Data with Josh

05/10/2023

28/09/2023

27/09/2023

Alright, I better make some coffee.
Original: TechGig

19/09/2023

Standardize before you optimize!

18/09/2023

This is how I make my data visualizations in Excel look cleaner and better.

30/08/2023

📊 𝗧𝗵𝗲 𝗟𝗶𝗳𝗲 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗜 𝗖𝗼𝗺𝗺𝗼𝗻𝗹𝘆 𝗙𝗮𝗰𝗲 📊

As someone who's been in the data work for a while, I figured to share some behind-the-scenes challenges we face and how I've managed to tackle them. As a brief background, I usually set up web scrapers, create dashboards, and improve the quality of databases. With all that, here are the struggles I faced!

1️⃣ 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗪𝗼𝗲𝘀
One of the first things you'll realize is that a clean dataset is as rare as a unicorn 🦄. Seriously, you'll spend a significant chunk of your time removing duplicates, filling in missing values, and ensuring data consistency.
👉 𝙃𝙤𝙬 𝙄 𝙊𝙫𝙚𝙧𝙘𝙖𝙢𝙚 𝙄𝙩: Automation is your friend. I used Python libraries like Pandas to create reusable data cleaning pipelines.

2️⃣ **The Inconsistency of Data**
Imagine getting data in multiple formats, from Excel spreadsheets to JSON files, and sometimes even handwritten notes! The inconsistency is a real headache.
👉 𝙃𝙤𝙬 𝙄 𝙊𝙫𝙚𝙧𝙘𝙖𝙢𝙚 𝙄𝙩: Standardization is key. I worked with my team to create data ingestion templates so that all incoming data fits a uniform structure. This made my life so much easier!

3️⃣ 𝗟𝗼𝘀𝘁 𝗶𝗻 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀
You'd be surprised how often projects begin without a clear aim. It's like setting sail without a compass 🧭.
👉 𝙃𝙤𝙬 𝙄 𝙊𝙫𝙚𝙧𝙘𝙖𝙢𝙚 𝙄𝙩: Before diving into any project, I now insist on a clear brief, outlining the objectives, KPIs, and the stakeholders involved. Trust me, clarity upfront saves so much time later!

4️⃣ 𝗧𝗵𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗛𝘂𝗿𝗱𝗹𝗲
Ever tried explaining p-values or decision trees to a marketer or a finance guy? Yep, not everyone speaks 'Data'.
👉 𝙃𝙤𝙬 𝙄 𝙊𝙫𝙚𝙧𝙘𝙖𝙢𝙚 𝙄𝙩: Storytelling is a skill every data analyst should master. I began using simple analogies, charts, and even memes to make my point clear to non-technical audiences. Data visualization tools like Tableau have been a lifesaver!

5️⃣ 𝗥𝗮𝗰𝗲 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗧𝗶𝗺𝗲
Deadlines. They can be your worst enemy, especially when you're sifting through gigabytes of data.
👉 𝙃𝙤𝙬 𝙄 𝙊𝙫𝙚𝙧𝙘𝙖𝙢𝙚 𝙄𝙩: Time management is crucial. I started chunking down big tasks into smaller, manageable tasks and prioritized them. Oh, and I've learned to say 'no' when it's necessary.

So there you have it, my list of challenges and how I've maneuvered around them. I hope this gives you a bit of insight into what lies ahead. But remember, every struggle is a step forward. Keep hustling! 💪

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Feel free to ask questions or share your own experiences in the comments below! ✌️

29/08/2023

Sharing this as I've been using this for several months now.

28/08/2023

📊 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗖𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 📊

I wanted to take a moment to share some invaluable experiences from my data analytics journey, specifically focusing on the role of curiosity. I firmly believe that curiosity is the catalyst for deeper insights and meaningful discoveries. Here's how I've nurtured my curiosity over the years:

1️⃣ 𝗔𝘀𝗸𝗶𝗻𝗴 𝗢𝗽𝗲𝗻-𝗘𝗻𝗱𝗲𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

One of the first things that spurred my growth in this field was my knack for asking open-ended questions. Rather than just accept what's presented, I constantly found myself asking, "What's the underlying cause here?" or "What patterns can I observe over a longer timeframe?" Trust me, it's these questions that often lead to the most interesting findings.

