#100daysofdataanalysis risultati di ricerca

Day 3/100 – #100DaysOfDataAnalysis Had a free day, so I went deeper into Excel! Learned about: - Freezing rows & columns - Name & reordering worksheets - Linking workbooks - Working across multiple sheets - Handy keyboard shortcuts & time/date tricks - Feeling really fulfilled😊

Day 2/100 – #100DaysOfDataAnalysis Practiced some of Excel formulas & functions( SUM, AVERAGE, MAX, MIN, COUNT ) today and learned how to filter, format, sort, and create basic charts.

DAY 1/100 - #100DaysOfDataAnalysis Started with excel basics using coursera & YouTube tutorials. Learned about 1. Difference between workbook & worksheet 2. Rows, columns, & references 3. Name box, formula box, ribbons & groups 4. Entering, copying, and saving data

I’m officially starting my #100DaysOfDataAnalysis journey! I’ve been learning data analysis for a while, but I haven’t been consistent. So I’m setting this 100-day challenge to stay focused, learn every day, and track my progress here. This will be a thread 🧵👇

Day 7✅ — just showing up and learning Data Analysis #100daysofdataanalysis Just like I mentioned yesterday, I’m working on a car database where I had to download a car inventory text file online. That part actually took me a while, but I eventually found a good one.

Day 6✅ — just showing up and learning Data Analysis #100daysofdataanalysis Today’s been such a long, busy day, and honestly I was too tired to take much class class. But consistency matters, so I still went through my next project — Car Database.

Day 5✅ — just showing up and learning Data Analysis #100daysofdataanalysis Today, I worked on a SALES REPORT and learnt Text to Columns, IF and SUMIF formulas, sorting, filtering, Pivot Tables, and pie charts. I spent more time learning than usual, but it was worth it

Day 4✅ — just showing up and learning Data Analysis #100daysofdataanalysis Today’s assessment was about creating a Decision Maker to choose a job based on pay, job market, enjoyment, talent, and schooling. I enjoyed it because it felt personal

23days ago I started #100DaysOfDataAnalysis and I intended to publicly journal my learning journey here daily but while I've being doing the learning I've not been sharing my journey. So, I've decided to start this journey again!

Day 18 #100DaysOfDataAnalysis Then comes evaluating the model, how the model has performed. We find out how the models are performing well or not. And finally we deploy. It's always better to learn all the different steps.

Day 17 #100DaysofDataAnalysis Then comes Modelling as next in steps to obtain a data analytics solution. Modelling simply means building a model. It could be a predictive model that does some predictions. Or a descriptive model that provides insights.

Day 16 #100DaysOfDataAnalysis Data understanding simply means what is the data about? How many columns, features; rows do we have, etc.? After that Data Preparation is very important. It is where we convert unstructured data to structured data that can easily be analysed.

Day 15 #100DaysOfDataAnalysis Steps to obtain solutions to our data analysis problem. The first is always understanding the problem. How do we get the business understanding in the first place? Example - reading more blogs. After business understanding comes data understanding.

Day 14 #100DaysOfDataAnalysis Two types of methods that are used in analysing data are descriptive statistics and inferential statistics. Certain topics under descriptive statistics, to be studied in the future, are Measure of Central tendencies and Measure of Veriability.

Day 13 #100DaysOfDataAnalysis In practice, statistics is the idea that we can learn the properties of large sets of objects or events by studying the characteristics of a smaller number of similar objects or events as sample because in many cases gathering data is impossible.

Day 12 #100DaysOfDataAnalysis We learned about types of data. Qualitative Vs Quantitative data. That is Categorical and Numerical. Under Qualitative there's two major ones - Nominal and Ordinal. Two major types of Quantitative data are discrete and continuous.

