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Unlock the Power of Data Analysis! 🔓💡 Hey fellow learners! 👋 Are you curious about data analysis and its applications? 🤔 I'm excited to share with you the benefits of learning data analysis and where it can be applied! 📈 Why Learn Data Analysis? 🤔 Data analysis is a highly sought-after skill in today's data-driven world. 💼 It can help you make informed decisions and drive business growth. 🚀 It can be applied in various industries and domains, and is a valuable skill for anyone looking to make data-driven decisions. 📊 Statistics: The Foundation of Data Analysis 📊 Statistics is a crucial aspect of data analysis. It helps you to: - Collect and analyze data 📈 - Identify patterns and trends 🔍 - Make predictions and informed decisions 📊 Essential Tools for Data Analysis 🛠️ Microsoft Excel: Excel is a widely used tool for data analysis. It offers a range of functions, including data visualization, pivot tables, and macros. 📊 Google Sheets: Google Sheets is a popular alternative to Excel, offering real-time collaboration and automatic saving. 📊 SQL: SQL (Structured Query Language) is a must-know for any data analyst. It allows you to extract, manipulate, and analyze data from databases. 💻 Tableau or Power BI: Choose one that interests you! 🤔 Both Tableau and Power BI are popular data visualization tools that help you create interactive and informative dashboards. 📊 Python or R: Choose one that interests you! 🤔 Both Python and R are widely used programming languages for data analysis. They offer a range of libraries and tools for data manipulation, visualization, and machine learning. 🤖 AI and Machine Learning: Take your data analysis skills to the next level by learning AI and machine learning concepts, such as predictive modeling, natural language processing, and computer vision. 🤖 And many more tools and technologies! 🚀 Where Data Analysis Can Be Applied 🌎 Data analysis can be applied in various industries, including: - Finance 💸 - Healthcare 🏥 - Marketing 📈 - Sports 🏆 - E-commerce 🛍️ - Social Media 📱 - Education 📚 - Research 🔬 - And many more! 🌈 Get Started with Data Analysis! 🎉 Learn the basics of data analysis, including statistics, data visualization, and SQL, to gain a solid understanding of the fundamentals. 📚 Apply your knowledge through real-world projects and case studies to deepen your understanding and develop practical skills. 📊 Continuously learn and improve by exploring new tools, technologies, and techniques, and staying up-to-date with industry trends and best practices. 🚀 Let's Connect! 🤝 I'd love to hear about your experiences with data analysis! What inspired you to learn data analysis? Share your stories, and let's learn from each other! 💬 #DataAnalysis #DataScience #CareerGrowth #DataEnthusiasts #Analytics #DataVisualization #SQL #Excel #GoogleSheets #Tableau #PowerBI #Python #R #MachineLearning #ArtificialIntelligence #BusinessIntelligence

Few steps on how to learn Data analytics Learning data analytics involves several key steps: 1. Understand the Basics: Start with the fundamental concepts of data analytics, including data types, statistical measures, and data visualization. 2. Learn Key Tools: Familiarize yourself with essential tools and software such as Microsoft Excel, SQL for database management, and data visualization tools like Tableau or Power BI. 3. Programming Skills: Gain proficiency in programming languages commonly used in data analytics, such as Python or R. These languages are powerful for data manipulation, analysis, and automation. 4. Statistics and Mathematics: Strengthen your understanding of statistical methods and mathematical concepts as they are crucial for analyzing data and making inferences. 5. Hands-On Practice: Work on real-world datasets to practice your skills. Sites like Kaggle offer datasets and competitions that can help you apply what you’ve learned. 6. Online Courses and Certifications: Enroll in online courses from platforms like Coursera, edX, or Udacity. Many offer specialized certifications in data analytics. 7. Build Projects: Create your own projects or contribute to open-source projects to build a portfolio that showcases your skills. 8. Stay Updated: Data analytics is a rapidly evolving field. Follow industry trends, read relevant blogs, and participate in forums or meetups to stay informed. Starting with these steps will give you a strong foundation in data analytics and help you develop the skills needed for a career in the field. Your inputs is welcomed.

