Eric Johnson’s Post

As a data analyst, you will use Excel a lot. From data cleaning, transformation to creating data summary, reports or dashboards. So, here are some Excel functions that you will be use on a daily basis as a data analyst. • 𝗠𝗮𝘁𝗵 𝗼𝗿 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Functions like SUM, COUNT and there extensions such as SUMIFS, COUNTIFS. • 𝗟𝗢𝗢𝗞𝗨𝗣 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Functions like VLOOKUP, HLOOKUP, XLOOKUP and INDEX-MATCH. • 𝗗𝗮𝘁𝗲 𝗮𝗻𝗱 𝗧𝗶𝗺𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Functions like TODAY, NOW, YEAR, MONTH, EOMONTH and more. • 𝗧𝗲𝘅𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Functions like LEFT, RIGHT, MID, TEXT, CONCAT and more. • 𝗟𝗼𝗴𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Functions like IF, IFS, AND, OR, IFERROR and more. • 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: It includes features or options such as 𝗣𝗶𝘃𝗼𝘁 𝗧𝗮𝗯𝗹𝗲𝘀 and 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹𝗽𝗮𝗸. So, these are some functions that you will use frequently as a data analyst using Excel. Enjoy and Follow for more! #ExcelTips #DataAnalytics #DataAnalyst #ExcelFunctions #DataTransformation #DataCleaning #DataSummarization #Reporting #DataAnalysis #ExcelSkills #ExcelForAnalysts #TechCareers #AnalyticsCommunity #DataProfessional #AnalyticsUSA #ExcelForDataUSA #DataJobsUSA #USTech #DataAnalyticsUK #ExcelUK #UKDataAnalyst #UKTechJobs #DataEurope #ExcelEurope #DataJobsEU #EuropeanTech #GlobalAnalytics #ExcelWorldwide #InternationalDataJobs #DataScienceGlobal

As a BI developer, Data Analyst, or even Data Scientist. You spend most of your time collecting data, working with data warehouses and, consequently, fact tables. As a result, it is crucial to fully understand fact tables, the main component within DWH, for capturing and analyzing quantitative data.   They provide the foundation for reporting, analysis, and decision-making by storing numerical facts within the context of associated dimensions.   The fact table is a central table in the data schemas and the core component of dimensional modeling in data warehousing.   But what exactly makes up a fact table? 1.  Foreign Keys: These are references to dimension tables that provide context in the fact table. 2.  Measures: The facts or numerical values that we want to analyze.   Types of Fact Tables?   1.  Transactional fact tables: are designed to capture individual business events or transactions. These tables are particularly useful for analyzing customer behavior, sales patterns, and operational efficiency.   2.  Periodic Snapshot Fact Tables: They are used to store information about measurements over regular time intervals, like days, weeks, months, etc. These tables are particularly useful for monitoring performance, identifying trends, and measuring progress.   3.  Accumulating Snapshot Fact Tables: Accumulating snapshot fact tables are designed to track the stages of a business process or workflow. These tables provide valuable insights into process efficiency, identifying bottlenecks, and optimizing operations.   4.  Factless Fact Tables: A factless fact table serves as a bridge between dimensions, capturing events or associations without numerical measures. The purpose of a factless fact table is to track and analyze the presence or absence of certain events or combinations of events. It enables the identification of patterns, trends, and relationships based on the occurrences or non-occurrences of specific events across different dimensions.   Ultimately, The choice of fact table type depends on the specific use case, the level of detail needed, and the analysis objectives. I highly encourage you to go deeper in this important topic, here are some resourse that can help https://lnkd.in/dm4zB6hk. https://lnkd.in/dnBK2yzF https://lnkd.in/dRf4Ci6E https://lnkd.in/d7RZPgQy #Datawarehousing #DataScience #DataAnalysis #BusinessIntelligence

📢 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗧𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 👉 "The Chief Data Officer (CDO) or the 'Head of the Data Team' is one of the most challenging jobs because is more of a 'political' than a technical role. It requires the ideal candidate to be able to throw and catch curved balls almost all the time, and one must be able to play ball with all the parties having an interest in data (aka stakeholders). It’s a full-time job that requires the combination of management and technical skillsets, and both are important. The focus will change occasionally in one direction more than in the other, with considerable fluctuations." 👇 Read further at sql-troubles.blogspot.com: https://lnkd.in/dADW7bsY (⬅️link to blogpost) 🔗 #datamanagement #complexity #management #blogpost #600words #decisionmaking #failure #dynamics #strategy #stakeholders

If it came down to two Data Analyst candidates for one opening: Candidate A: - 9/10 technically - 8/10 domain knowledge - 3/10 curiosity - 5/10 communication Candidate B: - 6/10 technically - 3/10 domain knowledge - 9/10 curiosity - 7/10 communication ....I'm taking B every single time. My reasons: 1. Curiosity and communication are superpowers for analytics. 2. Technical skills and domain knowledge can be taught. And personally, I like teaching and training analysts. Who would you take? #data #analytics

