#Data Management Techniques

Another powerful session with the cohort. Data Management is not theory. It is not “them say.” It is structured, strategic, and executed based on real organisational realities. In this clip, I was walking my mentees through what the first 30 days of a Data Governance implementation should look like for a banking client project they’re working on. Not guesswork. Not recycled slides. But a roadmap built from verifiable, hands-on experience spanning over two decades delivering governance frameworks in regulated environments. Because knowing definitions is easy. Designing and executing in a live financial services environment is different. This is how we build practical capability — not course completion certificates. #DataGovernance #DataManagement #BankingTransformation #DataLeadership #InformationGovernance

This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist

The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore

A data warehouse is a single source of truth that helps business functions perform their data analysis operations easier. Here's what a simple data warehouse looks like: 1. Data sources 2. Bronze layer 3. Silver layer 4. Gold layer 5. Analytics There's so much more that goes into a data warehouse (e.g. ingestion frequency, data governance policies, data validation checks etc), but this is a high level design you can start with. Different companies may configure the stages in different ways according to their users' unique requirements, but the generic workflow applies to all! #dataanalytics #dataengineering #datascience #techtok #dejavu

Comment “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!

Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai

watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance 2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams 3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine 4. The core skills are communicating your insights clearly and understanding business metrics Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!

Comment „Sheets“ to get it, your data analyst is just a WhatsApp message away. Dealing with data in a spreadsheet can be a hassle, especially when you’re on the go and need an instant answer. This automation changes all of that by turning your Google Sheet into an on-demand analysis tool that lives right in your pocket. This is a personal data analyst you can talk to. Here’s how it works. You send a quick, natural language question to a WhatsApp number—for example, „What were our sales for June?“ An AI agent, powered by n8n’s no-code workflow, connects directly to your Google Sheet. It analyzes the data, finds the exact insight you asked for, and sends you a clear, instant response. No more opening spreadsheets, searching for the right column, or building complex formulas. Just effortless, on-demand insights at your fingertips. Imagine you’re in a client meeting and need a specific metric, or you’re a team lead wanting a quick summary of a project’s status. With this agent, the answer is just a text message away. What kind of insights would you want to get from your data? #n8n #aiautomation

Quick revision one-liner notes for UGC NET Paper 2 – Management. This short covers Research Methods in Management and Quantitative Techniques & Decision Making, explained in an easy, exam-oriented way. Perfect for: Last-minute revision PYQ preparation Concept clarity in 60 seconds 📚 Topics covered: Research & Data Collection Hypothesis, Sampling, Questionnaire Operations Research & Linear Programming Decision Making, Risk & Uncertainty Game Theory & Simulation 👉 Watch till the end and save for revision. 👉 Follow for daily UGC NET Management notes.

Comment “DATA” for all projects & links! #coding #datascience #machinelearning #university #student

Performing joins especially with large datasets will be a huge challenge in data processing. Here is the fix. 👇 1️⃣ Make a broadcast join Instead of shuffling 50TB of data across the network to find matches, you should send a copy of the small table to every single worker node. 2️⃣ Map-Side Operation This converts the operation into a local lookup. Each executor holds the full 100MB table in RAM and joins it against its local slice of the 50TB data. 3️⃣ The Memory Trap Be careful -> if that “small” table grows too big (e.g., 2GB), broadcasting it will cause Out-Of-Memory (OOM) errors on the executors and crash the application. 4️⃣ Configuration Threshold Check the spark.sql.autoBroadcastJoinThreshold. If the table is slightly larger than the default (usually 10MB), the system might default to a slow Sort-Merge join unless I increase this limit. #dataengineering #bigdata #coding 🏷️ Data Engineering, Apache Spark, Coding Interview, Tech Interview, Big Data Processing, Spark, Python

How to Learn Data Structures & Algorithms For FREE - AlgoMap #java #software #softwarejobs #softwareengineer #datastructures #leetcode #programming #javadeveloper #datastructuresandalgorithms #python #softwaredeveloper #code #FAANG #coding #javascript #javascriptdeveloper #codingisfun #codinginterview #js #html #css #sql