2️⃣ 𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗪𝗶𝗱𝗲𝗹𝘆

I can't emphasize enough the power of reading. I started with literature from psychology, economics, and even philosophy. I found that the broader my knowledge base became, the more innovative my approaches to data analysis were. Reading widely opens up your mind to new methodologies and lets you see data from various angles.

3️⃣ 𝗦𝘂𝗿𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗠𝘆𝘀𝗲𝗹𝗳 𝘄𝗶𝘁𝗵 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗣𝗲𝗼𝗽𝗹𝗲

Networking isn't just for job hunting. I made it a point to be a part of communities where the thirst for knowledge was palpable. Interacting with like-minded individuals has not only made me more curious but also given me fresh perspectives that I wouldn't have considered otherwise. This has been a game-changer for me.

4️⃣ 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 𝗔𝘀𝗶𝗱𝗲 𝗧𝗶𝗺𝗲 𝗳𝗼𝗿 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Just like you'd schedule time for a workout or learning a new skill, I have what I call a 'Curiosity Hour' in my week. This is my time to deep-dive into datasets without a particular agenda, explore new tools, or play around with different visualization techniques. It's amazing what you can stumble upon when you're not looking for something specific.

5️⃣ 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘁𝗵𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝘀

Early on, I decided to follow industry leaders and experts in data science. The advantage? You get to be on the frontline of what's trending, what's debated, and what's groundbreaking. Following these thought leaders has significantly broadened my horizons and inspired many a-ha moments.

6️⃣ 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗢𝘁𝗵𝗲𝗿𝘀

Believe it or not, I've found that teaching is a super-effective way to solidify my own understanding. It could be through blog posts, webinars, or casual conversations. Each time I share my knowledge, I'm forced to look at data analysis through a new lens, and think critically, and inevitably, I end up learning something new myself.

🌟 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀

If you're aspiring to be a data analyst or looking to up your game, let curiosity be your guide. It has served me incredibly well, and I'm confident it will do the same for you. Stay curious, friends! 🚀

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Feel free to share, comment, or ask questions. I'd love to hear your thoughts and experiences on this! 💡

23/08/2023

Come and code with me!
Topic: Running sentiment analysis on E-Commerce site reviews (Web scraping + Sentiment Analysis)
We'll be doing some Web scraping, data manipulation, and some data science!
Nothing too fancy. Just coding and chatting. Tara!

23/08/2023

🚀 𝗗𝗶𝘃𝗲 𝗗𝗲𝗲𝗽 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗪𝗼𝗿𝗹𝗱 𝗼𝗳 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗮𝗻𝗱 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴!

🤔 Ever wondered how your email filters out spam so efficiently? Or how do shopping sites group similar products? The magic lies in machine learning!

📘 At its core, machine learning algorithms can be broadly categorized into Supervised and Unsupervised learning. Let's unpack these terms.

𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

📘 In supervised learning, we train models using labeled data, meaning each example in our dataset comes with a corresponding label or answer.

🔍 𝗦𝗮𝗺𝗽𝗹𝗲 𝗠𝗼𝗱𝗲𝗹𝘀:

- 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿𝘀: These are algorithms designed to categorize or "classify" input data into defined categories or classes.
- 𝘿𝙚𝙘𝙞𝙨𝙞𝙤𝙣 𝙏𝙧𝙚𝙚𝙨: Splits data based on feature values, like deciding if an email is spam based on certain keywords. Ideal for scenarios with clear-cut decision criteria.
- 𝙉𝙖𝙞̈𝙫𝙚 𝘽𝙖𝙮𝙚𝙨: Based on Bayes' theorem, often used for text classification, such as spam filtering. Efficient and effective for large datasets.
- 𝙇𝙤𝙜𝙞𝙨𝙩𝙞𝙘 𝙍𝙚𝙜𝙧𝙚𝙨𝙨𝙞𝙤𝙣: Predicts the probability of an instance belonging to a particular class, like determining the likelihood of a customer buying a product. Use when relationships are more linear.
- 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀: Predicts continuous values instead of classes.
- 𝙇𝙞𝙣𝙚𝙖𝙧 𝙍𝙚𝙜𝙧𝙚𝙨𝙨𝙞𝙤𝙣: Predicts a linear relationship between input and output. Use when the relationship between the input and the output seems to be linear.
- 𝙋𝙤𝙡𝙮𝙣𝙤𝙢𝙞𝙖𝙡 𝙍𝙚𝙜𝙧𝙚𝙨𝙨𝙞𝙤𝙣: Fits a non-linear relationship. Choose when the data shows a curvy trend.
- 𝙎𝙪𝙥𝙥𝙤𝙧𝙩 𝙑𝙚𝙘𝙩𝙤𝙧 𝙍𝙚𝙜𝙧𝙚𝙨𝙨𝙞𝙤𝙣: Focuses on finding the best margin to predict continuous values. Effective when working with high-dimensional data.

