d suggest Python as it’s easy to learn.
You don’t need hardcore DSA for most entry-level Data Engineering roles, but DSA is definitely important.
3. Data warehousing concepts like facts vs dimension, star vs snowflake schema, SCD Type 1,2 etc.
Understanding concepts and what data warehousing is and why it’s there mattered more than tools.
4. ETL and data pipeline understanding. How data is extracted, transformed, loaded is the CORE of Data Engineering.
You don’t need spark understanding in the beginning, just the understanding of how data flows in and out.
5. System design basics, not like design twitter/uber. Simple understanding of how data moves end to end and overall understanding of data eco-systems.
No deep design is expected at entry-level.
6. Pick any one cloud. Don’t chase all clouds, just any one cloud and cover its basics because you’d most likely be working on some cloud in your work.
I moved from Testing to Data Engineering by focusing on these basics, instead of trying to learn every other tool out there, and it is still the very core of Data Engineering which one must know to crack interviews.
Save this if you’re planning to make a switch into Data Engineering.
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[data engineering roadmap, entry level data engineer preparation, switching to data engineering, testing to data engineering, data engineer interview preparation, sql for data engineering, python basics for data engineer, data engineers for beginners, microsoft data engineer]
#dataengineer #dataengineering
@muskan.khannaa
2.9K
AI2026 Data Engineer Roadmap 🚀 (0 → Job Ready)
Want to become a Data Engineer?
Start with Python & advanced SQL → learn databases & data modeling → master ETL pipelines → work with big data tools like Spark & Kafka → deploy on cloud platforms.
This roadmap covers:
Python • SQL • ETL • Airflow • dbt • Spark • Kafka • AWS/GCP • Data Warehousing • Real-time pipelines.
Perfect for students, developers, and anyone entering data engineering.
Save this reel & start building data pipelines today 📊🔥
#DataEngineer #DataEngineering #BigData #ETL #AIwithPJ
@aiwithpj
106.5K
CHHere is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf.
#datascience #machinelearning #coding #ai #university
@chrispathway
13.8K
RAData Scientist Roadmap
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#reels #viral #trendingreels #newcollection
#viralvideos #reelsvideo #reelsinstagram #shorts #trending #viralreels
@random_code_83
9.5K
AMWant to become a Data Engineer?
This is the complete Data Engineer roadmap you need.
Comment “Data” to get Detailed Guide 📩
Follow @amanrahangdale_2108 for more Roadmaps, Coding, AI, and Career Tips every Day
@amanrahangdale_2108
484.1K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
14.5K
ASComment “AI” and I’ll send you my roadmap for Data Scientists to upskill in AI Engineering 🧠
AI is quickly becoming a non-negotiable part of any Data Scientists job. Whether it be using AI tools to be more efficient, building AI workflows, or even fine-tuning LLM models.
The great news is that Data Scientists are perfectly position to upskill into AI engineering because we already have the foundations of statistics and machine learning.
The future is coming. It’s coming fast.
But you got this. You can evolve with the times 💪🏽
#datascience #aiengineering
@askdatadawn
171.4K
HU🚀 Day 1: Noob to Pro Data Engineer 🚀
Started my journey today! 🔥 Learned about Apache Spark and how it helps solve the 3V problem (Volume, Velocity, Variety). Also compared Hadoop vs. Spark—turns out Spark is way faster! ⚡
💡 Key Takeaways:
✅ Spark processes data in-memory, making it much faster than Hadoop.
✅ Hadoop is great for batch processing, but Spark shines in real-time analytics.
✅ Practiced SQL on LeetCode & started working on my Azure Data Engineering project.
[Azure, cloud, learn, study, hardwork, consistency, hustle, motivation, job, employment, Microsoft azure, hadoop, dpark, daily vlog, daily study, unemployment, mnc, jio, corporate]
@hustleuphoney
217.1K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude!
Here are the 3 types of projects you need:
↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables
↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one.
↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it
Save this video for later + send to a data friend!
@jessramosdata
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