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MUST ADD these 5 POWER Bl projects to your resume to get hired Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger 📌Customer Churn Analysis 🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input 📌Credit Card Fraud 🔗 https://github.com/sahidul-shaikh/credit-card-fraud- 📌Movie Sales Analysis 🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data 📌Airline Sector 🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline- 📌Financial Data Analysis 🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data- ✅ Free Courses with Certificate: https://t.me/free4unow_backup Simple guide 1. Data Utilization: - Initiate the process by using the provided datasets for a comprehensive analysis. 2. Domain Research: - Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis. 3. Dashboard Blueprint: - Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality. 4. Data Handling: - Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations. 5. Question Formulation: - Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data. 6. Platform Integration: - Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility. 7. LinkedIn Visibility: - Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections. I have curated the best interview resources to crack Power BI Interviews 👇👇 https://topmate.io/analyst/866125 Hope you'll like it Like for more 👍❤️

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Want to become a data analyst? Stage 1 – Excel Stage 2 – SQL + Project Stage 3 – Python (Pandas, NumPy) + Project Stage 4 – Data Visualization (Matplotlib, Seaborn) + Project Stage 5 – Statistics + Project Stage 6 – Machine Learning (Scikit-learn) + Project Stage 7 – Big Data Tools (Hadoop, Spark) + Project 🏆 – DataAnalytics

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Data analytics essential concepts 1. Descriptive Analytics: Descriptive analytics involves analyzing historical data to understand what has happened in the past. It focuses on summarizing and interpreting data to describe patterns, trends, and relationships. 2. Predictive Analytics: Predictive analytics uses statistical algorithms and machine learning techniques to predict future outcomes based on historical data. It helps organizations make informed decisions by forecasting trends and identifying potential risks or opportunities. 3. Prescriptive Analytics: Prescriptive analytics goes beyond predicting outcomes and provides recommendations on what actions to take to achieve a desired outcome. It leverages optimization and simulation techniques to suggest the best course of action. 4. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It helps in understanding complex data sets, identifying trends, and making data-driven decisions. 5. Data Mining: Data mining is the process of discovering patterns, trends, and insights from large datasets using statistical and machine learning techniques. It involves extracting valuable information from data to support decision-making. 6. Machine Learning: Machine learning is a subset of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed. It involves algorithms that improve their performance over time through experience. 7. Natural Language Processing (NLP): Natural Language Processing is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It is used in applications such as sentiment analysis, chatbots, and language translation. 8. Sentiment Analysis: Sentiment analysis is the process of analyzing text data to determine the sentiment or opinion expressed in it. It is commonly used in social media monitoring, customer feedback analysis, and market research. 9. Data Warehousing: Data warehousing involves collecting, storing, and managing large volumes of structured and unstructured data from various sources. It provides a centralized repository for data analysis and reporting. 10. Data Governance: Data governance refers to the overall management of data assets within an organization. It includes policies, processes, and controls to ensure data quality, security, privacy, and compliance with regulations. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

Top 5 skills for DataAnalytics 1. Proficiency in programming languages like Python, R, or SQL. 2. Strong analytical and problem-solving skills. 3. Ability to work with data manipulation and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn. 4. Knowledge of statistical analysis and machine learning techniques. 5. Effective communication and storytelling skills to convey insights from data to stakeholders.

You don't need to know everything about every data tool. Focus on what will help land you your job. For Excel: - IFS (all variations) - XLOOKUP - IMPORTRANGE (in GSheets) - Pivot Tables - Dynamic functions like TODAY() For SQL: - Sum - Group By - Window Functions - CTEs - Joins For Tableau: - Calculated Columns - Sets - Groups - Formatting For Power BI: - Power Query for data transformation - DAX (Data Analysis Expressions) for creating custom calculations - Relationships between tables - Creating interactive and dynamic dashboards - Utilizing slicers and filters effectively

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If you're looking to build a career in Data Analytics but feel unsure about where to start, this post is for you. It's important to know that you don't need to spend money on expensive courses to succeed in this field. Many posts you see on LinkedIn promoting paid courses are often shared by individuals who are either trying to sell their own products or are being compensated to endorse these courses. Through this post, I will share with you everything you need to start your data journey absolutely free. Source