Data Analytics and business intelligence 30 days learning #Challenge what I have learnt in #Day1. Topic 1. What is analysis? To find something Types: 1. Mathematical analysis 2. Statistical analysis 3. Economics analysis 4. Data analysis What is analytics? Analysis with the help of a computer is called analytics. Types: 1. Mathematical analytics 2. Statistical analytics 3. Economics analytics 4. Data analytics The CS in word analytics shows the use of computer science in analysis. Topic 2 Pillars or 5 blocks of data analytics and business intelligence. 1. Mathematics 2. Statistics 3. Computer science 4. Economics 5. Decision science Arithmetic vs mathematics vs statistics vs computer science vs economics analysis. Arithmetic gives only one directional dimension it has no negative value. Mathematics gives two dimensional values. Statistics add uncertainty in analysis so it gives three dimensional analysis So it gives more accurate future prediction analysis. Computer science analyses on the basis of data It is four dimensional analysis it gives more accurate analysis as compare to other. Decision science we chose which analysis is required for given data if there is future prediction and uncertainty we use statistics otherwise mathematics we can use and computer science analysis gives more accurate analysis for big data. Tools and technologies that used in data analytics. 1. Excel 2. Power bi 3. Tableau 4. R 5. Python 6. Sql Data analytics and business intelligence. In simple words, making decisions on the basis of data and improve and get maximum business profit. Topic 3 What is data? Facts and figures with context, reason or meaning called data. With context facts and figures are not data. Types of Data 1. Quantitative data. Numbers data is called Quantitative data. a) Discrete data: numbers of students in class, numbers of workers in company etc. b) Continuous data: speed of car , height of a children 2. Qualitative data Non numbers data is called Qualitative data. a) Nominal data: gender( male or femal) hair (blonde or brown )numbers not used for order. b) Ordinal data: laters A,B, C Numbers fist second third. Numbers used for order. Note: Data analytics works only on data not on figures and facts it means context is must for data analytics. Codification Converting qualitative data into qualitative data is called codification for example in context Gender there is male and female assigned 1 and 2 respectively.1 for male and 2 for female. #dataAnalytics

Who can learn Data Analytics? Benefits of Data Analytics for different age groups 1. Students (20s-30s) 👉Enhance career prospects with in-demand skills 👉Gain a competitive edge in the job market 👉Develop problem-solving and critical thinking skills 👉Build a strong foundation for future career growth 2. Working Professionals (30s-50s) 👉Upskill and reskill to stay relevant in the industry 👉Transition to new roles or industries with ease 👉Enhance current job performance and productivity 👉Increase earning potential with new skills 3. Career changers (40s-60s): 👉Pursue a new career path with a strong foundation in data analytics 👉Leverage transferable skills and experience 👉Stay relevant in the job market with in-demand skills 👉Enjoy a fulfilling second career 4. Retirees (60s+): 👉Engage in lifelong learning and mental stimulation 👉Explore new interests and hobbies 👉Stay connected with the industry and community 👉Share knowledge and experience with others Key skills to learn: 1. *Data analysis and visualization*: - Extract insights from data - Communicate findings effectively 2. *Programming languages*: - Python, R, SQL, and others - Write efficient and effective code 3. *Data manipulation and statistics*: - Work with datasets and statistical concepts - Apply data analysis techniques 4. *Communication and storytelling*: - Present findings and insights effectively - Tell compelling stories with data Industry Applications:(Where you can work as a Data Analyst) 1. *Healthcare*: - Analyze patient data and outcomes - Improve healthcare services and policies 2. *Finance*: - Analyze market trends and risks - Make informed investment decisions 3. *Marketing*: - Analyze customer behavior and preferences - Develop targeted marketing campaigns 4. *Sports*: - Analyze player and team performance - Inform coaching decisions and strategy 5. *Environmental science*: - Analyze climate and environmental data - Inform policy and conservation efforts School Do you want to pivot into Data Analytics? Join our October Cohort. It is 5 weeks program (Weekend class) The training starts on Saturday 5th of October, 2024 Physical and virtual class available call us on +2348062468296 to register Choose Lead-Leap Data Analytics School

Your Journey to Becoming a Data Analyst Are you ready to embark on your data analytics journey? Here’s a simplified guide to help you get started and stay on course. 1. Overcome Tool Anxiety   Start by embracing the tools. They may seem daunting at first, but they are essential for analyzing data effectively. 2. Start with Excel   Excel is your friend. It’s user-friendly, widely used, and perfect for mastering basic data manipulation and analysis. 3. Hands-On Projects   Dive into 5-6 Excel projects. Practical experience is key to understanding and applying what you learn. 4. Move Beyond Tutorials   Transition from tutorials to real-world projects. Action and application will deepen your understanding faster than passive learning. 5. Learn SQL   SQL is a must-have skill. It’s the language of databases and crucial for querying and analyzing data efficiently. 6. Practice, Practice, Practice   Solve 100-150 SQL problems. Start simple and gradually tackle more complex queries to hone your skills. 7. Build Analytical Thinking   Regular practice sharpens your analytical abilities. Break down problems, analyze data critically, and derive insights effectively. 8. Case Studies   Work on 3 case studies. They simulate real-world scenarios and prepare you for the challenges you’ll face in the field. 9. Explore Power BI   Master Power BI for visualizing data. Complete 5 projects using SQL or Excel data to create insightful reports and dashboards. 10. Embrace Challenges   Challenge yourself with unguided projects. Solve problems independently and communicate your solutions effectively. 11. Prepare for Interviews   Use your case study experience to excel in interviews. Showcase your problem-solving skills and analytical mindset. 12. Polish Your Resume   Highlight your projects and skills effectively. Tailor your resume to demonstrate how you’ve applied your data analytics knowledge. 13. Leverage Free Resources   Take advantage of YouTube tutorials and guides. There’s a wealth of free information to supplement your learning journey. 14. Stay Motivated   Keep your focus on practical applications and real-world projects. Don’t get bogged down in endless theory—keep learning and applying. Follow these steps diligently, and you’ll build confidence, expertise, and a solid foundation in data analytics. Happy learning! Happy sunday

