*Want to become a Data Analyst?* Here’s a roadmap with essential skills, tools & concepts you’ll need to master: 1. Data Fundamentals * Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation. * Probability: Understand basic probability theory, including conditional probability, Bayes’ theorem, and probability distributions. 2. Data Cleaning * Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection. * Data Transformation: Data type conversions, feature engineering, and handling categorical variables. * Pandas: Master data manipulation with Pandas (merge, join, group, pivot). 3. Data Visualization (https://t.me/dataanalysisresourcestp) * Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations. * Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports. * Design Principles: Learn best practices for designing clear, effective visualizations. 4. SQL for Data Analysis (https://t.me/sqlresourcestp) * Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs. * Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries. * Aggregation Functions: SUM, AVG, MIN, MAX, COUNT. * Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases. 5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp) * Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE. * Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF. * Data Visualization in Excel: Create pivot tables, charts, and dashboards. 6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp) * Python: Learn data handling and manipulation with Pandas and NumPy. * R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2. * Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R. 7. Exploratory Data Analysis (EDA) * Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data. * Visual EDA: Use pair plots, heatmaps, and distribution plots for insights. * Summary Statistics: Understand distributions, variance, and central tendencies of variables. 8. Business Acumen * Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce). * Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals. * KPI Analysis: Identify and measure key performance indicators for informed decision-making. 9. Data Collection & Sourcing * APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Python’s Requests library. * Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality). * Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses. 10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp) * Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders. * Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret. 11. Soft Skills * Communication: Clearly present data insights and recommendations to stakeholders. * Critical Thinking: Approach problems analytically to uncover insights. * Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues. Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37) Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762) Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942) Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36) Like for more data analytics resources ❤️ ENJOY LEARNING👍👍 Join for more free resources https://t.me/TechPsyche