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As a Data Analyst or Business Analyst. I usually perform the following tasks to bring optimization and efficiency in my work. 1) Automation of reports which take 5 to 6 hours of time on weekly basis. I automate them using Python , SQL and BI Visualization or translating them to SharePoint so that the same work can be done in reduced time. 2) Scoping and documentation of reports and projects so that any stakeholder can easily understand it. I Analyze and prioritize requirements, ensuring they align with business goals and are feasible within project constraints such as budget and time 3) Pick the business problem and coordinate with the stakeholder and set up weekly meetings to understand it in more detail. Try to answer it with data using sql, excel and power bi. Build some hypothesis, test it and the execute it. Build visibility through dashboard and then track it to see the compliance and impact of solution. 4) Make sure data quality is correct. For Data Cleaning and Preprocessing prepare raw data for analysis by identifying and correcting errors, handling missing values, standardizing formats, and ensuring consistency. 5) Develop ETL to answer business problems 6) (EDA) Explore datasets using statistical and visualization techniques to discover patterns, trends, outliers, and relationships that can provide insights into business problems. 7) Meetings with stakeholders regular to identify any bottleneck in the ongoing or the previous projects 8) Monitor key performance indicators (KPIs) and metrics to assess the impact of business initiatives, identify areas for improvement, and provide regular reports to stakeholders.

Data Analytics https://www.linkedin.com/company/sql-analysts/

Python is also important for some data roles. If you're learning python for data analyst role, then start learning concepts related to related to lists, tuples, dictionaries, basic data structure problems, loops, conditional statements, functions , pandas & numpy. Other details I already added in 100 days roadmap. Practice is the key with SQL & Python.

Complete Python Topics for Data Analytics 👇👇 https://www.linkedin.com/posts/sql-analysts_python-cheatsheet-activity-7196728645044834307-BYgX?utm_source=share&utm_medium=member_android Like for more ❤️

I have created this 100-Day Roadmap & Resources for Data Analytics today 👇👇 https://topmate.io/analyst/981703 Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

This post is for freshers who get confused with the interview questions for the data roles. Best tip from my side would be to start focusing on your SQL skills. Most of the data roles ask SQL questions based on joins & aggregate functions. Some interviewers may also ask questions based on window function. But, make your basics solid and practice it well. If you are from non-coding background focus on your excel and bi skills. Learn vlookups, hlookups, pivot table, pivot charts and questions based on basic formulas. But whatever the case is, stay resilient and believe on yourself. If unsure, start applying for jobs & give interviews. Even you don't know the answers, don't worry. Even you don't crack the interview, don't worry. It's all part of this journey and you'll become better version of yourself with every small improvement. Hope it helps :)

Top 5 Tools to master Data Analytics 1. Python: - Versatile programming language. - Offers powerful libraries like Pandas, NumPy, and Scikit-learn. - Used for data manipulation, analysis, and machine learning tasks. 2. R: - Statistical programming language. - Provides extensive statistical capabilities. - Popular for data analysis in academia. - Offers visualization libraries like ggplot2. 3. SQL (Structured Query Language): - Essential for working with relational databases. - Allows querying, manipulation, and management of data. - Standard language for database management systems. 4. Tableau: - Data visualization tool. - Enables creation of interactive dashboards. - Helps in communicating insights effectively. - Widely used in business intelligence. 5. Apache Spark: - Framework for large-scale data processing. - Offers distributed computing capabilities. - Libraries like Spark SQL and MLlib for data manipulation and machine learning. - Ideal for processing big data efficiently. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Like if it helps :)

Best practices to follow while creating Tableau dashboards 👇👇 https://www.linkedin.com/posts/sql-analysts_learn-tableau-activity-7195275749245784064-9TNf?utm_source=share&utm_medium=member_android