ON MY JOURNEY TO BECOMING A DATA SCIENTIST. Here's the preview of my PowerBI for business Analytics Capstone Project: After my thorough study which was tutored by my amiable tutor and mentor Adeyanju Pinheiro-Aina a wonder woman that's doing Nigeria proud. and supervised by Temitope Babatunde who made it so simple and understandable for I and my colleagues. Basic Learning Outcomes: - Introduction to Power BI -Data Preparation and Transformation using Query Editor, How to Format Data, Remove Null, Pivot and Unpivot Column -Data Models & DAX Power BI language (creating measures) - Data Visualisation: Overview & Creation of Dashboard, How to share dashboard and publish to web, Data Storytelling - Understanding Chart, creating visuals, formatting visuals, using slicers, filters, and importing of visuals Power BI for Business Analytics Capstone Project Project Objectives: To create a strategic dashboard for BG Group of Companies Sales Data that would help their directors make inform decisions on way forward in their business. Preview: I carefully used 6 strategic KPIs that show golden business growth insight hidden in their data. 1. Total Sales: 2. Sales By Territory: showing the smart and under performing territories 3. Annual Sales Growth by territory: This shows the trends in competition amongst the territories 4. Sales vs Target: This shows the gap between the sales and target by territory, which would definitely help directors know their next form of business strategy to hit their target. 5. Top 10 Stores with Highest Revenue: This would help the directors know where to put their focus on and what type of moves would they take to help these performing stores motivate the least performing stores. 6. Revenue per store: This was a measure of total sales divided by distinct count of store. This is the average revenue per store. Slicers: 1. Significance of Competition on their revenue 2. Chain It has been a fulfilling journey which shows my organisation, determination and resourcefulness at work. Thanks to Utiva for this amazing journey and opportunity. PowerBI has amazing opportunity to offload. My SQL and Python Capstone Project is coming. Thanks 🙏

Today I am sharing a small project on Data Analysis for a Bussiness Analyst how to approach a Project. Practical Example: A Small Data Analysis Project Let’s walk through a student performance analysis project in education: Scenario: You are given a dataset of students' scores in various subjects, attendance records, and demographic details. The goal is to analyze the data to identify factors affecting performance and suggest improvements. Step 1: Define Objectives Understand how attendance impacts performance. Identify top and struggling students. Suggest strategies to improve overall scores. Step 2: Data Collection The dataset might look like this: | Student ID | Math Score | English Score | Science Score | Attendance (%) | Gender | |------------|------------|---------------|---------------|----------------|--------| | 1 | 78 | 85 | 92 | 95% | Male | | 2 | 65 | 72 | 70 | 80% | Female | Step 3: Data Cleaning Remove duplicate records or incorrect entries (e.g., attendance over 100%). Handle missing data by filling averages or dropping incomplete rows. Step 4: Data Analysis Descriptive Analysis: Calculate average scores for each subject. Check attendance averages by gender. Correlation Analysis: Examine if high attendance correlates with better scores. Visualization: Create bar charts for subject averages. Plot a scatterplot of attendance vs. overall performance. Step 5: Insights & Recommendations Insight: Students with attendance >90% scored 15% higher on average. Recommendation: Implement an attendance incentive program. Insight: Female students showed lower Math scores on average. Recommendation: Offer additional Math support workshops. Step 6: Tools Used Excel: Data cleaning and simple charts. Power BI/Tableau: Interactive dashboards for visualization. Python: Use Pandas for cleaning and Matplotlib for graphs.