Hi Hi #datafam Day2/100 of #100DaysOfDataAnalysis Learnt the difference between Data science and Data Analytics Key takeaway: Consistency matters

Hi Hi #datafam Starting my #100DaysOfDataAnalysis today and will be completing my 100th day on my birthday🥰So cheers to 99days of consistency ahead. #datafam #DataAnalytics

Day 11 #100DaysOfDataAnalysis And to the subject matter proper: Data Analytics is simply analysing data and gaining some insights from it. Data Analysis helps businesses optimise their performances, perform more efficiently, maximise profit or make some strategic decisions.

I am pushing though: I am at day 10, I am supposed to be at day 25 of #100DaysOfDataAnalysis. If I believe that I can make if I put my back into it.

A spreadsheet isn’t just rows and columns. It’s a storybook of: Sales trends Spending habits Growth signals Good analysts don’t just see numbers. They uncover stories in the data. #100DaysOfDataAnalysis #DataDriven

Day 4✅ — just showing up and learning Data Analysis #100daysofdataanalysis Today’s assessment was about creating a Decision Maker to choose a job based on pay, job market, enjoyment, talent, and schooling. I enjoyed it because it felt personal

Day 5✅ — just showing up and learning Data Analysis #100daysofdataanalysis Today, I worked on a SALES REPORT and learnt Text to Columns, IF and SUMIF formulas, sorting, filtering, Pivot Tables, and pie charts. I spent more time learning than usual, but it was worth it

Day 2✅ — just showing up and learning Data Analysis #100daysofdataanalysis Continued from where I stopped in Excel~ yesterday’s payroll assignment. I learnt how and when to use the IF formula, and also explored relative vs. absolute cell referencing, which I applied in my work

A brief intro to data cleaning #100daysofdataanalysis #datacleaning2025 #pythonlearning #PYTHON

DATA CLEANING Today, I received business files to clean. As a business analyst aiming to a business, option is elicitation. However, my data analysis mindset tells me to simply analyze, clean, and them back. #100daysofdataanalysis #datacleaning2025 #pythonlearning #PYTHON

Day 15 of Data Analysis Challenge. Learning doesn't stop because there isn't laptop to practice. Keep the momentum high #kehindeadekola #100daysofdataanalysis

Data ≠ Decisions. The real challenge isn’t collecting or analyzing data. It’s turning insights into clear actions. Great analysts don’t just crunch numbers. They bridge the gap between data and decisions. #100DaysOfDataAnalysis #DataDriven #DecisionMaking

#100DaysOfDataAnalysis SPSS done today. laptop nearly shame me but Mercy said No. Learnt Frequency distribution.

Been busy with formulas and functions of data analysis, combine with work the stress no be here but hey who's counting. #100daysofdataanalysis #datacleaning2025 #pythonlearning #PYTHON

#100daysofdataanalysis My Tutor @Nsukka_okpa #SPSS #Day1

Day 15: Finesse SQL capstone project and an Excel project in pivot tables and charts. The dashboard for the excel file is the attached picture. #100DaysofDataAnalysis #100DaysOfCode #DataAnalytics #Excelsior #DataManagement #tecktwitter #WomeninTech #WomenInSTEM #WomenWhoCode

What is data cleaning? Encoding Categorical Variables: Convert categorical data into numerical format using techniques like one-hot encoding or label encoding is also in alignment with data cleaning. #100daysofdataanalysis #datacleaning2025 #pythonlearning #PYTHON

Day 100:Today marks the wrap-up of my 100 days of Data Analytics challenge. Notwithstanding the time off in between, I still actualize my goal of becoming a certified Google Data Analyst to the Glory of God🙏🏻🙏🏻🙏🏻 #100daysofDataAnalysis #100daysofcodechallenge #DataScience #Google

#100DaysOfDataAnalysis #100DaysOfMachineLearning Day3 🔥

#100DaysOfDataAnalysis #100DaysOfMachineLearning Day1🔥

#100DaysOfDataAnalysis

#100DaysOfDataAnalysis

#100DaysOfDataAnalysis #MachineLearning Day 1🔥

#100DaysOfDataAnalysis #100DaysOfMachineLearning Day 4🔥