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources: 🗓️Week 1: Foundation of Data Analytics ◾Day 1-2: Basics of Data Analytics Resource: Khan Academy's Introduction to Statistics Focus Areas: Understand descriptive statistics, types of data, and data distributions. ◾Day 3-4: Excel for Data Analysis Resource: Microsoft Excel tutorials on YouTube or Excel Easy Focus Areas: Learn essential Excel functions for data manipulation and analysis. ◾Day 5-7: Introduction to Python for Data Analysis Resource: Codecademy's Python course or Google's Python Class Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas. 🗓️Week 2: Intermediate Data Analytics Skills ◾Day 8-10: Data Visualization Resource: Data Visualization with Matplotlib and Seaborn tutorials Focus Areas: Creating effective charts and graphs to communicate insights. ◾Day 11-12: Exploratory Data Analysis (EDA) Resource: Towards Data Science articles on EDA techniques Focus Areas: Techniques to summarize and explore datasets. ◾Day 13-14: SQL Fundamentals Resource: Mode Analytics SQL Tutorial or SQLZoo Focus Areas: Writing SQL queries for data manipulation. 🗓️Week 3: Advanced Techniques and Tools ◾Day 15-17: Machine Learning Basics Resource: Andrew Ng's Machine Learning course on Coursera Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics. ◾Day 18-20: Data Cleaning and Preprocessing Resource: Data Cleaning with Python by Packt Focus Areas: Techniques to handle missing data, outliers, and normalization. ◾Day 21-22: Introduction to Big Data Resource: Big Data University's courses on Hadoop and Spark Focus Areas: Basics of distributed computing and big data technologies. 🗓️Week 4: Projects and Practice ◾Day 23-25: Real-World Data Analytics Projects Resource: Kaggle datasets and competitions Focus Areas: Apply learned skills to solve practical problems. ◾Day 26-28: Online Webinars and Community Engagement Resource: Data Science meetups and webinars (Meetup.com, Eventbrite) Focus Areas: Networking and learning from industry experts. ◾Day 29-30: Portfolio Building and Review Activity: Create a GitHub repository showcasing projects and code Focus Areas: Present projects and skills effectively for job applications. 👉Additional Resources: Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus. Online Platforms: DataSimplifier, Kaggle, Towards Data Science Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

📢 Calling All Developers to talk with Founder from IIT Delhi to discover the future of Coding 📢 Are you ready to supercharge your productivity and take your coding skills to the next level? 🚀 Event Details: Date : 9th May 2024 Time : 9:00 PM to 10:00 PM Register for free: https://www.buildfastwithai.com/events/10x-developer-productivity-with-ai Connect with Founder: https://www.linkedin.com/in/satvik-paramkusham/ This event is especially designed for people interested in Data Science, Data Analysis, GenAI and LLM.

Skills Required For A Data Analyst in 2021 Basic Excel-  https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_ADx6nJcoTM5t2S1bmsMdm Advanced Excel-  https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_e9xmZNPACsLdgie3Tkaxf SQL- https://www.youtube.com/watch?v=5JCyiutyu_o&list=PLmQAMKHKeLZ-kD9VN0prfKCByr9pa4jw6 SQL- (Khan Academy)- https://www.khanacademy.org/computing/computer-programming/sql Python Programming Language-  https://www.youtube.com/watch?v=bPrmA1SEN2k&list=PLZoTAELRMXVNUL99R4bDlVYsncUNvwUBB Stats Lectures-  https://www.youtube.com/watch?v=zRUliXuwJCQ&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Stats Lectures(Khans Academy)- https://www.khanacademy.org/math/statistics-probability Python EDA-  https://www.youtube.com/playlist?list=PLZoTAELRMXVPQyArDHyQVjQxjj_YmEuO9 Python Feature Engineering-  https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Tableau-  https://www.tableau.com/academic/student-Iron-Viz

To become a successful data analyst, you need a combination of technical skills, analytical skills, and soft skills. Here are some key skills required to excel in a data analyst role: 1. Statistical Analysis: Understanding statistical concepts and being able to apply them to analyze data sets is essential for a data analyst. Knowledge of probability, hypothesis testing, regression analysis, and other statistical techniques is important. 2. Data Manipulation: Proficiency in tools like SQL for querying databases and manipulating data is crucial. Knowledge of data cleaning, transformation, and preparation techniques is also important. 3. Data Visualization: Being able to create meaningful visualizations using tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is essential for effectively communicating insights from data. 4. Programming: Strong programming skills in languages like Python or R are often required for data analysis tasks. Knowledge of libraries like Pandas, NumPy, and scikit-learn in Python can be beneficial. 5. Machine Learning(optional): Understanding machine learning concepts and being able to apply algorithms for predictive modeling, clustering, and classification tasks is becoming increasingly important for data analysts. 6. Database Management: Knowledge of database systems like MySQL, PostgreSQL, or MongoDB is useful for working with large datasets and understanding how data is stored and retrieved. 7. Critical Thinking: Data analysts need to be able to think critically and approach problems analytically. Being able to identify patterns, trends, and outliers in data is important for drawing meaningful insights. 8. Business Acumen: Understanding the business context and objectives behind the data analysis is crucial. Data analysts should be able to translate data insights into actionable recommendations for business decision-making. 9. Communication Skills: Data analysts need to effectively communicate their findings to non-technical stakeholders. Strong written and verbal communication skills are essential for presenting complex data analysis results in a clear and understandable manner. 10. Continuous Learning: The field of data analysis is constantly evolving, so a willingness to learn new tools, techniques, and technologies is important for staying current and adapting to changes in the industry. By developing these skills and gaining practical experience through projects or internships, you can build a strong portfolio for a successful career as a data analyst.

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