Dicta💕

Hi everyone. Good morning. Beginning my tech journey in Data Analytics. Please follow me on here. I am on TikTok as @dicta_andy and also on Instagram as @dicta_andy too. Thanks guys 😊😊😊💕💕💕

Junior-level Data Analyst interview questions: Introduction and Background 1. Can you tell me about your background and how you became interested in data analysis? 2. What do you know about our company/organization? 3. Why do you want to work as a data analyst? Data Analysis and Interpretation 1. What is your experience with data analysis tools like Excel, SQL, or Tableau? 2. How would you approach analyzing a large dataset to identify trends and patterns? 3. Can you explain the concept of correlation versus causation? 4. How do you handle missing or incomplete data? 5. Can you walk me through a time when you had to interpret complex data results? Technical Skills 1. Write a SQL query to extract data from a database. 2. How do you create a pivot table in Excel? 3. Can you explain the difference between a histogram and a box plot? 4. How do you perform data visualization using Tableau or Power BI? 5. Can you write a simple Python or R script to manipulate data? Statistics and Math 1. What is the difference between mean, median, and mode? 2. Can you explain the concept of standard deviation and variance? 3. How do you calculate probability and confidence intervals? 4. Can you describe a time when you applied statistical concepts to a real-world problem? 5. How do you approach hypothesis testing? Communication and Storytelling 1. Can you explain a complex data concept to a non-technical person? 2. How do you present data insights to stakeholders? 3. Can you walk me through a time when you had to communicate data results to a team? 4. How do you create effective data visualizations? 5. Can you tell a story using data? Case Studies and Scenarios 1. You are given a dataset with customer purchase history. How would you analyze it to identify trends? 2. A company wants to increase sales. How would you use data to inform marketing strategies? 3. You notice a discrepancy in sales data. How would you investigate and resolve the issue? 4. Can you describe a time when you had to work with a stakeholder to understand their data needs? 5. How would you prioritize data projects with limited resources? Behavioral Questions 1. Can you describe a time when you overcame a difficult data analysis challenge? 2. How do you handle tight deadlines and multiple projects? 3. Can you tell me about a project you worked on and your role in it? 4. How do you stay up-to-date with new data tools and technologies? 5. Can you describe a time when you received feedback on your data analysis work? Final Questions 1. Do you have any questions about the company or role? 2. What do you think sets you apart from other candidates? 3. Can you summarize your experience and qualifications? 4. What are your long-term career goals? Hope this helps you 😊

@CasmirCodesData Yes!!!

Data 101: Always duplicate your original dataset

@King_Abbey_ @ezekiel_aleke @TechSphereAcad 💕

@dicta_andy @ezekiel_aleke @TechSphereAcad I missed that part then. Thank you.

I’m not afraid to admit that I’m not a fan of theoretical exams but since @ezekiel_aleke told us in the last class that the next quiz would be theory.. well let’s just say i don’t want to lose my perfect streak😔 @TechSphereAcad #day52 #60DaysOfTech

