Becoming a Data Analyst in 2025 is more difficult than it was a couple of years ago. The competition has grown but so has the demand for Data Analysts!
There are 5 areas you need to excel at to land a career in data. (so punny...)
1. Skills
2. Experience
3. Networking
4. Job Search
5. Education
Let's dive into the first and most important area, skills.
Skills
Every data analytics job will require a different set of skills for their job denoscription. To cover the majority of entry-level positions, you should focus on the core 3 (or 4 if you have time).
- Excel
- SQL
- Tableau or Power BI
- Python or R(optional)
No need to learn any more than this to get started. Start learning other skills AFTER you land your first job and see what data analytics path you really enjoy.
You might fall into a path that doesn't require Python at all and if you took 3 months to learn it, you wasted 3 months. Your goal should be to get your foot in the door.
Experience
So how do you show that you have experience if you have never worked as a Data Analyst professionally?
It's actually easier than you think!
There are a few ways you can gain experience. volunteer, freelance, or any analytics work at your current job.
First ask your friends, family, or even Reddit if anyone needs help with their data.
Second, you can join Upwork or Fiverr to land some freelance gigs to gain great experience and some extra money.
Thirdly, even if your noscript isn't "Data Analyst", you might analyze data anyway. Use this as experience!
Networking
I love this section the most. It has been proven by everyone I have mentored that this is one of the most important areas to learn.
Start talking to other Data Analysts, start connecting with the RIGHT people, start posting on LinkedIn, start following people in the field, and start commenting on posts.
All of this, over time, will continue to get "eyes" on your profile. This will lead to more calls, interviews, and like the people I teach, job offers.
Consistency is important here.
Job Search
I believe this is not a skill and is more like a "numbers game". And the ones who excel here, are the ones who are consistent.
I'm not saying you need to apply all day every day but you should spend SOME time applying every day.
This is important because you don't know when exactly a company will be posting their job posting. You also want to be one of the first people to apply so that means you need to check the job boards in multiple small chunks rather than spend all of your time applying in a single chunk of time.
The best way to do this is to open up all of the filters and select the most recent and posted within the last 3 days.
Education
If you have a degree or are currently on your way to getting one, this section doesn't really apply to you since you have a leg up on a lot more job opportunities.
So how else does someone show they are educated enough to become a Data Analyst?
You need to prove it by taking relevant courses in relation to the industry you want to enter. After the course, the actual certificate does not hold much weight unless it's an accredited certificate like a Tableau Professional Certificate.
To counter this, you need to use your project denoscriptions to explain how you used data to solve a business problem and explain it professionally.
There are so many other areas you could work on but focussing on these to start will definitely get you going in the right direction.
Take time to put these actions to work. Pivot when something isn't working and adapt.
It will take time but these actions will reduce the time it takes you to become a Data Analyst in 2025
Hope this helps you 😊
There are 5 areas you need to excel at to land a career in data. (so punny...)
1. Skills
2. Experience
3. Networking
4. Job Search
5. Education
Let's dive into the first and most important area, skills.
Skills
Every data analytics job will require a different set of skills for their job denoscription. To cover the majority of entry-level positions, you should focus on the core 3 (or 4 if you have time).
- Excel
- SQL
- Tableau or Power BI
- Python or R(optional)
No need to learn any more than this to get started. Start learning other skills AFTER you land your first job and see what data analytics path you really enjoy.
You might fall into a path that doesn't require Python at all and if you took 3 months to learn it, you wasted 3 months. Your goal should be to get your foot in the door.
Experience
So how do you show that you have experience if you have never worked as a Data Analyst professionally?
It's actually easier than you think!
There are a few ways you can gain experience. volunteer, freelance, or any analytics work at your current job.
First ask your friends, family, or even Reddit if anyone needs help with their data.
Second, you can join Upwork or Fiverr to land some freelance gigs to gain great experience and some extra money.
Thirdly, even if your noscript isn't "Data Analyst", you might analyze data anyway. Use this as experience!
Networking
I love this section the most. It has been proven by everyone I have mentored that this is one of the most important areas to learn.
Start talking to other Data Analysts, start connecting with the RIGHT people, start posting on LinkedIn, start following people in the field, and start commenting on posts.
All of this, over time, will continue to get "eyes" on your profile. This will lead to more calls, interviews, and like the people I teach, job offers.
Consistency is important here.
Job Search
I believe this is not a skill and is more like a "numbers game". And the ones who excel here, are the ones who are consistent.
I'm not saying you need to apply all day every day but you should spend SOME time applying every day.
This is important because you don't know when exactly a company will be posting their job posting. You also want to be one of the first people to apply so that means you need to check the job boards in multiple small chunks rather than spend all of your time applying in a single chunk of time.
The best way to do this is to open up all of the filters and select the most recent and posted within the last 3 days.
Education
If you have a degree or are currently on your way to getting one, this section doesn't really apply to you since you have a leg up on a lot more job opportunities.
So how else does someone show they are educated enough to become a Data Analyst?
You need to prove it by taking relevant courses in relation to the industry you want to enter. After the course, the actual certificate does not hold much weight unless it's an accredited certificate like a Tableau Professional Certificate.
To counter this, you need to use your project denoscriptions to explain how you used data to solve a business problem and explain it professionally.
There are so many other areas you could work on but focussing on these to start will definitely get you going in the right direction.
Take time to put these actions to work. Pivot when something isn't working and adapt.
It will take time but these actions will reduce the time it takes you to become a Data Analyst in 2025
Hope this helps you 😊
👍23❤10
Data Analytics
Data Analyst Interview Part-8 How do you perform data cleaning in Python? Data cleaning in Python involves several steps: Handling missing data: Drop missing values: df.dropna() Fill missing values: df.fillna(value) Removing duplicates: df.drop_duplicates()…
Data Analyst Interview Part-9
How do you perform joins in Power BI using relationships?
