UK Data Analysts Prioritize SQL Over Python

📊 Day 5 of My Data Analyst Journey – Learning Python Fundamentals Today’s Python learning session helped me understand how programs interact with users and process data. The focus was on three important concepts: 🔹 User Input in Python Python allows us to take input directly from users using the input() function. Example: name = input("Enter your name: ") print("Hello", name) This simple concept is powerful because it enables programs to collect information dynamically. 🔹 Typecasting (Type Conversion) Sometimes user inputs come as text, but we need numbers for calculations. That’s where typecasting comes in. Example: age = int(input("Enter your age: ")) print(age + 5) Here, int() converts the input into a number so Python can perform arithmetic operations. 🔹 Basic Calculations Python makes performing calculations simple and efficient. Example: a = int(input("Enter first number: ")) b = int(input("Enter second number: ")) print("Sum:", a + b) ✨ These concepts may look simple, but they form the foundation for data analysis, automation, and real-world problem solving. Every day I’m getting closer to my goal of becoming a Data Analyst—learning how data can be captured, processed, and analyzed using Python. Grateful for the guidance from Satish Dhawale (SkillCourse) for making these concepts easy to understand. Excited for Day 6 of the journey! 📊 #Python #DataAnalytics #LearningInPublic #DataAnalystJourney #PythonForDataAnalysis #SkillCourse

Python was scary at first, specially being someone from a non-technical background, until I realized I was overcomplicating it. I know there are many people out there who are starting their careers or learning Python with its increasing demand in the industry, and I just want to simplify things for them. Starting with the very basics of Python- Data Types. Think of data types as how Python understands information. Just like in real life, a name is different from a number, and a yes/no answer is different from both. There are 4 you need to know first: String- anything in text form "Hello", "John", "Data Analyst" Integer- whole numbers 10, 500, 2024 Float- numbers with decimals 3.14, 9.99, 100.5 Boolean- only two values, True or False Used when you're making decisions in your code That's it. No jargon. No complex theory. Every dataset you'll ever work with as an analyst is built on these 4 types. Understanding them early saves you hours of confusion later. I'll be breaking down Python step by step, keeping it as simple as possible. Follow along if you're just getting started, this series is for you. What part of Python confuses you the most? Comment below and I'll simplify it in my future posts. 👇 #Python #DataAnalytics #datascience #learnpython #dataanalyst

Excel is where many data journeys begin. Python is where they scale. The real challenge is not learning a new tool. It is understanding how the same logic translates across tools. Filtering rows, sorting data, creating columns, handling missing values, joining tables. These are not tool-specific skills. They are analytical thinking patterns. When you understand how Excel actions map to Python (Pandas), you stop memorizing syntax and start thinking like a data professional. For Excel users, this is the fastest path to transition into Python. For Python learners, this builds clarity on what is happening behind the code. For working analysts, this improves speed, flexibility, and problem-solving across tools. Same problem. Different tools. One mindset. The goal is not to replace Excel. It is to expand your capability. #DataAnalytics #Python #Excel #Pandas #DataScience #BusinessIntelligence #DataAnalyst #Analytics #DataSkills #LearnPython #ExcelTips #DataEngineering #ETL #DataTransformation

Most people rush into Python for data analysis… But skip the foundation that actually makes them effective. This is where many get stuck. Before writing a single line of Python, ask yourself: Can you confidently work with data in SQL? Because these 6 concepts are not optional — they are the building blocks of real analysis: ✔ Joins – Can you combine datasets correctly? ✔ Aggregations – Can you summarize data meaningfully? ✔ Window Functions – Can you analyze trends over time? ✔ Subqueries & CTEs – Can you break down complex logic? ✔ Data Cleaning – Can you trust your data? ✔ Filtering Logic – Can you extract the right insights? Here’s the truth 👇 Python doesn’t replace these skills… it amplifies them. If your SQL foundation is weak, your Python analysis will also be weak. But if you master these? You don’t just analyze data — you think like a data professional. 💡 The real question is: Are you learning tools… or building analytical thinking? #DataAnalytics #SQL #Python #DataSkills #LearningJourney #AnalyticsMindset

🚀 Day 4: Understanding Python Data Types for Data Science 🐍📊 As I continue building my foundation in Data Science with Python, today I explored one of the most important concepts in programming — Python Data Types. Data types define the kind of data a variable can store. Understanding them is essential because almost every data science task involves working with different types of data. What I explored today: 🔹 Integer (int) Used to store whole numbers. Example: age = 25 🔹 Float (float) Used to store decimal numbers. Example: price = 99.99 🔹 String (str) Used to store text data. Example: course = "Data Science with Python" 🔹 Boolean (bool) Represents logical values. Example: is_student = True 🔹 Common Data Structures in Python • List → Ordered collection of items Example: numbers = [1, 2, 3, 4] • Tuple → Ordered but immutable collection Example: coordinates = (10, 20) • Dictionary → Key-value data structure Example: student = {"name": "John", "age": 22} • Set → Unordered collection of unique elements Example: unique_numbers = {1, 2, 3} 🔹 Why Data Types Matter in Data Science In real-world datasets, data can be numbers, text, categories, or logical values. Understanding data types helps in data cleaning, transformation, and analysis. 📌 Today's takeaway: Mastering Python data types is a crucial step toward working with real datasets and building strong data analysis skills. A special thanks to my mentor, Nallagoni Omkar sir 🙏 , for guiding me and helping me strengthen my Python fundamentals for Data Science. Next up: Python Operators! 🚀 #Python #DataScience #ProgrammingFundamentals #LearningInPublic #CodingJourney #StudentOfDataScience #MachineLearning #NeverStopLearning #omkarnallagoni Nallagoni Omkar