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SAMPLE RESUME TEMPLATE FOR A DATA ANALYST(FRESHER) Creating a resume as a fresher data analyst involves highlighting your education, skills, projects, and any relevant experience you have gained through internships, coursework, or personal projects. Here’s a structured resume template tailored for a fresher in data analysis: [Your Name] [Your Address] [City, State, Zip Code] [Your Email Address] [Your Phone Number] [LinkedIn Profile] [GitHub Profile (if applicable)] Objective:- A motivated and detail-oriented data analyst with a strong foundation in statistics, data manipulation, and visualization. Seeking to leverage technical and analytical skills to solve complex problems and drive business insights in an entry-level data analyst role. Education:- Bachelor of Science in [Your Major] [Your University], [City, State] Graduation Date: [Month, Year] ● Relevant Coursework: Data Structures, Statistics, Data Mining, Machine Learning, Database Management, Business Analytics Technical Skills:- ● Programming Languages: Python, R, SQL ● Data Manipulation: pandas, NumPy ● Data Visualization: matplotlib, seaborn, ggplot2, Tableau, Power BI ● Databases: MySQL, PostgreSQL ● Tools: Excel, Jupyter Notebook, RStudio ● Other Skills: Data Cleaning, Data Wrangling, Exploratory Data Analysis (EDA), Statistical Analysis, Machine Learning Basics Projects:- Project Title 1 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Project Title 2 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Project Title 3 ● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.] ● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.] Internships and Experience:- Data Analyst Intern [Company Name], [City, State] [Month, Year] – [Month, Year] ● Assisted in collecting, cleaning, and analyzing large datasets to support business decision-making. ● Developed dashboards and visualizations to present data insights to stakeholders. ● Conducted statistical analyses to identify trends and patterns in data. Research Assistant [University Department or Lab], [City, State] [Month, Year] – [Month, Year] ● Collaborated on research projects involving data collection, data entry, and preliminary data analysis. ● Used statistical software to analyze research data and prepare reports. Certifications:- ● Google Data Analytics Professional Certificate ● Microsoft Certified: Data Analyst Associate ● [Any other relevant certification] Extracurricular Activities:- Member, Data Science Club, [Your University] ● Participated in data analysis competitions and hackathons. ● Attended workshops and seminars on data science and analytics. Volunteer, [Organization Name] ● Contributed to data-driven projects that helped the organization improve its operations and outreach. Additional Information:- ● Languages: [Any languages you speak other than English, if applicable] ● Interests: [Relevant interests that can show your passion for data and analysis, e.g., participating in Kaggle competitions, blogging about data science, etc.]

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? 𝙍𝙚𝙖𝙙 𝙖𝙗𝙤𝙪𝙩 𝙢𝙮 𝙧𝙚𝙘𝙚𝙣𝙩 𝙤𝙣𝙚-𝙝𝙤𝙪𝙧 𝙞𝙣𝙩𝙚𝙧𝙫𝙞𝙚𝙬 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚 𝙩𝙤 𝙛𝙞𝙣𝙙 𝙤𝙪𝙩 𝙩𝙝𝙚 𝙢𝙖𝙞𝙣 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣𝙨 𝙄 𝙬𝙖𝙨 𝙖𝙨𝙠𝙚𝙙! 𝘋𝘰𝘯'𝘵 𝘧𝘰𝘳𝘨𝘦𝘵 𝘵𝘰 𝘴𝘩𝘢𝘳𝘦 𝘺𝘰𝘶𝘳 𝘢𝘯𝘴𝘸𝘦𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵 𝘴𝘦𝘤𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 𝘸𝘩𝘪𝘤𝘩 𝘐 𝘮𝘢𝘳𝘬𝘦𝘥 ⭐ . 𝘓𝘦𝘵'𝘴 𝘥𝘪𝘴𝘤𝘶𝘴𝘴 𝘵𝘩𝘦 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘢𝘯𝘴𝘸𝘦𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘴𝘦𝘤𝘵𝘪𝘰𝘯. 𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 -Brief introduction about yourself. -Explanation of how you developed an interest in learning Power BI despite having a chemical background. 𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 -Discussion about the tools you are proficient in. -Detailed explanation of a project that demonstrated your proficiency in these tools. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻 Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project Follow-up Question: Was there any improvement in sales after building the report? Provide a clear before and after scenario in sales post-report creation. What areas did you identify where the company was losing sales, and what were your recommendations? - How do you check the quality of data when it's given to you? Explain your methods for ensuring data quality. - How do you handle null values? Describe your approach to managing null values in datasets. 𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 -Explain the order in which SQL clauses are executed. -Write a query to find the percentage of the 18-year-old population. Details: You are given two tables: Table 1: Contains states and their respective populations. Table 2: Contains three columns (state, gender, and population of 18-year-olds). -Explain window functions and how to rank values in SQL. - Difference between JOIN and UNION. -How to return unique values in SQL. 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 -Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons. Step-by-step solution for the water puzzle. - What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career. -Describe cases when you showcased team spirit. -⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Scenario: Choose any social media app (I choose Discord). Question: What function/feature would you add to the Discord app, and how would you track its success? - Rate yourself on Excel, SQL, and Python out of 10. - What are your strengths in data analytics?

Data Analytics Projects for Beginners 👇 Web Scraping https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis Product Price Scraping and Analysis https://github.com/CodesdaLu/Web-Scrapping News Scraping https://github.com/rohit-yadav/scraping-news-articles Real Time Stock Price Scraping with Python https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz Zomato Analysis https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk IPL Analysis https://github.com/Yashmenaria1/IPL-Data-Exploration https://www.youtube.com/watch?v=ur-v0dv0Qtw https://www.youtube.com/watch?v=ur-v0dv0Qtw Football Data Analysis https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP Market Basket Analysis https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg Customer Churn Prediction https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction Employee’s Performance for HR Analytics https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics Food Price Prediction https://github.com/VectorInstitute/foodprice-forecasting

Short Data Analytics Learning Roadmap 👇😊 1. Foundational Knowledge -Statistics:Basics of statistics and probability. - Programming:Learn Python or R. 2. Data Handling - Libraries:Pandas, NumPy (Python); dplyr, tidyr (R). 3. Exploratory Data Analysis (EDA) -Visualization:Matplotlib, Seaborn (Python); ggplot2 (R). 4. Database Management - SQL:Querying and joins. 5. Core Analytical Skills - Hypothesis Testing: t-tests, chi-square. - Regression:Linear, logistic. - Machine Learning:Basics of supervised and unsupervised learning. 6. Practical Experience - Projects: Real-world data projects. - Competitions:Participate in Kaggle. 7. Continuous Learning I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Like if it helps :)