💡 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀:

- Predicting whether a credit card transaction is fraudulent (classifier: Decision Trees).
- Estimating stock prices based on historical data (regression: Polynomial Regression).

𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:

📘 In unsupervised learning, models explore patterns in unlabeled data, meaning we don’t provide the answers. The algorithm figures out the patterns on its own.

🔍 𝗦𝗮𝗺𝗽𝗹𝗲 𝗠𝗼𝗱𝗲𝗹𝘀:

- 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴: Grouping similar data points together.
- 𝙠-𝙈𝙚𝙖𝙣𝙨: Divides a dataset into 'k' number of clusters. Use when the number of clusters is known beforehand.
- 𝙃𝙞𝙚𝙧𝙖𝙧𝙘𝙝𝙞𝙘𝙖𝙡 𝘾𝙡𝙪𝙨𝙩𝙚𝙧𝙞𝙣𝙜: Builds a tree of clusters. Ideal when understanding hierarchical relationships in the data.
- 𝘿𝙞𝙢𝙚𝙣𝙨𝙞𝙤𝙣𝙖𝙡𝙞𝙩𝙮 𝙍𝙚𝙙𝙪𝙘𝙩𝙞𝙤𝙣: Reducing the number of random variables under consideration, obtaining a set of principal variables.
- 𝙋𝙧𝙞𝙣𝙘𝙞𝙥𝙖𝙡 𝘾𝙤𝙢𝙥𝙤𝙣𝙚𝙣𝙩 𝘼𝙣𝙖𝙡𝙮𝙨𝙞𝙨 (𝙋𝘾𝘼): Transforms the original variables into a new set of variables which are linear combinations of the original variables. Use when trying to simplify the data without losing much information.
- 𝙩-𝙎𝙉𝙀: Useful for visualizing high-dimensional data in a 2D or 3D space. Great for visual exploratory data analysis.

💡 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀:

- Segmenting customers into distinct categories based on purchasing behavior (clustering: k-Means).
- Visualizing gene expression data to identify patterns (dimensionality reduction: t-SNE).

🎁 Whether it's making sense of huge data sets, predicting future trends, or automating tasks, both supervised and unsupervised learning have essential roles in shaping modern industries.

21/08/2023

🔍 𝗬𝗼𝘂𝗿 𝗣𝗮𝘁𝗵 𝘁𝗼 𝗗𝗮𝘁𝗮 𝗠𝗮𝘀𝘁𝗲𝗿𝘆: 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽! 🚀

When I was introduced to data analytics, it was through a class in statistics. After that, I explored and saw a vast amount of resources in the internet, which to be honest, can be a bit overwhelming. Here's a roadmap that I believe covers the majority of the fundamental skills and knowledge you need to have to get into data analytics.

🛠️ 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 𝟭: 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗠𝘀 𝗘𝘅𝗰𝗲𝗹 & 𝗚𝗼𝗼𝗴𝗹𝗲 𝗦𝗵𝗲𝗲𝘁𝘀
✅𝘍𝘰𝘳𝘮𝘶𝘭𝘢𝘴 𝘢𝘯𝘥 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴: Dive into the most common formulas like VLOOKUP, HLOOKUP, and pivot tables.
✅𝘋𝘢𝘵𝘢 𝘊𝘭𝘦𝘢𝘯𝘪𝘯𝘨 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴: Understand how to deal with duplicates, missing values, and format inconsistencies.
✅𝘝𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘉𝘢𝘴𝘪𝘤𝘴: Create charts and graphs to represent your data visually.
✅𝘈𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘍𝘦𝘢𝘵𝘶𝘳𝘦𝘴: Experiment with macros, conditional formatting, and data validation tools.