Master SQL SQL MASTER TREE │ ├── 1. Database Fundamentals │ ├── What is DB / DBMS / RDBMS │ ├── Tables, Rows, Columns │ ├── Primary Key │ ├── Foreign Key │ ├── Candidate Key │ ├── Composite Key │ ├── Constraints │ │ ├── NOT NULL │ │ ├── UNIQUE │ │ ├── PRIMARY KEY │ │ ├── FOREIGN KEY │ │ ├── CHECK │ │ └── DEFAULT │ └── Data Integrity │ ├── 2. SQL Data Types │ ├── Numeric │ │ ├── INT │ │ ├── BIGINT │ │ ├── DECIMAL │ │ └── FLOAT │ ├── String │ │ ├── CHAR │ │ ├── VARCHAR │ │ └── TEXT │ ├── Date & Time │ │ ├── DATE │ │ ├── TIME │ │ ├── DATETIME │ │ └── TIMESTAMP │ └── Boolean / Binary │ ├── 3. DDL (Data Definition Language) │ ├── CREATE │ │ ├── DATABASE │ │ ├── TABLE │ │ └── INDEX │ ├── ALTER │ │ ├── ADD COLUMN │ │ ├── MODIFY COLUMN │ │ └── DROP COLUMN │ ├── DROP │ │ ├── DATABASE │ │ └── TABLE │ └── TRUNCATE │ ├── 4. DML (Data Manipulation Language) │ ├── INSERT │ ├── UPDATE │ ├── DELETE │ └── MERGE / UPSERT │ ├── 5. DQL (Data Query Language) │ ├── SELECT │ ├── DISTINCT │ ├── WHERE │ │ ├── AND │ │ ├── OR │ │ └── NOT │ ├── ORDER BY │ ├── GROUP BY │ ├── HAVING │ └── LIMIT / OFFSET │ ├── 6. SQL Operators │ ├── Arithmetic (+ - * /) │ ├── Comparison (= != > < >= <=) │ ├── Logical (AND OR NOT) │ ├── BETWEEN │ ├── IN │ ├── LIKE │ └── IS NULL │ ├── 7. SQL Functions │ ├── Aggregate │ │ ├── COUNT │ │ ├── SUM │ │ ├── AVG │ │ ├── MIN │ │ └── MAX │ ├── String │ │ ├── CONCAT │ │ ├── SUBSTRING │ │ ├── LENGTH │ │ └── TRIM │ ├── Numeric │ │ ├── ROUND │ │ └── ABS │ └── Date │ ├── NOW │ ├── DATEADD │ └── DATEDIFF │ ├── 8. Joins │ ├── INNER JOIN │ ├── LEFT JOIN │ ├── RIGHT JOIN │ ├── FULL JOIN │ ├── CROSS JOIN │ └── SELF JOIN │ ├── 9. Subqueries │ ├── Scalar Subquery │ ├── Correlated Subquery │ └── Nested Subquery │ ├── 10. Views │ ├── CREATE VIEW │ ├── UPDATE VIEW │ └── MATERIALIZED VIEW │ ├── 11. Indexing │ ├── Clustered Index │ ├── Non-Clustered Index │ ├── Composite Index │ └── Index Optimization │ ├── 12. Transactions │ ├── BEGIN │ ├── COMMIT │ ├── ROLLBACK │ └── SAVEPOINT │ ├── 13. ACID Properties │ ├── Atomicity │ ├── Consistency │ ├── Isolation │ └── Durability │ ├── 14. Normalization │ ├── 1NF │ ├── 2NF │ ├── 3NF │ ├── BCNF │ └── Denormalization │ ├── 15. Advanced SQL │ ├── Stored Procedures │ ├── Triggers │ ├── CTE (WITH) │ ├── Window Functions │ │ ├── ROW_NUMBER │ │ ├── RANK │ │ ├── DENSE_RANK │ │ └── PARTITION BY │ └── Recursive Queries │ ├── 16. Performance Optimization │ ├── Query Optimization │ ├── Execution Plan │ ├── Index Tuning │ └── Query Caching │ ├── 17. SQL Ecosystem │ ├── MySQL │ ├── PostgreSQL │ ├── SQLite │ ├── SQL Server │ └── Oracle DB │ └── 18. Real-World Usage ├── Backend APIs ├── Data Analytics ├── Reporting Systems ├── ETL Pipelines └── Data Warehousing SQL full course link: youtu.be/nPCAM1yI8uE

@King_Abbey_ @ezekiel_aleke @TechSphereAcad Yea.. he said it on Saturday

@dicta_andy @ezekiel_aleke @TechSphereAcad Wow really? I didn’t know this. I would have been caught by surprise.

If you're ready to start your own 4-month journey My April 27th cohort is open. Excel. SQL. Power BI. Python. AI-assisted analysis. Everything she learned. Structured. Step by step. Join here → selar.com/6r6226

#LearningWithTS #justagirlwholikesdata💕

@MaryjaneUzuegbu @ezekiel_aleke @TechSphereAcad Thank you 😊💕

@dicta_andy @ezekiel_aleke @TechSphereAcad This is amazing Dicta

Day 50. Two of the dashboards done by our data analytics tutor @ezekiel_aleke replicated by me using his Power BI YouTube tutorial videos.😊😊 @TechSphereAcad #day50 #60DaysOfTech #powerbi #visualizations #LearningWithTS #justagirlwholikesdata💕

@simplyselected @ezekiel_aleke Dm

@dicta_andy @ezekiel_aleke Dicta u fit send me the dataset

Day 51. I got a dataset on Car Insurance Policies from a friend to practice on. Using the knowledge i got from the Excel Classes with our data analytics tutor @ezekiel_aleke at TS Academy, i was

In 10 days, we'll be gaining practical insights on how to build a career that survives the era of AI. If you're still outside, you're wrong. Join us by clicking the link in bio to register.

@Sampresh22 @ezekiel_aleke Thank you 😊💕

@dicta_andy @ezekiel_aleke Nice work Dicta

@stellaanalyzes @ezekiel_aleke Thank you 💕

@dicta_andy @ezekiel_aleke WELDONE, Dicta.

d. Added Conditional Columns to group the columns needed. I would take my time with the next steps so as to get accurate insights. @TechSphereAcad #day51 #60DaysOfTech #workingwithexcel #LearningWithTS #justagirlwholikesdata💕

b. Applied the correct data type to each of the columns. c. Inserted a Custom Column in order to separate the incorrect data in the Car Year column and deleted it after i was done.

able to clean it using Power Query. The following steps were taken in order to change the messy data to the clean data as seen in the video. a. Used the Proper Function on the column headers and made the text bold.

@JeremiahMi14419 @ezekiel_aleke @TechSphereAcad Thank you 💕

@dicta_andy @ezekiel_aleke @TechSphereAcad Well done

@DIKAWRITES @Aabdul_HameedD @veraherself Thanks Dika 💕

@Aabdul_HameedD @dicta_andy @veraherself

Power BI DAX Cheet Sheet for beginners and pros. Bookmark this 🔖📑