Le Luu’s Post

Day 5 of My 108-Day Data Analytics Challenge: Today’s focus was on strengthening my understanding of SQL, Tableau, and Python. Here’s what I accomplished: ➡ SQL: • Focused on core SQL commands like `SELECT`, `DISTINCT`, and `COUNT`. These are essential for querying databases and retrieving meaningful insights. I’m starting to see how SQL forms the backbone of data manipulation. • I’m also keeping detailed notes on SQL concepts, which I’ll be sharing soon to help others who are learning alongside me. ➡ Tableau: • Today was all about combining data in Tableau. I learned the different methods such as Joins, Unions, Relationships, and Data Blending, which are critical for integrating multiple datasets into cohesive visualizations.  • I also explored the logical & physical layers in data modeling—helping me better understand how to structure data efficiently. ➡ Python (Numpy): • Continued my journey with Numpy by diving deeper into array manipulations. Learned array transposition, universal array functions, and efficient data processing techniques using arrays.  • Also covered input/output operations for arrays, which is key for storing and retrieving data during analysis. #DataAnalytics #SQL #Tableau #Python #Numpy #DataVisualization #LearningJourney #DataScience #TechSkills #ContinuousLearning #BusinessIntelligence #DataModeling #DatabaseManagement #GrowthMindset #108DayChallenge

🚀 Automation using Python 🚀 I've just completed a Python script designed to streamline and automate the reporting process. This powerful tool can: 🔍 Read Data: Seamlessly connect and pull data from multiple sources like BigQuery, MYSQL, Hive, Power BI, and Tableau. ⚙️ Run Queries: Execute complex queries automatically based on user inputs across these platforms, making data retrieval more efficient. 📊 Excel Automation: Automatically write the results to Excel, with custom functions to format sheets, create pivot tables, and generate insightful charts. 💻 Complete Automation: This script serves as a third-party source-to-Excel report automation tool, ensuring that all reporting needs are met with precision and detail. This is just a small sample of what the script can do! It’s designed with scalability in mind, meaning it can be extended to run queries on a larger scale. Plus, it has the potential to parallelize workloads, significantly increasing efficiency and reducing the time required for data processing. Please, visit my portfolio to know more about my projects, Project: https://lnkd.in/dYcTymSK Portfolio: https://lnkd.in/dUTjaVf3 #Python #Automation #DataScience #SQL #PowerBI #Tableau #Excel #DataAnalysis #Productivity

Some Questions For #DataAnalyst. #𝗦𝗤𝗟 - How to replace all null value present in table with a default value? - How do you write a query to find duplicate rows in a table? - How would you perform a left join and filter out nulls in SQL? - What is a window function in SQL, and how do you use it for ranking data? - How do you calculate the cumulative sum for a column in SQL? - What is the difference between UNION and UNION ALL in SQL? #𝗣𝘆𝘁𝗵𝗼𝗻 - How do you import a CSV file into a pandas DataFrame, and how would you handle missing data? - How do you use list comprehensions to filter and transform data in Python? - What are the differences between the apply() and map() functions in pandas? - How do you visualize data using matplotlib or seaborn in Python? - How do you write a function to calculate the correlation between two numerical columns in a pandas DataFrame? #𝗘𝘅𝗰𝗲𝗹 - How would you use VLOOKUP or XLOOKUP to merge data between two Excel sheets? - What is the difference between absolute and relative cell references, and when would you use each? - How do you create a pivot table, and what types of data analysis can you perform with it? - How would you use conditional formatting to highlight cells that meet certain criteria? - How do you use the IF, AND, and OR functions together to create complex logical tests? #𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 - How would you create and customize a calculated column in Power BI? - What is the difference between a slicer and a filter in Power BI, and when would you use each? - How do you create relationships between tables in Power BI, and how do they impact your data model? - How would you set up row-level security (RLS) to control access to sensitive data in Power BI? - What is the purpose of DAX functions like CALCULATE and FILTER, and how do you use them? #𝗧𝗮𝗯𝗹𝗲𝗮𝘂 - How do you create a calculated field in Tableau, and what types of calculations can you perform? - What is a parameter in Tableau, and how can it be used to create interactive dashboards? - How do you use a dual-axis chart in Tableau to show multiple measures in the same view? - How would you optimize a Tableau dashboard for performance when working with large datasets? - How do you create a custom date filter in Tableau to allow users to select specific date ranges?

Beginner’s Guide: Must-Know Interview Questions for Beginners SQL Server 1. What are the different types of joins in SQL, and when would you use each? 2. How do you optimize a SQL query for better performance? 3. What is the purpose of indexing in SQL databases? Excel 1. How do you perform a VLOOKUP and HLOOKUP in Excel? 2. What are pivot tables, and how do you create and use them? 3. How do you use conditional formatting in Excel to highlight important data? Power BI 1. How do you create a calculated column and a measure in Power BI, and what are the differences between them? 2. What is DAX, and how do you use it for advanced calculations in Power BI? 3. How do you set up and use Power BI dashboards to monitor key metrics? Python 1. How do you read and write data from/to different file formats in Python (e.g., CSV, JSON)? 2. What are lambda functions in Python, and when should you use them? 3. How do you use libraries like pandas and NumPy for data analysis in Python? Data Visualization 1. How do you tell a compelling story with your data visualizations? 2. What are the differences between exploratory and explanatory data visualization? 3. How do you design a dashboard that is both informative and user-friendly? Follow for more Priyanka SG #Excel #SQL #PowerBI #Python #Dataanalyst