In Power BI, joins are handled through relationships between tables instead of traditional SQL joins. You can create relationships using the Model View, where you define one-to-one, one-to-many, or many-to-many relationships. Power BI automatically determines the best relationship based on column values, but you can modify the cardinality and cross-filter direction to control how data is connected across tables.
What are some common aggregate functions in Excel?
Aggregate functions summarize data in Excel. Common ones include:
SUM: Adds values in a range.
AVERAGE: Calculates the mean.
COUNT: Counts the number of non-empty cells.
MAX/MIN: Finds the highest and lowest values.
MEDIAN: Returns the middle value of a dataset.
STDEV: Measures data variation (Standard Deviation).
These functions are commonly used in financial analysis, data validation, and reporting.
What are DAX functions in Power BI, and why are they important?
DAX (Data Analysis Expressions) functions help create custom calculations and measures in Power BI. They are important because they allow users to perform dynamic aggregations, conditional calculations, and time-based analysis. Key categories include:
Aggregation Functions: SUM, AVERAGE, COUNT
Filter Functions: FILTER, CALCULATE
Time Intelligence Functions: DATEADD, SAMEPERIODLASTYEAR
Logical Functions: IF, SWITCH
DAX enables advanced reporting and helps build meaningful insights from raw data.
What is data normalization, and why is it important?
Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It ensures efficient storage and retrieval by dividing large tables into smaller, related tables and using foreign keys to maintain relationships.
Benefits of normalization include:
Eliminates duplicate data
Improves consistency and accuracy
Enhances database performance
Reduces data anomalies
Normalization is crucial in relational databases to maintain a clean and scalable data structure.
What are some common data visualization best practices?
Effective data visualization helps communicate insights clearly. Best practices include:
Choose the right chart (e.g., bar charts for comparisons, line charts for trends).
Keep it simple (avoid unnecessary elements like 3D effects).
Use colors wisely (highlight key insights without overloading with colors).
Ensure data accuracy (labels, scales, and values must be correct).
Use interactive elements (filters, drill-downs in Power BI/Tableau).
Provide context (noscripts, legends, and annotations to explain findings).
Well-designed visualizations improve decision-making and help stakeholders understand data easily.
Like this post for if you want me to continue the interview series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
How do you perform joins in Power BI using relationships?
In Power BI, joins are handled through relationships between tables instead of traditional SQL joins. You can create relationships using the Model View, where you define one-to-one, one-to-many, or many-to-many relationships. Power BI automatically determines the best relationship based on column values, but you can modify the cardinality and cross-filter direction to control how data is connected across tables.
What are some common aggregate functions in Excel?
Aggregate functions summarize data in Excel. Common ones include:
SUM: Adds values in a range.
AVERAGE: Calculates the mean.
COUNT: Counts the number of non-empty cells.
MAX/MIN: Finds the highest and lowest values.
MEDIAN: Returns the middle value of a dataset.
STDEV: Measures data variation (Standard Deviation).
These functions are commonly used in financial analysis, data validation, and reporting.
What are DAX functions in Power BI, and why are they important?
DAX (Data Analysis Expressions) functions help create custom calculations and measures in Power BI. They are important because they allow users to perform dynamic aggregations, conditional calculations, and time-based analysis. Key categories include:
Aggregation Functions: SUM, AVERAGE, COUNT
Filter Functions: FILTER, CALCULATE
Time Intelligence Functions: DATEADD, SAMEPERIODLASTYEAR
Logical Functions: IF, SWITCH
DAX enables advanced reporting and helps build meaningful insights from raw data.
What is data normalization, and why is it important?
Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It ensures efficient storage and retrieval by dividing large tables into smaller, related tables and using foreign keys to maintain relationships.
Benefits of normalization include:
Eliminates duplicate data
Improves consistency and accuracy
Enhances database performance
Reduces data anomalies
Normalization is crucial in relational databases to maintain a clean and scalable data structure.
What are some common data visualization best practices?
Effective data visualization helps communicate insights clearly. Best practices include:
Choose the right chart (e.g., bar charts for comparisons, line charts for trends).
Keep it simple (avoid unnecessary elements like 3D effects).
Use colors wisely (highlight key insights without overloading with colors).
Ensure data accuracy (labels, scales, and values must be correct).
Use interactive elements (filters, drill-downs in Power BI/Tableau).
Provide context (noscripts, legends, and annotations to explain findings).
Well-designed visualizations improve decision-making and help stakeholders understand data easily.
Like this post for if you want me to continue the interview series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍11❤7🥰1
The key to starting your data analysis career:
❌It's not your education
❌It's not your experience
It's how you apply these principles:
1. Learn the job through "doing"
2. Build a portfolio
3. Make yourself known
No one starts an expert, but everyone can become one.
If you're looking for a career in data analysis, start by:
⟶ Watching videos
⟶ Reading experts advice
⟶ Doing internships
⟶ Building a portfolio
⟶ Learning from seniors
You'll be amazed at how fast you'll learn and how quickly you'll become an expert.
So, start today and let the data analysis career begin
Hope it helps :)
❌It's not your education
❌It's not your experience
It's how you apply these principles:
1. Learn the job through "doing"
2. Build a portfolio
3. Make yourself known
No one starts an expert, but everyone can become one.
If you're looking for a career in data analysis, start by:
⟶ Watching videos
⟶ Reading experts advice
⟶ Doing internships
⟶ Building a portfolio
⟶ Learning from seniors
You'll be amazed at how fast you'll learn and how quickly you'll become an expert.
So, start today and let the data analysis career begin
Hope it helps :)
❤8👍7
What seperates a good 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 from a great one?
The journey to becoming an exceptional data analyst requires mastering a blend of technical and soft skills.
