Aminat Olaoye’s Post

  Getting a Job in Data: It’s Simple, But Not Easy Securing a role in the data field isn’t complicated, but it does require effort and persistence. Here are five essential steps to help you on your journey: Master SQL: SQL is the foundation of data analysis. Invest time in understanding its nuances and become proficient in querying databases. Learn a BI Tool: Familiarize yourself with a business intelligence tool like Tableau, Power BI, or Looker. These tools are invaluable for visualizing data and communicating insights effectively. Create Projects: Build your portfolio by working on real or hypothetical projects. This will not only showcase your skills but also help you apply what you’ve learned in practical scenarios. Practice Interviewing: Prepare for interviews by practicing common data-related questions and scenarios. Mock interviews can boost your confidence and improve your responses. Network Like Crazy: Connect with professionals in the field through LinkedIn, local meetups, and industry events. Networking can open doors to job opportunities and valuable mentorship. Remember, while the steps are straightforward, the journey can be challenging. Dedicate a little effort each day to your goals. It will take time, energy, and commitment, but in the end, it will be worth it. Stay focused, and keep pushing forward! Data AnalyticsData AnalystData EngineeringPower BI VisualizationTableauSQLExcelBI analystTableauPython

Business Intelligence (BI) and Data Analytics are related fields but differ in many aspects. Here is the difference between them: Business Intelligence (BI): Purpose: Business Intelligence aims to support strategic and tactical decision-making by providing historical reports and insights based on data. Outputs: It provides static reports and dashboards that show historical trends and key performance indicators (KPIs). Tools: It relies on tools like Tableau, Power BI, and QlikView to analyze and present data in an understandable and readable manner. Focus: It focuses on providing insights into what happened in the past and what is happening currently. Users: It is typically used by executives and department managers to support strategic decisions. Data Analytics: Purpose: Data Analytics aims to examine raw data to extract patterns and trends and generate deeper insights related to the future. Outputs: Data Analytics includes data mining, statistics, and future predictions based on analytical models. Tools: It relies on statistical analysis tools like Python, R, SAS, and SPSS, as well as machine learning and deep learning tools. Focus: It focuses on providing insights into why past events occurred and what might happen in the future based on data. Users: It is used by analysts, data specialists, and researchers to deeply analyze data and prepare detailed reports. The Overlap Between Business Intelligence and Data Analytics: Data as a Common Source: Both rely on collecting and processing data from multiple sources to provide valuable insights. Integration and Complementarity: Data analytics tools can be used within BI systems to enhance the accuracy of forecasts and trend analysis. Common Goal: Ultimately, both Business Intelligence and Data Analytics aim to improve the decision-making process in an organization, despite the different tools and methods used. #Creative_Writer #Writer  #Graphic_Design  #Marketing  #Author  #Copywriter #TRABZON #Web_designer #iphone #Wallpapers #Abdullrazzaq #Cybersecurity #ChatGPT

KPI revealed through my report, data is very important in any business for making an informed decision. This power of data cleaning, data transformation and data visualization can be done through data visualization tools such as powerbi, tableau and ms excel by a data analyst. Aspiring data analyst, keeping learning and building portfolio for greater achievement💪💪

💁🏻♀️Exploring Different Types of Data Analysis in the World of Analytics 🌟 As I continue deepening my journey into data analytics, I’ve been learning about the various methods used to uncover insights and drive decision-making. Here's a quick overview of the four main types of data analysis: 1. Descriptive Analysis 📊 This type helps answer the question, "What happened?" by summarizing past data and identifying patterns. It’s the foundation for understanding trends and performance metrics. 2. Diagnostic Analysis 🔍 This goes a step further to answer "Why did it happen?" It digs deeper into data to uncover the reasons behind outcomes, using techniques like correlation analysis and data mining. 3. Predictive Analysis 🔮 This type uses historical data and algorithms to forecast "What might happen next?" It’s commonly applied in scenarios like sales forecasting, risk management, and customer behavior predictions. 4. Prescriptive Analysis 🧠 The most advanced type, it addresses "What should we do?" by providing actionable recommendations. This form of analysis uses optimization and simulation to guide decision-making for future strategies. Each type of analysis plays a crucial role in driving informed business decisions, and mastering these techniques is key for anyone looking to grow in data analytics. Excited to keep learning and applying these concepts in real-world scenarios! What’s your experience with these types of data analysis? Let’s discuss! 💬 #DataAnalytics #DescriptiveAnalytics #PredictiveAnalytics #DiagnosticAnalytics #PrescriptiveAnalytics #LearningJourney Aditi Gupta TechTip24 Codebasics Dhaval Patel Hemanand Vadivel Rishabh Mishra Himanshu Bansal The iScale Microsoft Accenture in India Google

I am glad to share that, I have completed the Data Analyst Course Certification from ExcelR Raising Excellence Academy . ▪️As a certified data analyst, I have acquired a comprehensive skill set in data analysis, visualization, and interpretation. I am now well-versed in utilizing advanced Excel functions, statistical techniques, and data visualization tools to drive meaningful insights and drive informed decision-making. ▪️Throughout the Certification program, I had the opportunity to work on real-world projects, gaining hands-on experience in manipulating large datasets, conducting exploratory data analysis, and presenting actionable recommendations to stakeholders. ▪️My journey in data analytics has equipped me with the ability to uncover hidden patterns, trends, and correlations within data, ultimately contributing to data-driven strategies. ✔️Skills Acquired from the training: ·      Microsoft Excel for data cleaning, analysis, and visualization. ·      Data visualization tools, such as Power BI & Tableau. ·      MySQL ·      Python & R ·      Chat GPT