Extracting Data from API and Parse #JSON in #Tableau Desktop - Part 2 On Monday, I posted the first part of my blog about extracting data from APIs and parsing JSON in Tableau Desktop. I shared the API definition and JSON structure, how to extract data from APIs and parse JSON with Python, and how to run a Python script in Tableau Desktop. Continue to that first part, I will introduce the complex JSON structure which can include an array with nested objects and nested arrays. The nested array contains multiple objects. In this second part, I will go through: 1/ Analyzing the complex JSON structure 2/ Parse the complex JSON and store data as tables in Python 3/ Apply Python script and build relationship models in Tableau Desktop I hope my blog can give you an idea or an option when you work on the complex JSON structure in Tableau Desktop. Although this blog is long, I tried explaining the contents in detail. I hope you enjoy reading it and I am looking forward to hearing feedback from you. The link to this second part is in the comment. See you soon in the next blog! #tabpy #api #extract #parse

🔍 Navigating the Data Analysis Toolkit: Tableau, Excel, or Python? 🔎 In the ever-expanding universe of data analysis, the choice of tool can make all the difference. Whether you're dissecting data for insightful visualizations, crunching numbers for business forecasts, or delving into predictive analytics, the key lies in selecting the right instrument for the task at hand. 📊 Tableau: Your go-to for crafting story-driven, interactive dashboards that speak volumes. Its intuitive interface opens the doors to complex visualizations, making data not just seen but experienced. 📈 Excel: The venerable Swiss Army knife of data manipulation and analysis. From spreadsheets to pivot tables, Excel's familiar grid layout and formula-based environment make it indispensable for quick analyses and financial modeling. 🐍 Python: The powerhouse of data science. With libraries like Pandas and NumPy, Python transcends traditional analysis, offering scalable solutions from data preprocessing to machine learning, all within a cohesive coding environment. Each tool has its stage, its moment where it shines brightest, determined by the nature of your data, the scale of your project, and the audience for your insights. 🤔 Have you found yourself at a crossroads, choosing between these tools? Share your experiences, tips, or questions below. Let's demystify the path to data enlightenment together! #DataAnalysis #Tableau #Excel #Python #DataScience #BusinessIntelligence

My bread and butter are SQL, Python, and Tableau. I'm confident that I can do most things that require of me on a project. And it's not only because I know the tools, but I'm also familiar with the concepts and knowledge associated with them. Things like ETL, data viz, and data modelling. It's like the 80/20 rule. You get very good at a few things and everything else will follow. Focus on what matters and keep expanding on your knowledge and skills. #python #sql #tableau #datamodeling #skills #dataviz #knowledge

I run a LOT of R and an increasing amount of Python within Power BI. While Power BI has a strong preference for star schema, often R and Python work best with flat files. Here how to have it both ways at minimal "cost"... While Power BI and R/Python integrate beautifully, and that integration dramatically expands Power BI's capabilities in areas including statistical analysis, forecasting, machine learning, advanced data cleaning, AI, webscraping and API handling, and many others, the one area where they have a minor squabble is in their underlying data model preferences. Power BI is largely "star schema or bust" - you can use other data models, but it usually substantially complicates your DAX and decreases your report performance. Here are two links, the former a prior post of mine, and the latter an article from SQLBI, explaining why this is: 🔸https://lnkd.in/eM-J57zk 🔸https://sql.bi/704257 R and Python, however, tend to work best with a simple flat file, particularly so when you are working via the Power Query integration, which just pulls in a single table called "dataset", which is the table resulting from the prior step before invoking an R or Python script from within PQ. My solution to this is just to build a compliant star schema that Power BI can use to its heart's content, paired with what I call "Fact Table Plus" which is just the standard fact table with the necessary additional columns needed by R/Py, joined from the appropriate dimension tables. This type of denormalization typically is very effective on net, since it typically does not involve high cardinality columns, and thus Power BI vertipaq compression will minimize the increase in file size. IMO this size penalty is usually more than offset by the gains in speed, simplicity, and transparency that result from being able to slide this table directly into R or Python with minimal to no additional transformations required before jumping right into the desired analyses. In addition, you can hide these extra columns when you're back in the report view in Power BI to avoid any confusion. If only all disagreements in life could be resolved so simply... 😄 I hope you find this helpful. #powerbi #r #python #DAX #datamodeling #powerquery #statisticalanalysis

Exploring key skills and tools in Business Data Analytics to drive data-driven insights and strategic decisions. #BusinessDataAnalytics #DataVisualization #SQL #Python #PowerBI #Tableau #Excel #DataCleaning #PredictiveAnalytics #GoogleAnalytics #MachineLearning #KPI #Dashboard #DataMining #BusinessIntelligence #BigData #DataAnalysis #SentimentAnalysis #DataDrivenDecisions #DataTrends

There are few very effective tool for analyzing data. A brief introduction about them. 1.    Excel - for smaller-scale (i.e., roughly 1 million rows of data or fewer) data analytics. It comes with robust analytical functions s.a. calculation, graphing and grouping etc. 2.    Tableau - strong focus on visualization 3.    SQL - programming language designed specifically for manipulating data in relational databases 4.    Python - to clean data, calculate statistics, scrape data from the web, and load records into data warehouses #dataanalyst #excel #python #sql #tableau #data