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
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🔍 Are you leveraging the power of data analytics effectively in your business strategies? As I dive deeper into my studies in data analytics and data science, I am continually amazed by how data can guide decisions in even the most uncertain scenarios. For instance, using Tableau to visualize sales data can reveal patterns that are not apparent from spreadsheets alone. I found that integrating Python for data analysis allows for more customized insights, enhancing predictive analytics significantly. How are you integrating data analytics into your business processes? I’d love to hear about the tools and techniques that are working for you! #DataAnalytics #BusinessIntelligence #Tableau #Python
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Just one of those weeks... Data Analysis is not only about tools, or working on complex SQL queries, Python models, PowerBI reports Sometimes, you will just be working on creating presentations and decks, that have nothing to do with these tools but still hold great value. I don't like making presentations, but I still like seeing them... #data #strategy #dataanalytics
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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
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Run #R Script in #tableau In the previous blogs, I wrote how to run the Python script in Tableau. Python is very helpful when we need to solve complex problems; especially when we need to forecast data, build data models, and return results to visualize in Tableau. For statistical data, R is one of the best languages if the user wants to work with statistical values. For some complex problems such as ANOVA, T-test, normality test, regression, and forecasting data, it's hard to build calculations in Tableau. But we can run the R script in Tableau Prep Builder or Tableau Desktop and return the statistical values we want. In my latest blog, I wrote how to install, load packages, and run Rserve in the R Console. I also shared how to connect Rserve on Tableau Prep Builder and Tableau Desktop. In the next blog, I am going to share the data structure when we input/output data from the R script to run in Tableau. I hope my blogs are helpful to you. Run R script in Tableau - part 1: https://lnkd.in/de8Kh4uz Python in Tableau - part 1: https://lnkd.in/dWmN4A4K Python in Tableau - part 2: https://lnkd.in/dK2apstz #statistics #statistical analytics
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#Technical Skills: Proficiency in tools such as: #Excel (advanced functions, pivot tables, and data visualization). #SQL (database querying and manipulation). #Python/R (for data analysis and machine learning). #Power BI/Tableau (for creating dashboards and reports). #Statistics and Predictive Modeling. #Soft Skills: Problem-solving, critical thinking, and communication.
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Any job involving data is more about data storytelling than anything else. Until companies start seeing data as a product, your job as a Data X is to show leadership why they should apply more resources and what their ROI will yield. It’s definitely an uphill battle but tenacity for the long-game is what’s required… and presentation skills. 😝
Just one of those weeks... Data Analysis is not only about tools, or working on complex SQL queries, Python models, PowerBI reports Sometimes, you will just be working on creating presentations and decks, that have nothing to do with these tools but still hold great value. I don't like making presentations, but I still like seeing them... #data #strategy #dataanalytics
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🚀 Unlocking Business Insights with Data Analytics 🚀 Today, I delved into optimizing a dataset to analyze the profitability of one of the services using Python and Power BI. 📊 Key takeaways: Data Aggregation Matters: Grouping data with fewer columns can often reduce row counts, but careful grouping ensures no data loss, especially in large datasets. Data Enrichment via Power Query: Adding calculated columns & using conditional logic can simplify insights. Consistency is Key: Whether working in Python or Power BI, ensuring consistent row counts before and after transformations guarantees data integrity for accurate reporting. Combining Technologies: Sometimes, merging the power of both Python and Power BI allows for more comprehensive and dynamic data exploration. Looking forward to applying these insights in future projects! 💡 #DataAnalytics #Python #PowerBI #BusinessIntelligence #D2D #Profitability #DataScience #LearningJourney #LinkedInLearning
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Must Study: These are the important Questions for Data Analyst(Beginners) SQL Server 1. Explain the difference between UNION and UNION ALL. 2. What is a subquery, and how is it different from a join? 3. How do you handle transactions in SQL Server? Excel 1. What are the different types of charts available in Excel, and how do you choose the right one? 2. How do you use Excel’s IF, AND, OR, and NOT functions for logical operations? 3. How do you create and manage named ranges in Excel? Power BI 1. How do you create and manage relationships between tables in Power BI? 2. What is the purpose of the Query Editor in Power BI, and how do you use it for data transformation? 3. How do you implement bookmarks and buttons in Power BI for navigation and interactivity? Python 1. What are Python's built-in data structures? 2. How do you handle missing data in a dataset using Python? 3. What are Python decorators, and how do you use them? Data Visualization 1. What are the key principles of effective data visualization? 2. How do you choose the right type of visualization for your data? 3. What are common pitfalls to avoid in data visualization? Follow more for Priyanka SG #Excel #Sql #PowerBI #Python #DataAnalyst
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#75DaysOfDataScienceChallenge - Day 45: R and Python Scripting in Power BI and its Applicability 🚀 Today’s exercise demonstrated the capabilities of scripted R and Python integration in Power BI, where I undertook advanced data analysis and built visuals. This integration feature permits data analysts to go further than standard features allowing room for innovations such data transformation, custom modeling and advanced visual representations. Here is the report of my undertaking: 🔹 Data Loading: Implementing Python and R scripts to load and prep data from various sources inside Power BI. 🔹 Data Transformation: Applying data wrangling techniques in Power Query using Python and R in order to improve information and clean up data. 🔹 Custom Visuals: Providing graphics using the likes of Matplotlib, ggplot2 among others for better visuals and analysis. 🔹 Scheduled Refreshes: Setting Power BIs gateways within my R and Python dashboards to enable automatic updates. Power BI becomes highly dynamic with the integration of Python and R which extends its capabilities to complete Projects in Data science. I am looking forward to utilizing these techniques in my upcoming projects. #DataScience #PowerBI #Python #RStats #DataVisualization #MachineLearning #DataAnalytics #DataScienceChallenge #LearningJourney #75_Days_Challenge #Powerbi #Powerbi_Dashboard #excel #data_science #DataScience #DataAnalysis #DataAnalytics #BigData #DataVisualization #DataDriven #DataInsights #Analytics #entri_elevate #75DaysOfDataAnalysisChallenge Dr.Jitha P Nair Entri Elevate Entri #DataScienceChallenge #75DaysOfDataScience #BusinessIntelligence #Tech #Coding #Statistics
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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
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