☑ Technical skills:
- Querying Data with SQL
- Data Visualization (Tableau/PowerBI)
- Data Storytelling and Reporting
- Data Exploration and Analytics
- Data Modeling
☑ Soft Skills:
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Critical Thinking
- Learning Mindset
But how do you develop these soft skills?
◆ Tackle real-world data projects or case studies. The more complex, the better.
◆ Practice explaining your analysis to non-technical audiences. If they understand, you’ve nailed it!
◆ Learn how industries use data for decision-making. Align your analysis with business outcomes.
◆ Stay curious, ask 'why,' and dig deeper into your data. Don’t settle for surface-level insights.
◆ Keep evolving. Attend webinars, read books, or engage with industry experts regularly.
The journey to becoming an exceptional data analyst requires mastering a blend of technical and soft skills.
☑ Technical skills:
- Querying Data with SQL
- Data Visualization (Tableau/PowerBI)
- Data Storytelling and Reporting
- Data Exploration and Analytics
- Data Modeling
☑ Soft Skills:
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Critical Thinking
- Learning Mindset
But how do you develop these soft skills?
◆ Tackle real-world data projects or case studies. The more complex, the better.
◆ Practice explaining your analysis to non-technical audiences. If they understand, you’ve nailed it!
◆ Learn how industries use data for decision-making. Align your analysis with business outcomes.
◆ Stay curious, ask 'why,' and dig deeper into your data. Don’t settle for surface-level insights.
◆ Keep evolving. Attend webinars, read books, or engage with industry experts regularly.
👍10❤5👌2
🚀 How to Land a Data Analyst Job Without Experience?
Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend:
✅ Step 1: Master the Essential Skills
You need to build a strong foundation in:
🔹 SQL – Learn how to extract and manipulate data
🔹 Excel – Master formulas, Pivot Tables, and dashboards
🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis
🔹 Power BI/Tableau – Learn to create interactive dashboards
🔹 Statistics & Business Acumen – Understand data trends and insights
Where to learn?
📌 Google Data Analytics Course
📌 SQL – Mode Analytics (Free)
📌 Python – Kaggle or DataCamp
✅ Step 2: Work on Real-World Projects
Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills.
🔹 Project Ideas:
✅ Analyze sales data to find profitable products
✅ Clean messy datasets using SQL or Python
✅ Build an interactive Power BI dashboard
✅ Predict customer churn using machine learning (optional)
Use Kaggle, Data.gov, or Google Dataset Search to find free datasets!
✅ Step 3: Build an Impressive Portfolio
Once you have projects, showcase them! Create:
📌 A GitHub repository to store your SQL/Python code
📌 A Tableau or Power BI Public Profile for dashboards
📌 A Medium or LinkedIn post explaining your projects
A strong portfolio = More job opportunities! 💡
✅ Step 4: Get Hands-On Experience
If you don’t have experience, create your own!
📌 Do freelance projects on Upwork/Fiverr
📌 Join an internship or volunteer for NGOs
📌 Participate in Kaggle competitions
📌 Contribute to open-source projects
Real-world practice > Theoretical knowledge!
✅ Step 5: Optimize Your Resume & LinkedIn Profile
Your resume should highlight:
✔️ Skills (SQL, Python, Power BI, etc.)
✔️ Projects (Brief denoscriptions with links)
✔️ Certifications (Google Data Analytics, Coursera, etc.)
Bonus Tip:
🔹 Write "Data Analyst in Training" on LinkedIn
🔹 Start posting insights from your learning journey
🔹 Engage with recruiters & join LinkedIn groups
✅ Step 6: Start Applying for Jobs
Don’t wait for the perfect job—start applying!
📌 Apply on LinkedIn, Indeed, and company websites
📌 Network with professionals in the industry
📌 Be ready for SQL & Excel assessments
Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts.
You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer!
🔥 Your Challenge: Start your first project today and track your progress!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend:
✅ Step 1: Master the Essential Skills
You need to build a strong foundation in:
🔹 SQL – Learn how to extract and manipulate data
🔹 Excel – Master formulas, Pivot Tables, and dashboards
🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis
🔹 Power BI/Tableau – Learn to create interactive dashboards
🔹 Statistics & Business Acumen – Understand data trends and insights
Where to learn?
📌 Google Data Analytics Course
📌 SQL – Mode Analytics (Free)
📌 Python – Kaggle or DataCamp
✅ Step 2: Work on Real-World Projects
Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills.
🔹 Project Ideas:
✅ Analyze sales data to find profitable products
✅ Clean messy datasets using SQL or Python
✅ Build an interactive Power BI dashboard
✅ Predict customer churn using machine learning (optional)
Use Kaggle, Data.gov, or Google Dataset Search to find free datasets!
✅ Step 3: Build an Impressive Portfolio
Once you have projects, showcase them! Create:
📌 A GitHub repository to store your SQL/Python code
📌 A Tableau or Power BI Public Profile for dashboards
📌 A Medium or LinkedIn post explaining your projects
A strong portfolio = More job opportunities! 💡
✅ Step 4: Get Hands-On Experience
If you don’t have experience, create your own!
📌 Do freelance projects on Upwork/Fiverr
📌 Join an internship or volunteer for NGOs
📌 Participate in Kaggle competitions
📌 Contribute to open-source projects
Real-world practice > Theoretical knowledge!
✅ Step 5: Optimize Your Resume & LinkedIn Profile
Your resume should highlight:
✔️ Skills (SQL, Python, Power BI, etc.)
✔️ Projects (Brief denoscriptions with links)
✔️ Certifications (Google Data Analytics, Coursera, etc.)
Bonus Tip:
🔹 Write "Data Analyst in Training" on LinkedIn
🔹 Start posting insights from your learning journey
🔹 Engage with recruiters & join LinkedIn groups
✅ Step 6: Start Applying for Jobs
Don’t wait for the perfect job—start applying!