As a data analyst, there are several Excel functions that are commonly used for data analysis, manipulation, and visualization. Here are some key functions: 🔹 SUM: Adds up a range of numbers. =SUM(A1:A10) 🔹 AVERAGE: Calculates the average of a group of numbers. =AVERAGE(B1:B10) 🔹 COUNT: Counts the number of cells that contain numbers in a range. =COUNT(C1:C10) 🔹 COUNTA: Counts the number of non-empty cells in a range. =COUNTA(D1:D10) 🔹 IF: Returns one value if a condition is true and another value if it's false. =IF(E1 > 100, "Above 100", "100 or Below") 🔹 VLOOKUP: Looks for a value in the first column of a table and returns a value in the same row from another column. =VLOOKUP(F2, $A$1:$D$10, 3, FALSE) 🔹 HLOOKUP: Searches for a value in the top row of a table and returns a value in the same column from another row. =HLOOKUP(G2, $A$1:$D$10, 2, FALSE) 🔹 INDEX: Returns the value of a cell in a specified row and column of a range. =INDEX(H1:H10, 5) 🔹 MATCH: Searches for a value in a range and returns the relative position of that item. =MATCH("apple", A1:A10, 0) 🔹 SUMIF: Adds up values that meet a specified condition. =SUMIF(I1:I10, ">50") 🔹 SUMIFS: Adds up values based on multiple criteria. =SUMIFS(J1:J10, K1:K10, ">100", L1:L10, "Approved") 🔹 COUNTIF: Counts the number of cells that meet a specified condition. =COUNTIF(M1:M10, "Yes") 🔹 COUNTIFS: Counts the number of cells that meet multiple criteria. =COUNTIFS(N1:N10, ">0", O1:O10, "Active") 🔹 TEXT: Formats a number and converts it to text. =TEXT(P1, "0.00%") 🔹 CONCATENATE (or CONCAT): Combines multiple text strings into one. =CONCATENATE(Q1, " ", Q2) =CONCAT(Q1, " ", Q2) 🔹 LEFT, MID, RIGHT: Extracts a specified number of characters from a text string. =LEFT(R1, 3) =MID(S1, 2, 4) =RIGHT(T1, 5) 🔹 TRIM: Removes extra spaces from a text string. =TRIM(U1) 🔹 LEN: Returns the length of a text string. =LEN(V1) 🔹 ROUND, ROUNDUP, ROUNDDOWN: Rounds a number to a specified number of digits. =ROUND(W1, 2) =ROUNDUP(X1, 0) =ROUNDDOWN(Y1, 1) 🔹 PivotTables: Not a function but a powerful feature for summarizing and analyzing data. These functions and tools are essential for data analysis tasks like cleaning data, calculating metrics, and generating insights from datasets in Excel. Don't miss out on mastering Excel and more! Join our live-running bootcamp, Data Analyst 3.0 With Azure, AI & Co-Pilot. Enroll now before it's too late. 🔗 Enrollment Link - https://lnkd.in/dvd94VqU Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Shubhankit Sirvaiya Sahil Choudhary Aman Kumar Rahul Shukla

I completely agree with Drew Mooney! As analysts, we need to have the curiosity we had when we were small children - gathering all the information we can to learn about the world around us, asking "Why?", "How?", "What about?", and lots more "Why?". Asking questions and actively listening to understand the responses are critical analyst skills.

As a Data Engineer do we need to understand the businesses or is it just the work of a Data Analyst? 📍Yes, Even as a Data Engineer we have to understand the business to a certain level. This is because the Data Architecture of the project is greatly influenced by the business models. If we are aware of how the business works then only we will be able to understand the data which will help us design better data models, improve the data quality and ultimately provide the best possible and optimised data solutions. Now, the context to which I have to understand the business and a data analyst has to understand the business are quite different. For instance, As a data engineer I will focus more on understanding how the business operates, functions, how the data is getting generated at various points and how the associated workflow looks like. Whereas a data analyst will dive more deep into the business to understand how the GP-PSF is being calculated, what's the logic associated with STR calculation, etc. #dataengineering #dataanalytics #data

In the last 8 years, I've worked in 5 different Data Analyst roles at 5 large publicly listed companies (mostly American Fortune 500 firms), the mixture of work has always been as follows: > Creation of Dashboards / Reports (20%) > Deep Dive Analysis and Presentation of Findings in meetings (25%) > Regular meetings with colleagues, on virtual calls or face to face (40%) > Fixing data issues/solving data quality problems (10%) > Supporting on data migration projects / new system migrations (5%) The irony here, is that I spent very little time doing coding or building pretty dashboards, most of my work was in dealing with business processes and strategic business issues. So remember, all that SQL/Python you are learning don't expect to use it everyday. Its not reality. #dataanalyst #data #dataquality #analytics #datascience #datascientist #datacareers #datatips #careeeradvice

🔍 What Does a Data Analyst & Reporting Professional Do? 🔍 In the age of data-driven decision-making, the role of a data analyst and reporting professional is crucial. Here's a snapshot of their key responsibilities: Data Collection and Cleaning: Gathering accurate data from diverse sources and ensuring its integrity. Data Analysis: Applying statistical methods to uncover trends, patterns, and insights. Reporting: Creating clear, actionable reports and visualizations—think dashboards, charts, and graphs. Data Interpretation: Turning analysis into insights and recommendations for stakeholders. Collaboration: Partnering with teams across marketing, finance, operations, and more to address their data needs. Tools and Software: Leveraging tools like Excel, SQL, Python, R, Tableau, and Power BI.Data Governance: Ensuring data privacy, security, and quality through strict governance policies. Problem Solving: Tackling business challenges with data-driven approaches. Continuous Learning: Staying ahead of the curve with the latest trends and technologies in data analysis. Data analysts play a vital role in helping organizations optimize operations, make informed decisions, and drive strategic initiatives. Their work transforms raw data into valuable insights that fuel business growth. #DataAnalysis #BusinessIntelligence #DataDriven #DataVisualization #DataScience #Analytics #BigData #DataGovernance