Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

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Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made. When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

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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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Skills need for everyday data analysis jobs

Data Analysis vs Data Science Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization. On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming. In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data. Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k Steps to become data scientist -> https://t.me/learndataanalysis/559

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Here is a glimpse of what these resources contains. It covers the top-notch Data Analytics Resources to learn SQL, Python, Excel, Power BI, Data Science, Machine Learning, BI Templates, Data Visualization, Tableau, Artificial Intelligence, and Deep Learning.

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✔️📚A beginner's roadmap for learning SQL: 🔺Understand Basics: Learn what SQL is and its purpose in managing relational databases. Understand basic database concepts like tables, rows, columns, and relationships. 🔺Learn SQL Syntax: Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE. Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN. 🔺Setup a Database: Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL. Practice creating databases, tables, and inserting data. 🔺Retrieve Data (SELECT): Learn to retrieve data from a database using SELECT statements. Practice filtering data using WHERE clause and sorting using ORDER BY. 🔺Modify Data (INSERT, UPDATE, DELETE): Understand how to insert new records, update existing ones, and delete data. Be cautious with DELETE to avoid unintentional data loss. 🔺Working with Functions: Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis. Understand string functions, date functions, and mathematical functions. 🔺Data Filtering and Sorting: Learn advanced filtering techniques using AND, OR, and IN operators. Practice sorting data using multiple columns. 🔺Table Relationships (JOIN): Understand the concept of joining tables to retrieve data from multiple tables. Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. 🔺Grouping and Aggregation: Explore GROUP BY clause to group data based on specific columns. Understand aggregate functions for summarizing data (SUM, AVG, COUNT). 🔺Subqueries: Learn to use subqueries to perform complex queries. Understand how to use subqueries in SELECT, WHERE, and FROM clauses. 🔺Indexes and Optimization: Gain knowledge about indexes and their role in optimizing queries. Understand how to optimize SQL queries for better performance. 🔺Transactions and ACID Properties: Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability). Understand how to use transactions to maintain data integrity. 🔺Normalization: Understand the basics of database normalization to design efficient databases. Learn about 1NF, 2NF, 3NF, and BCNF. 🔺Backup and Recovery: Understand the importance of database backups. Learn how to perform backups and recovery operations. 🔺Practice and Projects: Apply your knowledge through hands-on projects. Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects. 👀👍Remember to practice regularly and build real-world projects to reinforce your learning. Happy Learning 🥳 📚

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🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics. 👉🏻 Basic Aggregation function: 1️⃣ AVG 2️⃣ COUNT 3️⃣ SUM 4️⃣ MIN 5️⃣ MAX 👉🏻 JOINS 1️⃣ Left 2️⃣ Inner 3️⃣ Self (Important, Practice questions on self join) 👉🏻 Windows Function (Important) 1️⃣ Learn how partitioning works 2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE) 3️⃣ Use Cases of LEAD & LAG functions 4️⃣ Use cases of Aggregate window functions 👉🏻 GROUP BY 👉🏻 WHERE vs HAVING 👉🏻 CASE STATEMENT 👉🏻 UNION vs Union ALL 👉🏻 LOGICAL OPERATORS Other Commonly used functions: 👉🏻 IFNULL 👉🏻 COALESCE 👉🏻 ROUND 👉🏻 Working with Date Functions 1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY 2️⃣ Calculating date differences 👉🏻CTE 👉🏻Views & Triggers (optional) Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz Hope it helps in your SQL learning 📚