BECOMING A DATA ANALYST IN 2025
Becoming a data analyst doesn’t have to be expensive in 2025.
With the right free resources and a structured approach,
you can become a skilled data analyst.
Here’s a roadmap with free resources to guide your journey:
1️⃣ Learn the Basics of Data Analytics
Start with foundational concepts like:
↳ What is data analytics?
↳ Types of analytics (denoscriptive, predictive, prenoscriptive).
↳ Basics of data types and statistics.
📘 Free Resources:
1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability
2. Introduction to Data Analytics by IBM (audit for free) :
https://imp.i384100.net/WyNqoM
2️⃣ Master Excel for Data Analysis
Excel is an essential tool for data cleaning, analysis, and visualization.
📘 Free Resources:
1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun
2. Chandoo.org: https://chandoo.org/
🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF.
3️⃣ Learn SQL for Data Queries
SQL is the language of data—used to retrieve and manipulate datasets.
📘 Free Resources:
1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/
2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/
🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets.
4️⃣ Get Hands-On with Data Visualization
Learn to communicate insights visually with tools like Tableau or Power BI.
📘 Free Resources:
1. Tableau Public: https://www.tableau.com/learn/training
2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog
🎯 Practice: Create dashboards to tell stories using real datasets.
5️⃣ Dive into Python or R for Analytics
Coding isn’t mandatory, but Python or R can open up advanced analytics.
📘 Free Resources:
1. Google’s Python Course https://developers.google.com/edu/python
2. R for Data Science (free book) r4ds.had.co.nz
🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data.
6️⃣ Work on Real Projects
Apply your skills to real-world datasets to build your portfolio.
📘 Free Resources:
Kaggle: Datasets and beginner-friendly competitions.
Google Dataset Search: Access datasets on any topic.
🎯 Project Ideas:
Analyze sales data and create a dashboard.
Predict customer churn using a public dataset.
7️⃣ Build Your Portfolio and Network
Showcase your projects and connect with others in the field.
📘 Tips:
→ Use GitHub to share your work.
→ Create LinkedIn posts about your learning journey.
→ Join forums like r/DataScience on Reddit or LinkedIn groups.
💡 Start small, use free resources, and keep building.
💡 Remember: Every small step adds up to big progress.
Becoming a data analyst doesn’t have to be expensive in 2025.
With the right free resources and a structured approach,
you can become a skilled data analyst.
Here’s a roadmap with free resources to guide your journey:
1️⃣ Learn the Basics of Data Analytics
Start with foundational concepts like:
↳ What is data analytics?
↳ Types of analytics (denoscriptive, predictive, prenoscriptive).
↳ Basics of data types and statistics.
📘 Free Resources:
1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability
2. Introduction to Data Analytics by IBM (audit for free) :
https://imp.i384100.net/WyNqoM
2️⃣ Master Excel for Data Analysis
Excel is an essential tool for data cleaning, analysis, and visualization.
📘 Free Resources:
1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun
2. Chandoo.org: https://chandoo.org/
🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF.
3️⃣ Learn SQL for Data Queries
SQL is the language of data—used to retrieve and manipulate datasets.
📘 Free Resources:
1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/
2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/
🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets.
4️⃣ Get Hands-On with Data Visualization
Learn to communicate insights visually with tools like Tableau or Power BI.
📘 Free Resources:
1. Tableau Public: https://www.tableau.com/learn/training
2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog
🎯 Practice: Create dashboards to tell stories using real datasets.
5️⃣ Dive into Python or R for Analytics
Coding isn’t mandatory, but Python or R can open up advanced analytics.
📘 Free Resources:
1. Google’s Python Course https://developers.google.com/edu/python
2. R for Data Science (free book) r4ds.had.co.nz
🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data.
6️⃣ Work on Real Projects
Apply your skills to real-world datasets to build your portfolio.
📘 Free Resources:
Kaggle: Datasets and beginner-friendly competitions.
Google Dataset Search: Access datasets on any topic.
🎯 Project Ideas:
Analyze sales data and create a dashboard.
Predict customer churn using a public dataset.
7️⃣ Build Your Portfolio and Network
Showcase your projects and connect with others in the field.
📘 Tips:
→ Use GitHub to share your work.
→ Create LinkedIn posts about your learning journey.
→ Join forums like r/DataScience on Reddit or LinkedIn groups.
💡 Start small, use free resources, and keep building.
💡 Remember: Every small step adds up to big progress.
👍8
Hey Guys👋,
The Average Salary Of a Data Scientist is 14LPA
𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍
We help you master the required skills.
Learn by doing, build Industry level projects
👩🎓 1500+ Students Placed
💼 7.2 LPA Avg. Package
💰 41 LPA Highest Package
🤝 450+ Hiring Partners
Apply for FREE👇 :
https://tracking.acciojob.com/g/PUfdDxgHR
( Limited Slots )
The Average Salary Of a Data Scientist is 14LPA
𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬😍
We help you master the required skills.
Learn by doing, build Industry level projects
👩🎓 1500+ Students Placed
💼 7.2 LPA Avg. Package
💰 41 LPA Highest Package
🤝 450+ Hiring Partners
Apply for FREE👇 :
https://tracking.acciojob.com/g/PUfdDxgHR
( Limited Slots )
❤4👍1
𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?
𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
Like if it helps :)
𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
Like if it helps :)
👍5❤3
Seaborn Cheatsheet ✅
🔥3❤1
Python Interview Questions for data analyst interview
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
👍6❤1
Data Analyst Interview Questions
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
ENJOY LEARNING 👍👍
[Python, SQL, PowerBI]
1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.
4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI
ENJOY LEARNING 👍👍
👍4❤1
The best way to learn data analytics skills is to:
1. Watch a tutorial
2. Immediately practice what you just learned
3. Do projects to apply your learning to real-life applications
If you only watch videos and never practice, you won’t retain any of your teaching.
If you never apply your learning with projects, you won’t be able to solve problems on the job. (You also will have a much harder time attracting recruiters without a recruiter.)
1. Watch a tutorial
2. Immediately practice what you just learned
3. Do projects to apply your learning to real-life applications
If you only watch videos and never practice, you won’t retain any of your teaching.
If you never apply your learning with projects, you won’t be able to solve problems on the job. (You also will have a much harder time attracting recruiters without a recruiter.)
👍5🔥1
Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling 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.
Like for more 😄❤️
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting - Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling 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.
Like for more 😄❤️
👍5❤3
Hey guys,
Here are some best Telegram Channels for free education in 2025
👇👇
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
ChatGPT Prompts
AI Projects
Free access to all the Paid Channels
👇👇
https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
Do react with ♥️ if you need more content like this
ENJOY LEARNING 👍👍
Here are some best Telegram Channels for free education in 2025
👇👇
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
ChatGPT Prompts
AI Projects
Free access to all the Paid Channels
👇👇
https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
Do react with ♥️ if you need more content like this
ENJOY LEARNING 👍👍
❤2👍2
Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
👍4❤2
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 denoscriptive 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!
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive 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!
👍2
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!
👇👇
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!
❤2👍2