5 Ways Data Visualization Will Transform How You Understand Your Data Some time ago, I was working with a dataset that seemed overwhelming at first. Rows and rows of numbers stared back at me, and I knew there had to be a better way to communicate the insights hidden within. That’s when I turned to data visualization—and everything changed. These are five ways it transformed how I understood my data: 1. Clarity in Complexity: By using Pivot Charts in Excel, I could easily spot trends and patterns that were completely hidden in the raw data. Suddenly, what felt complicated became clear. 2. Faster Insights: Instead of spending hours trying to manually analyze data, Conditional Formatting helped me highlight key areas, allowing me to focus on the important details quickly. 3. Improved Engagement: When I shared my findings with others, they were able to grasp the insights instantly because the visualizations made the data more accessible, even to those who weren’t “data people.” 4. Fact-Based Decisions: Visualization guided conversations towards facts, ensuring that the insights were based on the data itself rather than assumptions. 5. Increased Confidence: With visuals guiding me, I felt more confident in my conclusions. I could see risks and opportunities clearly, something that was much harder when dealing with just rows of numbers. If you want to gain a better understanding of your data, data visualization can help you do the same. Start with Excel, where tools like Pivot Charts and Conditional Formatting can simplify your analysis. From there, you can explore other visualization tools like Power BI or Tableau to take things further. The Ambitious Analyst

I am glad to share that, i have successfully completed the Data Analyst Course Certification from ExcelR As a certified data analyst, i have acquired a comprehensive skill set in data analysis, visualization and interpretation. I am now well-versed in utilizing advance excel functions, statistical techniques and data visualization tools to drive meaningful insights Throughout the certification program, i had the opportunity to work on real world projects,  gaining hands-on experience in manipulating large datasets conducting exploratory data analysis,  and presenting actionable recommendations to stakeholders. My journey in data analytics has equipped me with ability to uncover hidden patterns, trends and correlation with data, ultimately contributing to data-driven strategies Skills Acquired from the training 1. Microsoft Excel for data cleaning, analysis and visualization. 2. Data visualization tools such as powerbi and tableau 3. MySQL 4. Python 5. Chat GPT

🔶 I am Glad to share that, I have successfully completed the Data Analyst Course Certification from ExcelR. 🔶 As a certified data analyst, I have acquired a comprehensive skill set in data analysis, visualization and interpretation. I am now well-versed in in utilizing advance Excel functions, statistical techniques, and data visualization tools to drive meaningful insights and drive informed decision-making. 🔶 Throughout the Certification program, I had the opportunity to work on real-world projects, gaining hands-on experience in manipulating large datasets, conducting exploratory data analysis, and presenting actionable recommendations to stakeholders. 🔶 My journey in data analytics has equipped me with the ability to uncover hidden patterns, trends, and corelation within data, ultimately contributing to data-driven strategies. ✅ Skills Acquired from the training: ·      Microsoft Excel for data cleaning, analysis and visualization. ·      Data visualization tools, such as Power BI & Tableau. ·      MySQL ·      Python & R ·      Chat GPT

I am glad to share that, I have completed the Data Analyst Course Certification from ExcelR. 🔶 As a certified data analyst, I have acquired a comprehensive skill set in data analysis, visualization, and interpretation. I am now well-versed in utilizing advanced Excel functions, statistical techniques, and data visualization tools to drive meaningful insights and drive informed decision-making. 🔶 Throughout the Certification program, I had the opportunity to work on real-world projects, gaining hands-on experience in manipulating large datasets, conducting exploratory data analysis, and presenting actionable recommendations to stakeholders. 🔶 My journey in data analytics has equipped me with the ability to uncover hidden patterns, trends, and correlations within data, ultimately contributing to data-driven strategies. ✅ Skills Acquired from the training: ·      Microsoft Excel for data cleaning, analysis, and visualization. ·      Data visualization tools, such as Power BI & Tableau. ·      MySQL ·      Python & R ·      Chat GPT

Here's Part 3 of the phone interview series for data analysts: 𝐃𝐞𝐬𝐜𝐫𝐢𝐛𝐞 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐚 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. 𝐇𝐑: [Your Name], can you describe your process for solving a data analysis problem? [Your Name]: Certainly! When approaching a data analysis problem, I typically follow a structured process that involves several key steps: 1. Understanding the Problem: The first step is to clearly understand the problem at hand. I make sure to define the objectives and identify the key questions that need to be answered. This often involves communicating with stakeholders to ensure we're aligned on the goals. 2. Data Collection: Once the problem is defined, I gather the necessary data. This could involve extracting data from databases, collecting data from various sources, or working with existing datasets. Ensuring data quality is crucial at this stage. 3. Data Cleaning: Data often comes with inconsistencies, missing values, or errors. I spend time cleaning the data to ensure it's accurate and reliable. This step involves handling missing data, removing duplicates, and correcting errors. 4. Exploratory Data Analysis (EDA): After cleaning the data, I perform exploratory data analysis to uncover initial insights and patterns. This involves visualizing the data, calculating summary statistics, and identifying any outliers or trends. 5. Data Modeling: Depending on the problem, I might apply statistical models or machine learning algorithms to analyze the data. This step involves selecting the appropriate model, training it on the data, and evaluating its performance. 6. Interpretation and Presentation: Once the analysis is complete, I interpret the results and draw meaningful conclusions. I create visualizations and reports to present the findings in a clear and concise manner, making sure to tailor the presentation to the audience. 7. Recommendations and Actionable Insights: Finally, I provide recommendations based on the analysis. The goal is to offer actionable insights that can help the stakeholders make informed decisions. 𝐇𝐑: That's a comprehensive process. Can you give me an example of a project where you applied this process? continue....