🛠️ 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 𝟮: 𝗗𝗶𝘃𝗶𝗻𝗴 𝗗𝗲𝗲𝗽 𝗶𝗻𝘁𝗼 𝗦𝗤𝗟
✅𝘋𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘍𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴: Grasp the basics of databases, tables, keys, and relationships.
✅𝘞𝘳𝘪𝘵𝘪𝘯𝘨 𝘘𝘶𝘦𝘳𝘪𝘦𝘴: Fetch, filter, and sort data using SELECT, WHERE, and ORDER BY.
✅𝘊𝘰𝘮𝘣𝘪𝘯𝘪𝘯𝘨 𝘋𝘢𝘵𝘢: Master JOINs, UNIONs, and sub-queries to compile data from multiple sources.
✅𝘈𝘨𝘨𝘳𝘦𝘨𝘢𝘵𝘪𝘰𝘯𝘴: Use GROUP BY, COUNT, SUM, and other functions to summarize data.
✅𝘋𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘔𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵: Get a basic understanding of database optimization and indexing.

🛠️ 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 𝟯: 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 & 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
✅ 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘋𝘦𝘴𝘪𝘨𝘯: Understand the principles of effective dashboard design for clear data storytelling.
✅𝘐𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘷𝘪𝘵𝘺: Add filters, dropdowns, and sliders to make your visualizations user-friendly.
✅𝘊𝘶𝘴𝘵𝘰𝘮 𝘊𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘪𝘰𝘯𝘴: Use calculated fields and parameters to derive new insights.
✅𝘋𝘢𝘵𝘢 𝘉𝘭𝘦𝘯𝘥𝘪𝘯𝘨: Mix and match data from different sources seamlessly.
✅𝘗𝘶𝘣𝘭𝘪𝘴𝘩𝘪𝘯𝘨 & 𝘚𝘩𝘢𝘳𝘪𝘯𝘨: Learn to publish your reports and dashboards and share them with stakeholders.

🛠️ 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲 𝟰: 𝗣𝘆𝘁𝗵𝗼𝗻 - 𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗯𝘂𝘁 𝗛𝗶𝗴𝗵𝗹𝘆 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱
✅𝘉𝘢𝘴𝘪𝘤 𝘚𝘺𝘯𝘵𝘢𝘹 & 𝘖𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴: Get to grips with Python’s intuitive syntax, variables, and basic operations.
✅𝘋𝘢𝘵𝘢 𝘔𝘢𝘯𝘪𝘱𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘗𝘢𝘯𝘥𝘢𝘴: Learn how to use the Pandas library to clean, analyze, and visualize data.
✅𝘋𝘢𝘵𝘢 𝘝𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘔𝘢𝘵𝘱𝘭𝘰𝘵𝘭𝘪𝘣 & 𝘚𝘦𝘢𝘣𝘰𝘳𝘯: Generate insightful graphs, plots, and charts to visualize your findings.
✅𝘐𝘯𝘵𝘳𝘰 𝘵𝘰 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘚𝘤𝘪𝘬𝘪𝘵-𝘭𝘦𝘢𝘳𝘯: Take a preliminary dive into algorithms, data modeling, and prediction techniques.
✅𝘈𝘗𝘐𝘴 & 𝘞𝘦𝘣 𝘚𝘤𝘳𝘢𝘱𝘪𝘯𝘨: Grasp the basics of pulling data from the web, be it through direct APIs or web scraping tools like BeautifulSoup.

Becoming proficient in Python can immensely boost your data analytics capabilities. While it's optional on this roadmap, mastering Python is highly recommended for those aiming to go beyond traditional data analysis and delve into advanced data science. Think of it as a tool that unlocks endless analytical possibilities!

19/08/2023

And this is why we write readable codes and include documentation. 🫠

18/08/2023

🔍 𝗛𝗼𝘄 𝗠𝘂𝗰𝗵 𝗥𝗔𝗠 𝗦𝗵𝗼𝘂𝗹𝗱 𝗜 𝗵𝗮𝘃𝗲? 🖥️

Ever found your tools dragging their digital feet? Ever experienced waiting for 325 minutes while waiting for your charts to update? Your computer's RAM might be the culprit. Here’s an in-depth look at how much RAM is essential for different data roles:

1. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Primarily leveraging platforms like MS Excel, Google Sheets, or BI tools, analysts deal with varied dataset sizes. While 8GB of RAM is an acceptable base, for those intricate Excel models, or comprehensive Power BI dashboards, 16GB provides that extra room ensuring responsiveness and efficiency.

2. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: The science of data often demands heavier computational tasks. Python libraries, TensorFlow models, and extensive simulations can eat up RAM quickly. Starting at 16GB is recommended. However, scaling up to 32GB ensures that you won't hit any memory bottlenecks if you're running intensive deep-learning models or simulations.

3. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: As the backbone of data architecture and operations, engineers need to be equipped with robust systems. Working with ETL processes, large-scale data pipelines, and complex database operations, 16GB of RAM is a must-have. For substantial big data tasks or when using platforms like Hadoop or Spark, 32GB to 64GB becomes a game-changer, ensuring seamless data flows and operations.

While RAM is undeniably vital, it's part of a larger ecosystem. Pair it with a strong processor, an efficient GPU, and high-speed storage for a harmonious, lag-free experience. As an example, data scientists benefit a huge deal when having a GPU as it speeds up the training of models.

𝗔 𝗣𝗿𝗼 𝗧𝗶𝗽: If you’re regularly maxing out your RAM, it's a sign you might need an upgrade. But always analyze your exact needs before taking the plunge.

Which data career are you in and how much RAM are you using? Share us your experience!

16/08/2023

🫠🫠🫠

14/08/2023

🚀 𝑯𝒐𝒘 𝒅𝒐𝒆𝒔 𝑵𝒆𝒕𝒇𝒍𝒊𝒙 𝒐𝒓 𝒀𝒐𝒖𝑻𝒖𝒃𝒆 𝒌𝒏𝒐𝒘 𝒆𝒙𝒂𝒄𝒕𝒍𝒚 𝒘𝒉𝒂𝒕 𝒚𝒐𝒖'𝒓𝒆 𝒊𝒏 𝒕𝒉𝒆 𝒎𝒐𝒐𝒅 𝒕𝒐 𝒘𝒂𝒕𝒄𝒉 𝒏𝒆𝒙𝒕? 𝑻𝒉𝒆 𝒎𝒂𝒈𝒊𝒄 𝒃𝒆𝒉𝒊𝒏𝒅 𝒊𝒕: 𝑹𝒆𝒄𝒐𝒎𝒎𝒆𝒏𝒅𝒆𝒓 𝑺𝒚𝒔𝒕𝒆𝒎𝒔! 🎬✨

𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀?

These are specialized algorithms designed to predict and suggest items that a user would love to see, based on their past behavior. Think of them as your personal digital concierge, always on the lookout for your next favorite show or video.

📊 𝗧𝗵𝗲 𝗜𝗻𝘁𝗿𝗶𝗰𝗮𝘁𝗲 𝗠𝗮𝘁𝗵 𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗠𝗮𝗴𝗶𝗰:

While it seems almost magical, the essence of these systems is deep-rooted in complex mathematical concepts. Techniques from linear algebra, probability, and statistics come into play. It’s like weaving together a vast tapestry from threads of your watch history, likes, searches, and even skips.

👩‍💻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: 𝗧𝗵𝗲 𝗠𝗮𝗴𝗶𝗰𝗶𝗮𝗻𝘀 𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗖𝘂𝗿𝘁𝗮𝗶𝗻:

These professionals spend countless hours curating these systems. They utilize user-item interactions to train the algorithms. Two main methods are in play:

1. **𝑪𝒐𝒍𝒍𝒂𝒃𝒐𝒓𝒂𝒕𝒊𝒗𝒆 𝑭𝒊𝒍𝒕𝒆𝒓𝒊𝒏𝒈:** This method looks at past behaviors of you and other users to find patterns. Ever noticed how after watching a mystery thriller, you get recommendations of top-rated thrillers others watched? That’s collaborative filtering in action!
2. 𝑪𝒐𝒏𝒕𝒆𝒏𝒕-𝒃𝒂𝒔𝒆𝒅 𝑭𝒊𝒍𝒕𝒆𝒓𝒊𝒏𝒈: This focuses more on the attributes of items themselves. If you’ve been watching a lot of documentaries on space, this method ensures you get recommendations based on that content interest.

🔄 𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗖𝘆𝗰𝗹𝗲 𝗼𝗳 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:

Recommender systems are never stagnant. They are dynamic, continuously evolving with each click, like, or search you make. As more data is inputted, the system refines its recommendations, offering a more tailored viewing experience every time.

11/08/2023

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗲𝗮𝗻 𝘁𝗼 "𝗖𝗹𝗲𝗮𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮"?

One of the most crucial and possibly the most time-consuming part of the data analysis process is data cleaning. Think of it as prepping your ingredients before cooking a gourmet meal. Similarly, diving straight into analysis with messy data can lead to inaccurate results or misleading insights. So, before we immerse ourselves in the world of charts, graphs, and models, let's roll up our sleeves and get our data in tip-top shape! Keep in mind the saying “Garbage in, garbage out!”

