Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science journey! 📊

ENJOY LEARNING 👍👍

Goldman Sachs senior data analyst interview asked questions

SQL

1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)

POWER BI

1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?

PYTHON

1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Hope this helps you 😊

Data Analytics Youtube Resources

Self guide to become a data analyst

Join for more: https://t.iss.one/DataAnalystInterview

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!

⏰ MySQL Data Types

MySQL provides a variety of data types to store different kinds of data. These are categorized into three main groups:

1. Numeric Data Types:
- INT, BIGINT, SMALLINT, TINYINT: For whole numbers.
- DECIMAL, FLOAT, DOUBLE: For real numbers with decimal points.
- BIT: For binary values.
- Example:

            CREATE TABLE numeric_example (
                id INT,
                amount DECIMAL(10, 2)
            );
            
            

1. String Data Types:
- CHAR, VARCHAR: For fixed and variable-length strings.
- TEXT: For large text.
- BLOB: For binary large objects like images.
- Example:

            CREATE TABLE string_example (
                name VARCHAR(100),
                description TEXT
            );
            
            

1. Date and Time Data Types:
- DATE, DATETIME, TIMESTAMP: For date and time values.
- YEAR: For storing a year.
- Example:

                CREATE TABLE datetime_example (
                    created_at DATETIME,
                    year_of_joining YEAR
                );
                
                

Interview Questions:

- Q1: What is the difference between CHAR and VARCHAR?
A1: CHAR has a fixed length, while VARCHAR has a variable length. VARCHAR is more storage-efficient for varying-length data.
- Q2: When should you use DECIMAL instead of FLOAT?
A2: Use DECIMAL for precise calculations (e.g., financial data) and FLOAT for approximate values where precision is less critical.

Essential questions related to Data Analytics 👇👇

Question 1: What is the first skill a fresher should learn for a Data Analytics job?
Answer: SQL. It’s the foundation for retrieving, manipulating, and analyzing data stored in databases.

Question 2: Which SQL database query should we learn - MySQL, PostgreSQL, PL-SQL, etc.?
Answer: Core SQL concepts are consistent across platforms. Focus on joins, aggregations, subqueries, and window functions.

Question 3: How much Python is required?
Answer: Learn basic syntax, loops, conditional statements, functions, and error handling. Then focus on Pandas and Numpy very well for data handling and analysis. Working Knowledge of Python + Good knowledge of Data Analysis Libraries is needed only.

Question 4: What other skills are required?
Answer: MS Excel for data cleaning and analysis, and a BI tool like Power BI or Tableau for creating dashboards.

Question 5: Is knowledge of Macros/VBA required?
Answer: No. Most Data Analyst roles don’t require it.

Question 6: When should I start applying for jobs?
Answer: Apply after acquiring 50% of the required skills and gaining practical experience through projects or internships.

Question 7: Are certifications required?
Answer: No. Projects and hands-on experience are more valuable.

Question 8: How important is data visualization in a Data Analyst role?
Answer: Very important. Use tools like Tableau or Power BI to present insights effectively.

Question 9: Is understanding statistics important for data analysis?
Answer: Yes. Learn descriptive statistics, hypothesis testing, and regression analysis for better insights.

Question 10: How much emphasis should be placed on machine learning?
Answer: A basic understanding is helpful but not essential for Data Analyst roles.

Question 11: What role does communication play in a Data Analyst's job?
Answer: It’s crucial. You need to present insights in a clear and actionable way for stakeholders.

Question 12: Is data cleaning a necessary skill?
Answer: Yes. Cleaning and preparing raw data is a major part of a Data Analyst’s job.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

🚀👉Data Analytics skills and projects to add in a resume to get shortlisted

1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.

2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.

3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.

4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.

5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.

6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.

7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.

8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.

9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.

10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.

11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.

12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.

💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.

Excel Cheatsheet

If you’re a data analyst, here’s what recruiters really want:

It’s not just about knowing the tools like Power BI, SQL, and Python.

They want to see that you can:

Understand business problems

Communicate your findings clearly

Turn data into useful insights

Make predictions about future trends

Data analysis isn’t just about generating reports; it’s about using data to support your company’s goals.


Show that you can connect the dots, see the bigger picture, and explain your findings in simple terms.

Data Analytics isn't SQL.
Data Analytics isn't Python.
Data Analytics isn't Tableau.
Data Analytics isn't Power BI.
Data Analytics isn't R.
Data Analytics isn't Statistics.
Data Analytics isn't even spreadsheets.

Data Analytics is exporting dashboards to Excel for people who make 3 times your salary.

✅𝗖𝗼𝗿𝗿𝗲𝗰𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗮𝘀𝗸 𝗳𝗼𝗿 𝗮 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹:👩💻

---

Subject: Referral Request for [Position] at [Company Name]

Hi [Recipient's Name]🙂,

I hope you’re doing well. I’m interested in the [Position] at [Company] and noticed you work there. My background in data analytics, particularly in [specific expertise], aligns well with this role.

I understand the interviews will likely focus heavily on technical data analysis skills, and I’m well-prepared, having worked on numerous projects and effectively used data-driven strategies to address complex challenges.

Here are the details for your reference:
- Job posting: [Job Link]
- Resume: [Resume Link]
- Projects and coding profile:
- GitHub: [GitHub Link]
- [Coding Profile Link] (e.g., [mention ranking/level if impressive])

I assure you that a referral will be highly valued and I will make the most of this opportunity. I’m also happy to assist you with anything in return.

Any additional suggestion/advice you can provide would be greatly appreciated.

Thanks in advance!

Best,
[Your Full Name]

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Portfolio - https://t.iss.one/DataPortfolio

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Hope it helps :)

DATA ANALYST + CHATGPT = DATA MASTERY
👇👇
https://t.iss.one/aiindi/469

100 Days of Data Analysis Roadmap

Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes.

Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.

Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.

ENJOY LEARNING 👍👍

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

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

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

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)

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

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.

Top-notch Data Analytics Resources

How to become a Data Analyst in 2025

Free Resources to learn Data Analytics

Data Analyst Learning Plan

Join @free4unow_backup for more free courses

Like for more data analytics resources ❤️

ENJOY LEARNING👍👍

✔️📚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 🥳 📚