Starting your journey as a Data Analyst? Don’t overlook the basics of Python — especially Lists, Tuples, Sets, and Dictionaries. Here’s why they matter: • Lists – Handle ordered, flexible data (like datasets you’ll analyze) • Tuples – Store fixed data that shouldn’t change • Sets – Help remove duplicates and work with unique values • Dictionaries – Organize data in key-value pairs (very useful for structured data) In real-world analytics, data is rarely clean or structured. These core data structures help you store, clean, transform, and analyze data efficiently. Strong fundamentals in Python directly translate to better problem-solving and faster insights. Keep learning. Keep building. 🚀 #DataAnalytics #Python #LearningJourney #DataAnalyst #CareerGrowth

I thought Python was just doing calculations, until it gave me a “wrong” answer 😅 I was like: “How is this even possible??” Then I discovered something that changed everything Operators don’t just run, they follow rules. Let me explain this like I’m talking to a baby Imagine 3 kids solving math Kid 1: “Let’s go left to right” Kid 2: “No, start from the right” Kid 3: “Follow the rules first!” That’s exactly how Python behaves. What are Operators?Operators are just symbols like: ➕ ➖ ✖️ ➗ ** % They tell Python what to do with numbers. Python doesn’t just calculate randomly. It follows priority + binding rules. Two important rules I learned Modulo (%) → Left to Right For example: 20 % 6 % 4 = (20 % 6) % 4 = 2 % 4 = 2 Exponent (**) → Right to Left For Example: 2 ** 3 ** 2 = 2 ** (3 ** 2) = 2 ** 9 = 512 🤯 I used to think python is giving wrong answers Now I know that python is always correct, I just didn’t understand the rules. As I grow from excel to SQL and to Tableau and now python I’m learning that: Small mistakes = wrong insights Wrong insights = wrong decisions And in data, that’s dangerous Python is not confusing, it’s just very obedient to its rules. If you’re learning python, have you ever been surprised by a result like this? 😅 #Python #DataAnalytics #LearningInPublic #SQL #Excel #Tableau #Programming #TechJourney #BeginnerInTech #DataScience #CareerGrowth

Most beginners learn Python… but very few learn how to apply it to real data. Over the past few days, I completed Day 04, 05 & 06 of a Data Science Python Challenge and focused on building practical analytical skills. 🔹 Day 04 — Used loops to calculate total and average weekly sales 🔹 Day 05 — Created reusable functions to compute Mean, Median & Mode 🔹 Day 06 — Implemented a dictionary-based word frequency counter What I strengthened through this challenge: • Data aggregation using loops • Writing modular and reusable functions • Statistical thinking for data analysis • Working with dictionaries for text data • Clean and structured Python coding These small exercises are helping me build a strong foundation for real-world data analysis and problem-solving. Small data insights today lead to powerful decisions tomorrow. ABTalksOnAI Anil Bajpai #Python #DataScience #DataAnalytics #LearningInPublic #DataAnalyst #Statistics #CodingJourney #100DaysOfCode

Are you ready to unlock the power of Python and take your data management skills to the next level? Join us for our exciting course, "Mastering Python for Data Grouping and Validation"! This course is designed specifically for adults eager to dive into the world of data. You'll explore real-world applications where you'll learn to group information, validate data for accuracy, perform reservation calculations, and build robust projection models. Whether you're looking to elevate your career or simply satisfy your curiosity about data, this course will equip you with essential tools and insights. Here’s a sneak peek at what we’ll cover: 1. Introduction to Python: From the basics to setting up your development environment, we’ll get you comfortable in no time. 2. Grouping Information with Python: Learn to group data effectively using lists, dictionaries, and the powerful Pandas library. 3. Data Validation Techniques: Discover why data validation is crucial and how to implement it with Python. 4. Performing Reservation Calculations: Create dynamic reservation systems and analyze real-world case studies. 5. Building Projection Models: Dive into financial projections and develop dynamic models that take your skills to a new height. 6. Project and Evaluation: Put your skills to the test with a final project and peer review. By taking this course, you’re not just gaining knowledge; you’re also enhancing your career prospects. You'll learn practical skills that employers are looking for, all while enjoying the process. So, are you ready to roll up your sleeves and become a data wizard? Sign up today at https://lnkd.in/gJ7TmVCx. Your journey into the world of Python awaits!

⚡ Data Cleaning in Python — The Only Cheat Sheet You’ll Ever Need Data cleaning isn’t the most exciting part of analytics… but it’s where real insights are built. In fact, most analysts spend 70–80% of their time just preparing data. ⚡ This cheat sheet brings together the most-used Python commands you’ll rely on in real projects: ✔️ Quickly inspect datasets ✔️ Handle missing values efficiently ✔️ Clean & transform messy data ✔️ Filter and select the right information ✔️ Perform aggregations & analysis ✔️ Merge and combine datasets seamlessly 💡 Whether you’re preparing for interviews or working on live projects, these are the commands you’ll keep coming back to. Save this post — it’s the kind of reference you’ll open again and again. 🔁 Repost to help others learn 💬 Comment “PYTHON” if you want more cheat sheets like this hashtag #python hashtag #datacleaning hashtag #cheatsheet hashtag #analytics hashtag #datascience