🔍 𝟭. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗩𝗮𝗹𝘂𝗲𝘀: First and foremost, scan your dataset for any gaps. Sometimes, they're glaringly obvious, but other times they may be disguised as "N/A" or "0". Depending on the nature of your data, you can either fill in these gaps (maybe with an average or median) or decide to omit the entry entirely. Alternatively, you can check in with the person who provided you the data if these missing points can be filled up in other ways.

📏 𝟮. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Is your date format consistent? Are your currency values all in USD or PHP? Double-check these to avoid confusion later.

🔢 𝟯. 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀: You'd be surprised how often data can get recorded twice. Use tools like Excel's "Remove Duplicates" or Python's 'drop_duplicates()' function to ensure each entry is unique and accurate.

🔤 𝟰. 𝗖𝗼𝗿𝗿𝗲𝗰𝘁 𝗦𝗽𝗲𝗹𝗹𝗶𝗻𝗴 & 𝗚𝗿𝗮𝗺𝗺𝗮𝗿: Especially in categorical data, a slight spelling mistake can cause your software to treat "Analyst" and "Analsyt" as two entirely different categories.

🌐 𝟱. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗘𝗻𝘁𝗿𝗶𝗲𝘀: Ensure that data entries follow a set standard. For instance, if you're working with addresses, decide if you're using "St." or "Street" and stick to it throughout. Another example is in the case of “&” and “and”.

🔗 𝟲. 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀: For more complex databases, understand how tables relate. Does one ID in Table A correspond with another ID in Table B? Establishing these connections now can save you a ton of headaches later.

✨ 𝟳. 𝗩𝗲𝗿𝗶𝗳𝘆 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: This might sound tedious, but occasionally cross-checking entries with the original source can save you from major mishaps. Remember the age-old adage: "Trust, but verify." This would be easier than starting over the analysis just because of something easily corrected from the start.

Once you've given your data the TLC it deserves, you're all set to dive into the thrilling realm of data analysis! Remember, your analysis is only as good as the data you feed into it. So next time you're handed a fresh dataset, instead of diving straight into the deep end, take a moment to ensure it's clean, polished, and analysis-ready.

09/08/2023

📊 𝗪𝗵𝗶𝗰𝗵 𝗖𝗵𝗮𝗿𝘁 𝗗𝗼 𝗜 𝗨𝘀𝗲?? 📈

Have you ever been overwhelmed by a sea of numbers, unsure of how to present them visually? As a data analyst, this is a common question. I've been there, and I've learned that picking the right chart can be a game-changer. Let me share some insights on common chart types and when I find them most useful:

1. 𝗕𝗮𝗿 𝗖𝗵𝗮𝗿𝘁𝘀: I love using these when I need to compare individual categories.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: If I'm looking at the monthly sales of various products, a bar chart clearly highlights the top and bottom performers. The key here is if you are comparing between categories, this is a top candidate.
2. 𝗟𝗶𝗻𝗲 𝗖𝗵𝗮𝗿𝘁𝘀: These are my go-to for tracking trends over a span of time.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: When I monitor my website's traffic throughout the year, a line chart beautifully captures the peaks and valleys. You can also use this in measuring the sales of a company over time.
3. 𝗣𝗶𝗲 𝗖𝗵𝗮𝗿𝘁𝘀: Whenever showcasing parts of a whole, pie charts come to the rescue.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: Curious about how different smartphone brands stack up in market share? A pie chart gives a vivid breakdown. But. Please. Use. This. Chart. Sparingly! And only use this if the number of parts is under 5.
4. 𝗦𝗰𝗮𝘁𝘁𝗲𝗿 𝗣𝗹𝗼𝘁𝘀: I turn to these when I'm keen on spotting relationships between two variables.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: If I'm exploring a link between my advertising budget and sales, a scatter plot can reveal intriguing patterns. This can answer questions like "Do sales increase with a higher advertising budget?" This is a top choice if you want to understand correlations, basically.
5. 𝗛𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺𝘀: To grasp the distribution of a single variable, histograms are invaluable.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: If I'm analyzing the age distribution of my audience, a histogram neatly groups them, showing where the bulk lies. I use this chart almost every day!
6. 𝗔𝗿𝗲𝗮 𝗖𝗵𝗮𝗿𝘁𝘀: They're like line charts but with an added emphasis on magnitude.
𝐸𝑥𝑎𝑚𝑝𝑙𝑒: When I track the growth of my investments, an area chart accentuates the overall growth.