--- Day 6: How to Kick-Start Your Journey in Business Analytics 🚀 Ready to dive into the world of business analytics but not sure where to begin? Don’t worry—I’ve got you covered! Let’s break it into simple steps: 1️⃣ Understand the Basics Start with the fundamentals of analytics—terms like KPIs, dashboards, and datasets. There are plenty of free resources online (YouTube, blogs, or even LinkedIn posts like this!). 2️⃣ Master Excel Excel is your best friend in analytics. Learn how to work with formulas, pivot tables, and charts. It’s the easiest way to get hands-on experience with data. 3️⃣ Learn Data Visualization Start with tools like Tableau or Power BI to create visuals that make data insights clear and actionable. Visualization is key to storytelling with data! 4️⃣ Get Comfortable with SQL SQL helps you retrieve, filter, and manipulate data from databases. It’s an essential skill for anyone in analytics. Pro tip: Practice with free datasets online! 5️⃣ Explore Advanced Tools If you’re serious about analytics, learn Python or R for more advanced tasks like predictive modeling or machine learning. Start with beginner-friendly courses to build your confidence. 6️⃣ Work on Real Projects Practice makes perfect! Start with small projects, like analyzing sales data or creating a dashboard for a personal project. This will boost your skills and portfolio. 7️⃣ Stay Curious Analytics is a fast-evolving field. Follow industry blogs, connect with experts on LinkedIn, and keep learning new tools and techniques. 🔑 Pro Tip: Certifications can give your resume a boost. Platforms like Coursera and Google offer great analytics certifications to validate your skills. Tomorrow, we’ll discuss how to create impactful dashboards that communicate insights effectively. Stay tuned and keep learning! 😊

Domain Knowledge for Analysis In my previous post, I gave some steps to analyzing a Power BI project and the first point was Understanding the dataset. Most beginners (especially those who are not already in jobs trying to move to the analytics teams in their organization; yeah! because these already have it figured out) acquiring data analytics skills missed these hints and find themselves in a difficult state embarking on a project for practice or portfolio building. Hence, I am taking the time to address the topic. What is Domain Knowledge? Domain knowledge is adequate knowledge in a specific field. It is the thorough knowledge of a specific industry. As defined by indeed.com, A domain knowledge is an area of expertise that a person has in a particular industry and each industry has one or multiple domains in which a professional can specialize. Next is at what level are we satisfied we have domain Knowledge? Satisfaction with domain knowledge varies but most commonly, we have; Foundational knowledge: this has to do with grasping basic concepts, fundamental principles, historical concepts and understanding jargon. Intermediate Proficiency: this has to do with applying knowledge to practicals.  Advanced level: this has to do with mastering and contributing to the industry.  Not forgetting the place of continuous learning, industries evolved, so keeping up with the developments, adapting to new technologies and methods will require continuous learning. How useful is it to a Data Analyst? A data analyst defines metrics for business rules, measures KPIs and performances. It goes to say that domain knowledge is a core requirement for a data analyst in every step of a project. From gathering data to defining the problem, to cleaning the data, to modeling and measuring performance in visualizations. The lack of domain knowledge makes it difficult to apply the right methods as well as to judge their performance properly. How do we get domain knowledge alongside our tech skills? In today’s world, one need not go to a traditional educational center to gain domain knowledge. Most of what it takes is curiosity, willingness to learn and be involved. We can acquire domain knowledge through research, one on one consultations, taking courses and attending industry events. I am willing to engage more in these conversations and maybe help point to more specific directions.

📊 *Unlock the Power of Data with These Analysis Techniques!* 📊 *What is Data Analysis?* Data analysis is the process of collecting, cleaning, and interpreting data to extract valuable insights, spot trends, and make data-driven decisions. From business growth strategies to healthcare innovations, data analysis transforms raw numbers into actionable knowledge. *Types of Data Analysis Every Analyst Should Know* *1. Descriptive Analysis –* Understand past events with summaries, trends, and historical insights. *2. Diagnostic Analysis –* Discover the “why” behind your data, identifying causes and correlations. *3. Predictive Analysis –* Look to the future with forecasting techniques, using patterns to make predictions. *4. Prescriptive Analysis* Find the best actions to take using simulation and optimization. *5. Exploratory Data* Analysis (EDA) – Dive deep into data without assumptions to uncover new insights and opportunities. *Ready to Level Up?* These data analysis courses will equip you with hands-on skills to master these techniques, from basic descriptive methods to advanced predictive models. No matter your level, there’s something here to empower your career! 📈 *7 Completely FREE Data Analytics Online* 1. Intro to Data Analysis- Google 🔗 https://lnkd.in/dtiJjsdv 2. Learn SQL Basics for Data Science Specialization. 🔗 https://lnkd.in/d2WZDU6B 3. Bayesian Statistics: From Concept to Data Analysis– 🔗 https://lnkd.in/dRgaUhvv 4. Data Analysis with R Specialization 🔗 https://lnkd.in/dM5HpBBR 5. Exploratory Data Analysis in Python– 🔗 https://lnkd.in/djDFbtrt 6. Data Analysis and Visualization Foundations Specialization 🔗 https://lnkd.in/d5PUKGuZ 7. Python for Data Analysis- 🔗 https://lnkd.in/dgFz-SVw Happy Learning!