📌 Apply on LinkedIn, Indeed, and company websites
📌 Network with professionals in the industry
📌 Be ready for SQL & Excel assessments
Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts.
You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer!
🔥 Your Challenge: Start your first project today and track your progress!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍21❤8👏2🔥1
𝟓 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐀𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐉𝐨𝐛𝐬
🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.
🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t send the same resume to every job.
Take time to highlight the skills and tools that the job denoscription asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.
Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.
🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.
🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t send the same resume to every job.
Take time to highlight the skills and tools that the job denoscription asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.
Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.
🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
👍19
I see so many people jump into data analytics, excited by its popularity, only to feel lost or uninterested soon after. I get it, data isn’t for everyone, and that’s okay.
Data analytics requires a certain spark or say curiosity. You need that drive to dig deeper, to understand why things happen, to explore how data pieces connect to reveal a bigger picture. Without that spark, it’s easy to feel overwhelmed or even bored.
Before diving in, ask yourself, Do I really enjoy solving puzzles? Am I genuinely excited about numbers, patterns, and insights? If you’re curious and love learning, data can be incredibly rewarding. But if it’s just about following a trend, it might not be a fulfilling path for you.
Be honest with yourself. Find your passion, whether it’s in data or somewhere else and invest in something that truly excites you.
Hope this helps you 😊
Data analytics requires a certain spark or say curiosity. You need that drive to dig deeper, to understand why things happen, to explore how data pieces connect to reveal a bigger picture. Without that spark, it’s easy to feel overwhelmed or even bored.
Before diving in, ask yourself, Do I really enjoy solving puzzles? Am I genuinely excited about numbers, patterns, and insights? If you’re curious and love learning, data can be incredibly rewarding. But if it’s just about following a trend, it might not be a fulfilling path for you.
Be honest with yourself. Find your passion, whether it’s in data or somewhere else and invest in something that truly excites you.
Hope this helps you 😊
👍31❤17👎1🔥1
As a data analytics enthusiast, the end goal is not just to learn SQL, Power BI, Python, Excel, etc. but to get a job as a Data Analyst👨💻
Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.
Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job denoscription to curate the resume accordingly and searching for profile of people who can refer me for the role.
📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
📍To get into data analytics, applying for jobs is as important as learning and upskilling.
If you are not applying for the jobs, you are simply delaying your success to get into data analytics👨💻📊
Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.
Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job denoscription to curate the resume accordingly and searching for profile of people who can refer me for the role.
📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
📍To get into data analytics, applying for jobs is as important as learning and upskilling.
If you are not applying for the jobs, you are simply delaying your success to get into data analytics👨💻📊
Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
👍18🥰4🎉2❤1
Power BI Learning Plan in 2025
|-- Week 1: Introduction to Power BI
| |-- Power BI Basics
| | |-- What is Power BI?
| | |-- Components of Power BI
| | |-- Power BI Desktop vs. Power BI Service
| |-- Setting up Power BI
| | |-- Installing Power BI Desktop
| | |-- Overview of the Interface
| | |-- Connecting to Data Sources
| |-- First Power BI Report
| | |-- Creating a Simple Report
| | |-- Basic Visualizations
|
|-- Week 2: Data Transformation and Modeling
| |-- Power Query Editor
| | |-- Importing and Shaping Data
| | |-- Applied Steps
| |-- Data Modeling
| | |-- Relationships
| | |-- Calculated Columns and Measures
| | |-- DAX Basics
| |-- Data Cleaning
| | |-- Handling Missing Data
| | |-- Data Types and Formatting
|
|-- Week 3: Advanced DAX and Data Modeling
| |-- Advanced DAX Functions
| | |-- Time Intelligence
| | |-- Iterators
| | |-- Filter Functions
| |-- Advanced Data Modeling
| | |-- Star and Snowflake Schemas
| | |-- Role-playing Dimensions
| |-- Performance Optimization
| | |-- Query Performance
| | |-- Model Performance
|
|-- Week 4: Visualizations and Reports
| |-- Advanced Visualizations
| | |-- Custom Visuals
| | |-- Conditional Formatting
| | |-- Interactive Elements
| |-- Report Design
| | |-- Designing for Clarity
| | |-- Using Themes
| | |-- Report Navigation
| |-- Power BI Service
| | |-- Publishing Reports
| | |-- Workspaces and Apps
| | |-- Sharing and Collaboration
|
|-- Week 5: Dashboards and Data Analysis
| |-- Creating Dashboards
| | |-- Pinning Visuals
| | |-- Dashboard Tiles
| | |-- Alerts
| |-- Data Analysis Techniques
| | |-- Drillthrough
| | |-- Bookmarks
| | |-- What-If Parameters
| |-- Advanced Analytics
| | |-- Quick Insights
| | |-- AI Visuals
|
|-- Week 6-8: Power BI and Other Tools
| |-- Power BI and Excel
| | |-- Excel Integration
| | |-- PowerPivot and PowerQuery
| | |-- Publishing from Excel
| |-- Power BI and R
| | |-- Using R Scripts in Power BI
| | |-- R Visuals
| |-- Power BI and Python
| | |-- Using Python Scripts
| | |-- Python Visuals
| |-- Power Automate and Power BI
| | |-- Automating Workflows
| | |-- Data Alerts and Actions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing the Model
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- SQL Databases
| | |-- Azure Data Services
|
|-- Week 12: Post-Project Learning
| |-- Power BI Administration
| | |-- Data Governance
| | |-- Security
| | |-- Monitoring and Auditing
| |-- Power BI in the Cloud
| | |-- Power BI Premium
| | |-- Power BI Embedded
| |-- Continuing Education
| | |-- Advanced Power BI Topics
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udacity)
| |-- Books (The Definitive Guide to DAX, Microsoft Power BI Cookbook)
| |-- GitHub Repositories
| |-- Power BI Communities (Microsoft Power BI Community, Reddit)
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Power BI
| |-- Power BI Basics
| | |-- What is Power BI?