The essence isn't just to visualize data, but to narrate a clear, captivating story. My choice of which chart to use can illuminate or confuse, so I always choose with care! 🧠✨

07/08/2023

10 Reasons Why Data Science and Analytics Might Not Be for You

I've decided to write something a little different today. We're going to delve into the reasons why a career in Data Science and Analytics might not be the best fit for you. Remember, everyone is unique and finding the right career is all about finding what aligns with your skills, interests, and personal traits.

1️⃣ Lack of Curiosity: At the heart of Data Science and Analytics is an insatiable curiosity about the world and its patterns. If delving into data, asking questions, and searching for hidden patterns doesn't resonate with your passion, you may struggle to find satisfaction in this field. Data exploration requires a desire to dig deeper and unearth insights, which demands a deep-seated curiosity about the world.

2️⃣ Patience Isn't Your Virtue: Data Science often involves sifting through massive amounts of data to extract meaningful insights. This is not something that happens quickly. It can take time and extensive analysis to reach conclusions, and this requires patience. If you prefer fast-paced work with immediate results, the methodical pace of data analysis might frustrate you.

3️⃣ You Prefer Certainty: The realm of data is filled with uncertainties. Sometimes, the data you have is incomplete or inconsistent. Other times, the insights you derive may not be as definitive as you'd like. This field requires a comfort level with ambiguity and the ability to make informed decisions even when things are unclear.

4️⃣ You're Not a Fan of Continuous Learning: The field of Data Science and Analytics is in a constant state of evolution, with new methodologies, technologies, and best practices emerging regularly. To stay relevant, you need to embrace continuous learning and professional development. If you're not the type who enjoys learning new things regularly, this field might not be for you.

5️⃣ Math Scares You: While not every role in the data field requires an advanced understanding of mathematical concepts, a basic grasp of statistics is vital. If numbers and equations make you uncomfortable or you find them hard to comprehend, you may struggle in this field.

6️⃣ You Don't Enjoy Problem Solving: Data Science is all about tackling complex problems and finding solutions. This requires a logical mind and a knack for seeing how pieces of information fit together. If you prefer tasks with a clear path to completion, you might find the problem-solving aspect of Data Science challenging.

7️⃣ Communication Isn't Your Strong Suit: Being a data scientist or analyst isn't just about crunching numbers and generating insights. You must also be able to effectively communicate your findings to a variety of audiences, many of whom won't have a technical background. If you struggle with communication or don't enjoy this aspect of the role, it might not be the right fit for you.

8️⃣ You Prefer Working Alone: While there are plenty of opportunities for individual work in Data Science, it's also a highly collaborative field. You will often need to work with teams, share insights, and collaborate on projects. If you are someone who prefers to work alone, you might find this aspect challenging.

9️⃣ You're Not Detail-Oriented: In data analysis, minor details can have a major impact. Whether it's ensuring data quality or catching mistakes in your analysis, being detail-oriented is crucial in this field. If you tend to overlook small details, this could lead to major errors in this field.

🔟 You Lack the Drive to Apply Your Skills: Knowing the theory of data science is one thing, but being motivated to apply it in real-world scenarios is quite another. Data Science and Analytics is a field that requires practical application of knowledge. If you lack the drive to apply what you've learned, you might struggle to find satisfaction in this field.

If you find these points resonating with you, don't be disheartened! There are many other fields and career paths where your unique skills and interests can shine brightly. Understanding what isn't for you is just as important as understanding what is, so you can find your perfect career match. Remember, the goal is to find a career that brings you satisfaction, aligns with your skills, and makes you happy!

05/08/2023

🙄🙄🙄

04/08/2023

🤖 𝗔𝗜 𝘃𝘀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘃𝘀 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲?! 🧠

In the world of data and technology, terms like AI, Machine Learning, and Deep Learning are often used interchangeably. But they're not the same! Let's break it down:

1️⃣ 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜): This is the broadest concept. It refers to machines or software mimicking human intelligence. Think of voice assistants like Siri or Alexa - they use AI to understand and respond to your commands.

