NEW BOT Телеграм, страница

Data Analytics

👍13❤4

7.33K viewsedited 06:22

Data Analytics

It takes time to learn Excel.

It takes time to master SQL.

It takes time to understand Power BI.

It takes time to analyze complex datasets.

It takes time to create impactful dashboards.

It takes time to work on real-world data projects.

It takes time to build a strong LinkedIn profile.

It takes time to prepare for technical and behavioral interviews.

Here’s one tip from someone who’s been through it all:

Be Patient. Good things take time ☺️

Keep building your skills and showcasing your value. Your time will come!

❤35👍17🔥2🥰2👎1

7.44K viewsedited 07:56

Data Analytics

Data Analyst Interview Part-7 What is the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN in SQL? INNER JOIN: Returns only matching rows from both tables. LEFT JOIN: Returns all rows from the left table and matching rows from the right…

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()

Converting data types:
df['column'] = df['column'].astype(int)

Handling outliers:
Use filtering or statistical methods to identify and remove outliers.

Standardizing or normalizing data:
Use libraries like scikit-learn for scaling:

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df[['column']] = scaler.fit_transform(df[['column']])

What is the use of GROUP BY in SQL?

GROUP BY is used to group rows that have the same values into summary rows, often with aggregate functions like COUNT, SUM, AVG, etc.

Example:

SELECT department, AVG(salary) FROM employees GROUP BY department;

This will calculate the average salary for each department.

What is the significance of normalization in SQL?

Normalization is the process of organizing data in a way that reduces redundancy and dependency by dividing large tables into smaller ones and using relationships (foreign keys).

1st Normal Form (1NF): Ensures atomicity (no multi-valued fields).

2nd Normal Form (2NF): Ensures that all non-key attributes are fully dependent on the primary key.

3rd Normal Form (3NF): Ensures that no transitive dependencies exist (non-key attributes do not depend on other non-key attributes).

How do you handle time series data in Python?

Handling time series data in Python involves several steps:

Converting to DateTime format: df['date'] = pd.to_datetime(df['date'])

Resampling: To aggregate data at different frequencies:
df.set_index('date').resample('M').sum()

Decomposition: Split the time series into trend, seasonality, and residuals:

from statsmodels.tsa.seasonal
import seasonal_decompose decomposition = seasonal_decompose(df['value'], model='additive', period=12) decomposition.plot()

Plotting: Use libraries like Matplotlib and Seaborn to visualize trends over time.

What are the advantages of using Power BI over Excel?

Data Handling: Power BI can handle much larger datasets (millions of rows) compared to Excel.

Data Modeling: Power BI allows creating complex data models and relationships between tables, which is harder to manage in Excel.

Interactive Visualizations: Power BI offers interactive dashboards with drill-down capabilities.

Advanced Features: Power BI supports advanced analytics, DAX for custom calculations, and integration with other tools like Azure and SharePoint.

Scheduled Refresh: Power BI allows automatic data refresh from connected sources, while in Excel, this needs to be done manually.

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

7.75K views15:29

Data Analytics

Recent Interview Question for Data Analyst Role

Question: You have two tables:

Employee:-
Columns: EID (Employee ID), ESalary (Employee Salary)

empdetails:-
Columns: EID (Employee ID), EDOB (Employee Date of Birth)

Your task is to:
1) Identify all employees whose salary (ESalary) is an odd number?
2) Retrieve the date of birth (EDOB) for these employees from the empdetails table.

How would you write a SQL query to achieve this?

SELECT e.EID, ed.EDOB
FROM (
SELECT EID
FROM Employee
WHERE ESalary % 2 <> 0
) e
JOIN empdetails ed ON e.EID = ed.EID;

Explanation of the query :-

Filter Employees with Odd Salaries:

The subquery SELECT EID FROM Employee WHERE ESalary % 2 <> 0 filters out Employee IDs (EID) where the salary (ESalary) is an odd number. The modulo operator % checks if ESalary divided by 2 leaves a remainder (<>0).

Merge with empdetails:

The main query then takes the filtered Employee IDs from the subquery and performs a join with the empdetails table using the EID column. This retrieves the date of birth (EDOB) for these employees.

Hope this helps you 😊

👍22❤6

7.42K viewsedited 18:49

Data Analytics

🔍 Best Data Analytics Roles Based on Your Graduation Background!

Thinking about a career in Data Analytics but unsure which role fits your background? Check out these top job roles based on your degree:

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

✅ Pro Tip:

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

Like if it helps ❤️

👍14❤4

6.97K viewsedited 06:14

Data Analytics

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 😊

👍23❤10

7.5K views07:26

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 :)

👍11❤7🥰1

7.6K views16:12

Data Analytics

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 :)

❤8👍7

7.15K views05:14

Data Analytics

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.

👍10❤5👌2

7.28K views07:47

Data Analytics

🚀 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 :)

👍21❤8👏2🔥1

9.35K viewsedited 09:45

Data Analytics

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.

👍19

9.14K viewsedited 06:42

Data Analytics

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 😊

👍31❤17👎1🔥1

8.93K views18:13

Data Analytics

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 😊

👍18🥰4🎉2❤1

9.03K viewsedited 09:11

Data Analytics

👍27❤13

8.6K views16:47

Data Analytics

👍23❤11

8.13K views06:15

Data Analytics

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 :)

👍15❤13

8.21K viewsedited 10:28

Data Analytics

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 :)

👍18❤15

7.98K views14:21

Data Analytics

📊 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 :)

❤8👍7🔥2

7.48K views02:02

Data Analytics

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:

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

7.87K viewsedited 06:07

Data Analytics

🔍 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):

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

9.37K views13:18

Data Analytics

📊 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 :)

👍16❤10👏2

9.6K views18:04

About

Blog

Apps

Platform