| | |-- Components of Power BI
| | |-- Power BI Desktop vs. Power BI Service
| |-- Setting up Power BI
| | |-- Installing Power BI Desktop
| | |-- Overview of the Interface
| | |-- Connecting to Data Sources
| |-- First Power BI Report
| | |-- Creating a Simple Report
| | |-- Basic Visualizations
|
|-- Week 2: Data Transformation and Modeling
| |-- Power Query Editor
| | |-- Importing and Shaping Data
| | |-- Applied Steps
| |-- Data Modeling
| | |-- Relationships
| | |-- Calculated Columns and Measures
| | |-- DAX Basics
| |-- Data Cleaning
| | |-- Handling Missing Data
| | |-- Data Types and Formatting
|
|-- Week 3: Advanced DAX and Data Modeling
| |-- Advanced DAX Functions
| | |-- Time Intelligence
| | |-- Iterators
| | |-- Filter Functions
| |-- Advanced Data Modeling
| | |-- Star and Snowflake Schemas
| | |-- Role-playing Dimensions
| |-- Performance Optimization
| | |-- Query Performance
| | |-- Model Performance
|
|-- Week 4: Visualizations and Reports
| |-- Advanced Visualizations
| | |-- Custom Visuals
| | |-- Conditional Formatting
| | |-- Interactive Elements
| |-- Report Design
| | |-- Designing for Clarity
| | |-- Using Themes
| | |-- Report Navigation
| |-- Power BI Service
| | |-- Publishing Reports
| | |-- Workspaces and Apps
| | |-- Sharing and Collaboration
|
|-- Week 5: Dashboards and Data Analysis
| |-- Creating Dashboards
| | |-- Pinning Visuals
| | |-- Dashboard Tiles
| | |-- Alerts
| |-- Data Analysis Techniques
| | |-- Drillthrough
| | |-- Bookmarks
| | |-- What-If Parameters
| |-- Advanced Analytics
| | |-- Quick Insights
| | |-- AI Visuals
|
|-- Week 6-8: Power BI and Other Tools
| |-- Power BI and Excel
| | |-- Excel Integration
| | |-- PowerPivot and PowerQuery
| | |-- Publishing from Excel
| |-- Power BI and R
| | |-- Using R Scripts in Power BI
| | |-- R Visuals
| |-- Power BI and Python
| | |-- Using Python Scripts
| | |-- Python Visuals
| |-- Power Automate and Power BI
| | |-- Automating Workflows
| | |-- Data Alerts and Actions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing the Model
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- SQL Databases
| | |-- Azure Data Services
|
|-- Week 12: Post-Project Learning
| |-- Power BI Administration
| | |-- Data Governance
| | |-- Security
| | |-- Monitoring and Auditing
| |-- Power BI in the Cloud
| | |-- Power BI Premium
| | |-- Power BI Embedded
| |-- Continuing Education
| | |-- Advanced Power BI Topics
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udacity)
| |-- Books (The Definitive Guide to DAX, Microsoft Power BI Cookbook)
| |-- GitHub Repositories
| |-- Power BI Communities (Microsoft Power BI Community, Reddit)
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍27❤13
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍23❤11
Hi guys,
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Free Data Analytics Resources 👇
https://news.1rj.ru/str/datasimplifier
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Free Data Analytics Resources 👇
https://news.1rj.ru/str/datasimplifier
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
👍15❤13
Complete SQL Topics for Data Analysts 😄👇
1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables
2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY
3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables
4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN
5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses
6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback
7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)
8. Indexes:
- Creating and managing indexes for performance optimization
9. Views:
- Creating and using views for simplified querying
10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions
11. Normalization:
- Understanding database normalization concepts
12. Data Import and Export:
- Importing and exporting data using SQL
13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others
14. Advanced Filtering:
- Using CASE statements for conditional logic
15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios
16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics
17. Working with Dates and Times:
- Date and time functions and formatting
18. Performance Tuning:
- Query optimization strategies
19. Security:
- Understanding SQL injection and best practices for security
20. Handling NULL Values:
- Dealing with NULL values in queries
Ensure hands-on practice on these topics to strengthen your SQL skills.
Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables
2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY
3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables
4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN
5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses
6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback
7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)
8. Indexes:
- Creating and managing indexes for performance optimization
9. Views:
- Creating and using views for simplified querying
10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions
11. Normalization:
- Understanding database normalization concepts
12. Data Import and Export:
- Importing and exporting data using SQL
13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others
14. Advanced Filtering:
- Using CASE statements for conditional logic
15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios
16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics
17. Working with Dates and Times:
- Date and time functions and formatting
18. Performance Tuning:
- Query optimization strategies
19. Security:
- Understanding SQL injection and best practices for security
20. Handling NULL Values:
- Dealing with NULL values in queries
Ensure hands-on practice on these topics to strengthen your SQL skills.
Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍18❤15
📊 Excel Hack of the Week
Did you know you can use Flash Fill in Excel to automatically clean and format data without writing formulas?
📝 How to Use Flash Fill?
1️⃣ Type the first correct value manually in the adjacent column.
2️⃣ Press Ctrl + E (or go to Data > Flash Fill).
3️⃣ Excel will recognize the pattern and fill in the rest automatically!
🔍 Example:
✅ Extract first names from "John Doe" → Type "John" → Press Ctrl + E → Done!
✅ Format phone numbers from "1234567890" to "(123) 456-7890" in seconds!
✅ Convert dates from "01-02-2024" to "February 1, 2024" instantly!
📌 Bonus: Try using Flash Fill for splitting names, fixing email formats, or even extracting numbers from text.
You can join @excel_data for free Excel Resources.
