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Skills need for everyday data analysis jobs

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“While noting that I labeled myself as proficient in SQL, I can’t tell you the difference right off the top of my head. A quick search to refresh my memory on JOINs would enable me to answer that for you. While I may not remember 100% of the details of SQL, I am not afraid to do research for a question or process I don’t have a clear answer to.” 😂

𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍 - Python - Artificial Intelligence, - Cybersecurity - Cloud Computing, and - Machine Learning 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/3E2wYNr Enroll For FREE & Get Certified 🎓

I once told a hiring manager I was “proficient in SQL.” In reality, I had watched half a YouTube tutorial on 2x speed. In the interview, she said: “What’s the difference between INNER JOIN and LEFT JOIN?” I said: “It depends on your mindset.” I blacked out. She smiled. I think it was pity. Lesson? Lie if you must. But memorize the script. And never lie about tech. They will test you immediately.

5 misconceptions about data analytics (and what's actually true): ❌ The more sophisticated the tool, the better the analyst ✅ Many analysts do their jobs with "basic" tools like Excel ❌ You're just there to crunch the numbers ✅ You need to be able to tell a story with the data ❌ You need super advanced math skills ✅ Understanding basic math and statistics is a good place to start ❌ Data is always clean and accurate ✅ Data is never clean and 100% accurate (without lots of prep work) ❌ You'll work in isolation and not talk to anyone ✅ Communication with your team and your stakeholders is essential

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Microsoft Excel → Python: In Excel, you'd use =AVERAGE(TableName[ColumnName]) to find the average. In Python: TableName['ColumnName'].mean() One line. Works even if you have 10 million rows.

Data Analyst Tech stack

Data Structure in Python

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Power BI DAX

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For data analysts working with Python, mastering these top 10 concepts is essential: 1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation. 2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats. 3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables. 4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling. 5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data. 6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn. 7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets. 8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently. 9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL. 10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources. Give credits while sharing: https://t.me/pythonanalyst ENJOY LEARNING 👍👍

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Projects to boost your resume for data roles