2️⃣ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟): This is a subset of AI. ML involves algorithms that allow machines to learn from data and improve over time without being explicitly programmed. For example, Netflix's recommendation system uses ML to suggest shows based on your viewing history.

3️⃣ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟): This is a subset of ML. DL uses neural networks with many layers (hence 'deep') to learn from vast amounts of data. An example is Google's image recognition system, which uses DL to identify objects in photos.

Remember, while these terms are related, they represent different levels of machine intelligence. From the broad capabilities of AI to the specific, data-intensive learning of DL, each has its unique role and application.

What's the most impressive application of AI, ML, or DL you've encountered? We'd love to hear your stories in the comments! 💬

03/08/2023

But why nooot? 😫

02/08/2023

💼💰 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗦𝗮𝗹𝗮𝗿𝘆 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 💰💼

Negotiating your salary can feel daunting, especially in the rapidly evolving field of data. Here are some tips to help you confidently negotiate your worth:

1️⃣ 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗪𝗼𝗿𝘁𝗵: Research the average salary for your role in your location and industry. Websites like Glassdoor and Payscale can provide valuable insights. For example, if you're a data analyst in New York, the average salary might be different from a data analyst in Austin.

2️⃣ 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀: In the data industry, certain skills can command a higher salary. If you're proficient in high-demand tools like Python, R, SQL, or Tableau, be sure to highlight these in your negotiation. Make a note of how these tools can create value in the organization.

3️⃣ 𝗦𝗵𝗼𝘄 𝗬𝗼𝘂𝗿 𝗜𝗺𝗽𝗮𝗰𝘁: Concrete examples of how you've driven value can strengthen your position. For instance, if you've developed a machine learning model that increased efficiency by 30%, bring this up during your negotiation.

4️⃣ 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗵𝗲 𝗧𝗼𝘁𝗮𝗹 𝗣𝗮𝗰𝗸𝗮𝗴𝗲: Salary is just one part of your compensation. Don't forget to consider benefits like health insurance, retirement contributions, professional development funds, and work flexibility.

5️⃣ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Role-play the negotiation with a friend or mentor. This can help you feel more comfortable and prepared when it's time for the real conversation.

Remember, negotiation is a two-way street. It's about finding a balance where you feel valued for your skills and contributions, and the company feels they're making a good investment. Good luck! 💪

What are your experiences with salary negotiation in the data industry? Share your stories and tips in the comments below! 👇

01/08/2023

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Original: 9GAG

31/07/2023

🚀 Journey Through Data Maturity Stages with SampleData Inc! 🚀

📚 Inspired by the book "Fundamentals of Data Engineering" by Joe Reis and Matt Housley, let's explore the stages of data maturity in companies. We'll use a theoretical company, SampleData Inc., as an example to illustrate how a company progresses through these stages.

1️⃣ 𝗣𝗿𝗲-𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗦𝘁𝗮𝗴𝗲: SampleData Inc. starts as a primarily paper-based company with little to no digital data. Recognizing the need for digital transformation, they begin digitizing their records and start using basic software tools for operations.

2️⃣ 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗦𝘁𝗮𝗴𝗲: Now, SampleData Inc. has digitized records and uses software tools for day-to-day operations. However, data is siloed and not used for strategic decision-making. They start integrating their data and using it to inform their decisions to progress to the next stage.

3️⃣ 𝗗𝗮𝘁𝗮-𝗔𝘄𝗮𝗿𝗲 𝗦𝘁𝗮𝗴𝗲: SampleData Inc. now uses data for decision-making, but it's mostly reactive, looking at past trends. They start using data proactively to predict future trends and outcomes to move forward.

4️⃣ 𝗗𝗮𝘁𝗮-𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗦𝘁𝗮𝗴𝗲: At this stage, SampleData Inc. uses data proactively to drive decisions, leveraging predictive analytics. However, data usage is not yet fully automated. They start implementing automated data-driven systems to advance.

5️⃣ 𝗗𝗮𝘁𝗮 𝗗𝗿𝗶𝘃𝗲𝗻 𝗦𝘁𝗮𝗴𝗲: Finally, SampleData Inc. has fully automated data-driven systems, and data is at the heart of all strategic decisions. They continue to innovate and stay data-driven by constantly updating their systems with the latest data technologies.

Remember, every company's journey is unique, and moving from one stage to the next takes time and strategic planning. But the rewards of becoming a data-driven organization are immense! 💼📈💡

Which stage of data maturity is your company currently at? Share your experiences in the comments below! 👇