Like this post for more data analytics tricks 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Did you know you can use Flash Fill in Excel to automatically clean and format data without writing formulas?
📝 How to Use Flash Fill?
1️⃣ Type the first correct value manually in the adjacent column.
2️⃣ Press Ctrl + E (or go to Data > Flash Fill).
3️⃣ Excel will recognize the pattern and fill in the rest automatically!
🔍 Example:
✅ Extract first names from "John Doe" → Type "John" → Press Ctrl + E → Done!
✅ Format phone numbers from "1234567890" to "(123) 456-7890" in seconds!
✅ Convert dates from "01-02-2024" to "February 1, 2024" instantly!
📌 Bonus: Try using Flash Fill for splitting names, fixing email formats, or even extracting numbers from text.
You can join @excel_data for free Excel Resources.
Like this post for more data analytics tricks 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤8👍7🔥2
Python for Data Analysis: Must-Know Libraries 👇👇
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
🔥 Essential Python Libraries for Data Analysis:
✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
📌 Example: Loading a CSV file and displaying the first 5 rows:
✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
📌 Example: Creating an array and performing basic operations:
✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
📌 Example: Creating a basic bar chart:
✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
💡 Challenge for You!
Try writing a Python noscript that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization
React with ♥️ if you want me to post the noscript for above challenge! ⬇️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.
🔥 Essential Python Libraries for Data Analysis:
✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.
📌 Example: Loading a CSV file and displaying the first 5 rows:
import pandas as pd df = pd.read_csv('data.csv') print(df.head()) ✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.
📌 Example: Creating an array and performing basic operations:
import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average
✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.
📌 Example: Creating a basic bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()
✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.
✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.
💡 Challenge for You!
Try writing a Python noscript that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization
React with ♥️ if you want me to post the noscript for above challenge! ⬇️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍15❤11🎉1
🔍 Real-World Data Analyst Tasks & How to Solve Them
As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!
📌 Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
✅ Solution (Using Pandas in Python):
💡 Tip: Always check for inconsistent spellings and incorrect date formats!
📌 Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
✅ Solution (Using SQL):
💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!
📌 Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
✅ Solution (Using Power BI / Tableau):
👉 Add KPI Cards to show total sales & profit
👉 Use a Line Chart for monthly trends
👉 Create a Bar Chart for top-selling products
👉 Use Filters/Slicers for better interactivity
💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!
📌 Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
✅ Solution (Using Pandas in Python):
import pandas as pd
df = pd.read_csv('sales_data.csv')
df.drop_duplicates(inplace=True) # Remove duplicate rows
df.fillna(0, inplace=True) # Fill missing values with 0
print(df.head())
💡 Tip: Always check for inconsistent spellings and incorrect date formats!
📌 Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
✅ Solution (Using SQL):
SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue
FROM Sales
GROUP BY MONTH(SaleDate)
ORDER BY Total_Revenue DESC;
💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!
📌 Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
✅ Solution (Using Power BI / Tableau):
👉 Add KPI Cards to show total sales & profit
👉 Use a Line Chart for monthly trends
👉 Create a Bar Chart for top-selling products
👉 Use Filters/Slicers for better interactivity
💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤19👍12🥰1
📊 Power BI / Tableau Dashboard Inspiration
🚀 Want to Build Stunning Dashboards? Try This!
Creating an interactive and insightful dashboard is a key skill for any Data Analyst. Here’s a simple Power BI / Tableau dashboard idea to practice!
📝 Project Idea: Sales Performance Dashboard
📌 Dataset: Use free datasets from Kaggle or Sample Superstore (Tableau)
📌 Key Visuals to Include:
✅ Total Sales, Profit, and Orders (KPI Cards)
✅ Sales Trend Over Time (Line Chart)
✅ Top 5 Best-Selling Products (Bar Chart)
✅ Sales by Region & Category (Map & Pie Chart)
✅ Customer Segmentation (Filters & Slicers)
💡 Pro Tips:
🔹 Use conditional formatting to highlight trends 📊
🔹 Add slicers to make the dashboard interactive 🔍
🔹 Keep colors consistent for better readability 🎨
📌 Bonus Challenge: Can you create a drill-through feature to view details by region?
Join @dataportfolio to find free data analytics projects
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
🚀 Want to Build Stunning Dashboards? Try This!
Creating an interactive and insightful dashboard is a key skill for any Data Analyst. Here’s a simple Power BI / Tableau dashboard idea to practice!
📝 Project Idea: Sales Performance Dashboard
📌 Dataset: Use free datasets from Kaggle or Sample Superstore (Tableau)
📌 Key Visuals to Include:
✅ Total Sales, Profit, and Orders (KPI Cards)
✅ Sales Trend Over Time (Line Chart)
✅ Top 5 Best-Selling Products (Bar Chart)
✅ Sales by Region & Category (Map & Pie Chart)
✅ Customer Segmentation (Filters & Slicers)
💡 Pro Tips:
🔹 Use conditional formatting to highlight trends 📊
🔹 Add slicers to make the dashboard interactive 🔍
🔹 Keep colors consistent for better readability 🎨
📌 Bonus Challenge: Can you create a drill-through feature to view details by region?
Join @dataportfolio to find free data analytics projects
Like this post for more content like this ♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍16❤10👏2
🎯 Top 20 SQL Interview Questions You Must Know
SQL is one of the most in-demand skills for Data Analysts.
Here are 20 SQL interview questions that frequently appear in job interviews.
📌 Basic SQL Questions
1️⃣ What is the difference between INNER JOIN and LEFT JOIN?
2️⃣ How does GROUP BY work, and why do we use it?
3️⃣ What is the difference between HAVING and WHERE?
4️⃣ How do you remove duplicate rows from a table?
5️⃣ What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?
📌 Intermediate SQL Questions
6️⃣ How do you find the second highest salary from an Employee table?
