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!

I have uploaded a lot of free resources on Linkedin as well
👇👇
https://www.linkedin.com/company/sql-analysts/

We're just 6k followers away from reaching 200k on LinkedIn! ❤️ Join us and be part of this milestone!

✅ 𝐇𝐨𝐰 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐂𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐢𝐧 𝟐𝟎𝟐𝟓 🧑‍💻

If you are thinking about becoming a data analyst, 2025 is the perfect year to start. Companies need people who can understand data and turn it into useful insights. Here’s a simple step-by-step guide to help you start your journey.

𝟏. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐑𝐨𝐥𝐞
A data analyst collects and studies data to help companies make better decisions. They find trends, create reports, and suggest solutions to business problems.

𝟐. 𝐋𝐞𝐚𝐫𝐧 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐒𝐤𝐢𝐥𝐥𝐬
𝐄𝐱𝐜𝐞𝐥: Start with PivotTables, VLOOKUP, and creating dashboards.
𝐒𝐐𝐋: Master queries to extract and manipulate data.
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥𝐬: Learn Power BI and Tableau to present insights effectively.
𝐏𝐲𝐭𝐡𝐨𝐧: Focus on libraries like Pandas, NumPy, Matplotlib, and Seaborn.
𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: Basic concepts- mean, median, mode, standard deviation, regression.

𝟑. 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
https://t.iss.one/sqlproject
https://t.iss.one/pythonspecialist

𝟒. 𝐆𝐚𝐢𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Certifications add credibility to your resume. Some popular ones include:
Google Data Analytics Professional Certificate
Microsoft Certified: Data Analyst Associate
Tableau Desktop Specialist Certification

𝟓. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨
𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: Treat your LinkedIn profile as your portfolio. Update it with skills, certifications, and projects.
𝐆𝐢𝐭𝐇𝐮𝐛: Add links to your GitHub repositories with coding projects and Power BI/Tableau dashboards.

𝟔. 𝐆𝐚𝐢𝐧 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 (𝐅𝐨𝐫 𝐅𝐫𝐞𝐬𝐡𝐞𝐫𝐬)
If you're a fresher, here are some ideas to gain experience:
𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩𝐬: Apply for internships at companies where you can work on real data problems.
𝐅𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐢𝐧𝐠: Offer data analysis services on platforms like Upwork, Fiverr, or Freelancer.
𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Build your own projects, such as analyzing public datasets (e.g., from Kaggle), and share them on GitHub.
𝐎𝐧𝐥𝐢𝐧𝐞 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧𝐬: Participate in data analysis competitions on Kaggle or DrivenData to build your skills and gain recognition.
𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞: Contribute to open-source data analysis projects on GitHub.

𝟕. 𝐒𝐭𝐚𝐫𝐭 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐟𝐨𝐫 𝐉𝐨𝐛𝐬
Tailor your resume and portfolio for each role. Highlight projects and key skills. Consider entry-level roles like:
Junior Data Analyst, Business Analyst, Reporting Analyst
Use platforms like LinkedIn & Naukri to apply for jobs.

Free Session to learn Data Analytics, Data Science & AI
👇👇
https://tracking.acciojob.com/g/PUfdDxgHR

Register fast, only for first few users

Data Analytics tools

How to become a Data Analyst in 2025

5 Data Analytics Project Ideas to boost your resume:

1. Stock Market Portfolio Optimization

2. YouTube Data Collection & Analysis

3. Elections Ad Spending & Voting Patterns Analysis

4. EV Market Size Analysis

5. Metro Operations Optimization

Excel Formulas

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.