🚀 Essential Python snippets to explore data:
 
1.   .head() - Review top rows
2.   .tail() - Review bottom rows
3.   .info() - Summary of DataFrame
4.   .shape - Shape of DataFrame
5.   .describe() - Descriptive stats
6.   .isnull().sum() - Check missing values
7.   .dtypes - Data types of columns
8.   .unique() - Unique values in a column
9.   .nunique() - Count unique values
10.   .value_counts() - Value counts in a column
11.   .corr() - Correlation matrix

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Python for Data Analytics - Quick Cheatsheet with Cod e Example 🚀

1️⃣ Data Manipulation with Pandas

import pandas as pd  
df = pd.read_csv("data.csv")  
df.to_excel("output.xlsx")  
df.head()  
df.info()  
df.describe()  
df[df["sales"] > 1000]  
df[["name", "price"]]  
df.fillna(0, inplace=True)  
df.dropna(inplace=True)  

2️⃣ Numerical Operations with NumPy

import numpy as np  
arr = np.array([1, 2, 3, 4])  
print(arr.shape)  
np.mean(arr)  
np.median(arr)  
np.std(arr)  

3️⃣ Data Visualization with Matplotlib & Seaborn

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])  
plt.bar(["A", "B", "C"], [5, 15, 25])  
plt.show()  
import seaborn as sns  
sns.heatmap(df.corr(), annot=True)  
sns.boxplot(x="category", y="sales", data=df)  
plt.show()  

4️⃣ Exploratory Data Analysis (EDA)

df.isnull().sum()  
df.corr()  
sns.histplot(df["sales"], bins=30)  
sns.boxplot(y=df["price"])  

5️⃣ Working with Databases (SQL + Python)

import sqlite3  
conn = sqlite3.connect("database.db")  
df = pd.read_sql("SELECT * FROM sales", conn)  
conn.close()  
cursor = conn.cursor()  
cursor.execute("SELECT AVG(price) FROM products")  
result = cursor.fetchone()  
print(result)

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Career Path for a Data Analyst

Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.

Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.

Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.

Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.

Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.

Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.

Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.

Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.

Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.

Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.

Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)

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SQL Interview Questions

1. How would you find duplicate records in SQL?
2.What are various types of SQL joins?
3.What is a trigger in SQL?
4.What are different DDL,DML commands in SQL?
5.What is difference between Delete, Drop and Truncate?
6.What is difference between Union and Union all?
7.Which command give Unique values?
8. What is the difference between Where and Having Clause?
9.Give the execution of keywords in SQL?
10. What is difference between IN and BETWEEN Operator?
11. What is primary and Foreign key?
12. What is an aggregate Functions?
13. What is the difference between Rank and Dense Rank?
14. List the ACID Properties and explain what they are?
15. What is the difference between % and _ in like operator?
16. What does CTE stands for?
17. What is database?what is DBMS?What is RDMS?
18.What is Alias in SQL?
19. What is Normalisation?Describe various form?
20. How do you sort the results of a query?
21. Explain the types of Window functions?
22. What is limit and offset?
23. What is candidate key?
24. Describe various types of Alter command?
25. What is Cartesian product?

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Almost everyone knows that these are the tools a Data Analyst works with:

➡️ SQL
➡️ Excel
➡️ Power BI/Tableau
➡️ Python

But people getting started with analytics are confused about the preferences of picking these tools.

There are various kinds of data analytics roles available in the market :

➡️ BI + SQL: Will primarily be involved in BI development.

➡️ SQL + Excel: Will primarily work on Excel reporting.

➡️ SQL + Python: Will primarily do data analysis using python.

Now, If you are getting started with learning analytics, choose any one role that interests you the most and focus on completing the primary tools that the role requires. Learn them VERY WELL.

Learn any of the above combinations that interests you first and then start looking out for opportunities which ask for these primary tools and simultaneously start learning the basics of the 3rd tool.

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10 SQL Concepts Every Data Analyst Should Master 👇

✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB

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Data Analyst Interview Questions
[Python, SQL, PowerBI]

1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.

2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.

Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.

3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.

4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI

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Essential Pandas Functions for Data Analysis

Data Loading:

pd.read_csv() - Load data from a CSV file.

pd.read_excel() - Load data from an Excel file.


Data Inspection:

df.head(n) - View the first n rows.

df.info() - Get a summary of the dataset.

df.describe() - Generate summary statistics.


Data Manipulation:

df.drop(columns=['col1', 'col2']) - Remove specific columns.

df.rename(columns={'old_name': 'new_name'}) - Rename columns.

df['col'] = df['col'].apply(func) - Apply a function to a column.


Filtering and Sorting:

df[df['col'] > value] - Filter rows based on a condition.

df.sort_values(by='col', ascending=True) - Sort rows by a column.


Aggregation:

df.groupby('col').sum() - Group data and compute the sum.

df['col'].value_counts() - Count unique values in a column.


Merging and Joining:

pd.merge(df1, df2, on='key') - Merge two DataFrames.

pd.concat([df1, df2]) - Concatenate

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