7️⃣ What is a Common Table Expression (CTE), and when should you use it?
8️⃣ How do you identify missing values in a dataset using SQL?
9️⃣ What is the difference between UNION and UNION ALL?
🔟 How do you calculate a running total in SQL?
📌 Advanced SQL Questions
1️⃣1️⃣ How does a self-join work? Give an example.
1️⃣2️⃣ What is a window function, and how is it different from GROUP BY?
1️⃣3️⃣ How do you detect and remove duplicate records in SQL?
1️⃣4️⃣ Explain the difference between EXISTS and IN.
1️⃣5️⃣ What is the purpose of COALESCE()?
📌 Real-World SQL Scenarios
1️⃣6️⃣ How do you optimize a slow SQL query?
1️⃣7️⃣ What is indexing in SQL, and how does it improve performance?
1️⃣8️⃣ Write an SQL query to find customers who have placed more than 3 orders.
1️⃣9️⃣ How do you calculate the percentage of total sales for each category?
2️⃣0️⃣ What is the use of CASE statements in SQL?
React with ♥️ if you want me to post the correct answers in next posts! ⬇️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
SQL is one of the most in-demand skills for Data Analysts.
Here are 20 SQL interview questions that frequently appear in job interviews.
📌 Basic SQL Questions
1️⃣ What is the difference between INNER JOIN and LEFT JOIN?
2️⃣ How does GROUP BY work, and why do we use it?
3️⃣ What is the difference between HAVING and WHERE?
4️⃣ How do you remove duplicate rows from a table?
5️⃣ What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?
📌 Intermediate SQL Questions
6️⃣ How do you find the second highest salary from an Employee table?
7️⃣ What is a Common Table Expression (CTE), and when should you use it?
8️⃣ How do you identify missing values in a dataset using SQL?
9️⃣ What is the difference between UNION and UNION ALL?
🔟 How do you calculate a running total in SQL?
📌 Advanced SQL Questions
1️⃣1️⃣ How does a self-join work? Give an example.
1️⃣2️⃣ What is a window function, and how is it different from GROUP BY?
1️⃣3️⃣ How do you detect and remove duplicate records in SQL?
1️⃣4️⃣ Explain the difference between EXISTS and IN.
1️⃣5️⃣ What is the purpose of COALESCE()?
📌 Real-World SQL Scenarios
1️⃣6️⃣ How do you optimize a slow SQL query?
1️⃣7️⃣ What is indexing in SQL, and how does it improve performance?
1️⃣8️⃣ Write an SQL query to find customers who have placed more than 3 orders.
1️⃣9️⃣ How do you calculate the percentage of total sales for each category?
2️⃣0️⃣ What is the use of CASE statements in SQL?
React with ♥️ if you want me to post the correct answers in next posts! ⬇️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤48👍31🔥1👏1
Data Analytics
🎯 Top 20 SQL Interview Questions You Must Know SQL is one of the most in-demand skills for Data Analysts. Here are 20 SQL interview questions that frequently appear in job interviews. 📌 Basic SQL Questions 1️⃣ What is the difference between INNER JOIN…
SQL Interview Questions with detailed answers:
1️⃣ What is the difference between INNER JOIN and LEFT JOIN?
INNER JOIN: It returns only the rows where there is a match between both tables.
Example:
This will only return rows where an employee has a department.
LEFT JOIN: It returns all the rows from the left table, along with matching rows from the right table. If there is no match, NULL values will be returned for the right table.
Example:
This will return all employees, even if they don't belong to any department (NULL will be returned for department-related columns).
Like this post if you want me to continue posting all the answers 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1️⃣ What is the difference between INNER JOIN and LEFT JOIN?
INNER JOIN: It returns only the rows where there is a match between both tables.
Example:
SELECT * FROM employees INNER JOIN departments ON employees.department_id = departments.department_id;
This will only return rows where an employee has a department.
LEFT JOIN: It returns all the rows from the left table, along with matching rows from the right table. If there is no match, NULL values will be returned for the right table.
Example:
SELECT * FROM employees LEFT JOIN departments ON employees.department_id = departments.department_id;
This will return all employees, even if they don't belong to any department (NULL will be returned for department-related columns).
Like this post if you want me to continue posting all the answers 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤18👍18
SQL Interview Questions with detailed answers:
2️⃣ How does GROUP BY work, and why do we use it?
GROUP BY is used to arrange identical data into groups, often for performing aggregation functions (like COUNT, SUM, AVG, etc.) on each group. It's typically used with aggregate functions to summarize data.
Example:
Consider a sales table:
Explanation:
GROUP BY department_id: This groups all rows in the employees table by their department.
SUM(salary): This calculates the total salary for each department.
The result will show the department_id along with the corresponding total salary.
Why use GROUP BY?
It allows you to analyze data at different levels of granularity (e.g., department, region) by summarizing data in a meaningful way.
Like this post if you want me to continue this SQL Interview Series♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
2️⃣ How does GROUP BY work, and why do we use it?
GROUP BY is used to arrange identical data into groups, often for performing aggregation functions (like COUNT, SUM, AVG, etc.) on each group. It's typically used with aggregate functions to summarize data.
Example:
Consider a sales table:
SELECT department_id, SUM(salary) AS total_salary FROM employees GROUP BY department_id;
Explanation:
GROUP BY department_id: This groups all rows in the employees table by their department.
SUM(salary): This calculates the total salary for each department.
The result will show the department_id along with the corresponding total salary.
Why use GROUP BY?
It allows you to analyze data at different levels of granularity (e.g., department, region) by summarizing data in a meaningful way.
Like this post if you want me to continue this SQL Interview Series♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍22❤11
🧠 Case Study: How to Analyze a Business Problem Like a Pro
🚀 Want to solve real-world business problems? Here's how to approach it!
Data analysis isn’t just about writing queries or generating charts—it’s about solving business problems that drive key decisions.
Here’s a step-by-step guide to help you analyze business problems effectively:
📌 Step 1: Understand the Business Problem
First, understand the context. Speak with the stakeholders or team to clarify:
What is the business goal?
What data do you need to solve the problem?
What actions or decisions will the analysis lead to?
🔍 Example: A retail company wants to increase sales in a particular region. Your job is to identify the key factors affecting sales and come up with recommendations.
📌 Step 2: Gather the Right Data
After understanding the problem, ensure you have access to reliable data. This could include:
Sales data (transactions, customers, regions)
Marketing data (advertising campaigns, promotions)
External factors (economic conditions, competition)
🧠 Tip: Ensure data is clean and complete before analysis to avoid skewed results.
📌 Step 3: Analyze the Data
Now, dive into the data and perform the following tasks:
1. Data Exploration: Look for patterns, trends, and anomalies.
2. Hypothesis Testing: Identify possible causes of the problem (e.g., "Are promotions leading to an increase in sales?").
3. Segmentation Analysis: Break down the data by regions, products, customer types, etc. to identify key insights.
🧠 Example:
Use SQL to extract sales data by region and calculate monthly growth:
📌 Step 4: Visualize the Insights
Once you've analyzed the data, create visualizations to make the insights clear and actionable:
💹 Use line charts for trends over time.
📊 Use bar charts to compare different segments (regions, products, etc.).
🗺 Use heatmaps for geographical analysis.
💡 Tip: Keep your visualizations simple and focused on the key insights.
📌 Step 5: Provide Recommendations
Finally, based on your analysis, provide actionable recommendations to the business.
For example: “Focus promotions on Region X, where sales are consistently lower than other regions.”
“Increase marketing spend for the high-performing products.”
Free Resources for business analysts
👇👇
https://news.1rj.ru/str/analystcommunity
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
🚀 Want to solve real-world business problems? Here's how to approach it!
Data analysis isn’t just about writing queries or generating charts—it’s about solving business problems that drive key decisions.
Here’s a step-by-step guide to help you analyze business problems effectively:
📌 Step 1: Understand the Business Problem
First, understand the context. Speak with the stakeholders or team to clarify:
What is the business goal?
What data do you need to solve the problem?
What actions or decisions will the analysis lead to?
🔍 Example: A retail company wants to increase sales in a particular region. Your job is to identify the key factors affecting sales and come up with recommendations.
📌 Step 2: Gather the Right Data
After understanding the problem, ensure you have access to reliable data. This could include:
Sales data (transactions, customers, regions)
Marketing data (advertising campaigns, promotions)
External factors (economic conditions, competition)
🧠 Tip: Ensure data is clean and complete before analysis to avoid skewed results.
📌 Step 3: Analyze the Data
Now, dive into the data and perform the following tasks:
1. Data Exploration: Look for patterns, trends, and anomalies.
2. Hypothesis Testing: Identify possible causes of the problem (e.g., "Are promotions leading to an increase in sales?").
3. Segmentation Analysis: Break down the data by regions, products, customer types, etc. to identify key insights.
🧠 Example:
Use SQL to extract sales data by region and calculate monthly growth:
SELECT Region, SUM(Sales) AS Total_Sales, AVG(Sales) AS Avg_Sales
FROM Sales
GROUP BY Region;
📌 Step 4: Visualize the Insights
Once you've analyzed the data, create visualizations to make the insights clear and actionable:
💹 Use line charts for trends over time.
📊 Use bar charts to compare different segments (regions, products, etc.).
🗺 Use heatmaps for geographical analysis.
💡 Tip: Keep your visualizations simple and focused on the key insights.
📌 Step 5: Provide Recommendations
Finally, based on your analysis, provide actionable recommendations to the business.
For example: “Focus promotions on Region X, where sales are consistently lower than other regions.”
“Increase marketing spend for the high-performing products.”
Free Resources for business analysts
👇👇
https://news.1rj.ru/str/analystcommunity
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍17❤9
Data Analytics
SQL Interview Questions with detailed answers: 2️⃣ How does GROUP BY work, and why do we use it? GROUP BY is used to arrange identical data into groups, often for performing aggregation functions (like COUNT, SUM, AVG, etc.) on each group. It's typically…
SQL Interview Questions with detailed answers:
3️⃣ What is the difference between HAVING and WHERE?
WHERE: It is used to filter records before any grouping occurs. It operates on individual rows in the table.
HAVING: It is used to filter records after the grouping operation. It works on aggregated data (e.g., data created using GROUP BY).
Example:
Explanation:
WHERE filters rows where the salary is greater than 50,000 before grouping by department.
HAVING filters departments where the average salary is greater than 60,000 after grouping.
Key difference:
WHERE filters individual rows.
HAVING filters groups after aggregation.
Like this post if you want me to continue this SQL Interview Series♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
3️⃣ What is the difference between HAVING and WHERE?
WHERE: It is used to filter records before any grouping occurs. It operates on individual rows in the table.
HAVING: It is used to filter records after the grouping operation. It works on aggregated data (e.g., data created using GROUP BY).
Example:
-- Using WHERE to filter rows before grouping
SELECT department_id, AVG(salary) AS avg_salary FROM employees WHERE salary > 50000 GROUP BY department_id;
-- Using HAVING to filter groups after aggregation
SELECT department_id, AVG(salary) AS avg_salary FROM employees GROUP BY department_id HAVING AVG(salary) > 60000;
Explanation:
WHERE filters rows where the salary is greater than 50,000 before grouping by department.
HAVING filters departments where the average salary is greater than 60,000 after grouping.
Key difference:
WHERE filters individual rows.
HAVING filters groups after aggregation.
Like this post if you want me to continue this SQL Interview Series♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍16❤10