How to Think Like a Data Analyst ๐ง ๐
Being a great data analyst isnโt just about knowing SQL, Python, or Power BIโitโs about how you think.
Hereโs how to develop a data-driven mindset:
1๏ธโฃ Always Ask โWhy?โ ๐ค
Donโt just look at numbersโquestion them. If sales dropped, ask: Is it seasonal? A pricing issue? A marketing failure?
2๏ธโฃ Break Down Problems Logically ๐
Instead of tackling a problem all at once, divide it into smaller, manageable parts. Example: If customer churn is increasing, analyze trends by segment, region, and time period.
3๏ธโฃ Be Skeptical of Data โ ๏ธ
Not all data is accurate. Always check for missing values, biases, and inconsistencies before drawing conclusions.
4๏ธโฃ Look for Patterns & Trends ๐
Raw numbers donโt tell a story until you find relationships. Compare trends over time, detect anomalies, and identify key influencers.
5๏ธโฃ Keep Business Goals in Mind ๐ฏ
Data without context is useless. Always tie insights to business impactโcost reduction, revenue growth, customer satisfaction, etc.
6๏ธโฃ Simplify Complex Insights โ๏ธ
Not everyone understands data like you do. Use visuals and clear language to explain findings to non-technical audiences.
7๏ธโฃ Be Curious & Experiment ๐
Try different approachesโA/B testing, new models, or alternative data sources. Experimentation leads to better insights.
8๏ธโฃ Stay Updated & Keep Learning ๐
The best analysts stay ahead by learning new tools, techniques, and industry trends. Follow blogs, take courses, and practice regularly.
Thinking like a data analyst is a skill that improves with experience. Keep questioning, analyzing, and improving! ๐ฅ
React with โค๏ธ if you agree with me
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Being a great data analyst isnโt just about knowing SQL, Python, or Power BIโitโs about how you think.
Hereโs how to develop a data-driven mindset:
1๏ธโฃ Always Ask โWhy?โ ๐ค
Donโt just look at numbersโquestion them. If sales dropped, ask: Is it seasonal? A pricing issue? A marketing failure?
2๏ธโฃ Break Down Problems Logically ๐
Instead of tackling a problem all at once, divide it into smaller, manageable parts. Example: If customer churn is increasing, analyze trends by segment, region, and time period.
3๏ธโฃ Be Skeptical of Data โ ๏ธ
Not all data is accurate. Always check for missing values, biases, and inconsistencies before drawing conclusions.
4๏ธโฃ Look for Patterns & Trends ๐
Raw numbers donโt tell a story until you find relationships. Compare trends over time, detect anomalies, and identify key influencers.
5๏ธโฃ Keep Business Goals in Mind ๐ฏ
Data without context is useless. Always tie insights to business impactโcost reduction, revenue growth, customer satisfaction, etc.
6๏ธโฃ Simplify Complex Insights โ๏ธ
Not everyone understands data like you do. Use visuals and clear language to explain findings to non-technical audiences.
7๏ธโฃ Be Curious & Experiment ๐
Try different approachesโA/B testing, new models, or alternative data sources. Experimentation leads to better insights.
8๏ธโฃ Stay Updated & Keep Learning ๐
The best analysts stay ahead by learning new tools, techniques, and industry trends. Follow blogs, take courses, and practice regularly.
Thinking like a data analyst is a skill that improves with experience. Keep questioning, analyzing, and improving! ๐ฅ
React with โค๏ธ if you agree with me
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐4๐1
๐ Real-World Data Analyst Tasks & How to Solve Them
As a Data Analyst, your job isnโt just about writing SQL queries or making dashboardsโitโs about solving business problems using data. Letโs explore some common real-world tasks and how you can handle them like a pro!
๐ Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
โ Solution (Using Pandas in Python):
๐ก Tip: Always check for inconsistent spellings and incorrect date formats!
๐ Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
โ Solution (Using SQL):
๐ก Tip: Try adding YEAR(SaleDate) to compare yearly trends!
๐ Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
โ Solution (Using Power BI / Tableau):
๐ Add KPI Cards to show total sales & profit
๐ Use a Line Chart for monthly trends
๐ Create a Bar Chart for top-selling products
๐ Use Filters/Slicers for better interactivity
๐ก Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
As a Data Analyst, your job isnโt just about writing SQL queries or making dashboardsโitโs about solving business problems using data. Letโs explore some common real-world tasks and how you can handle them like a pro!
๐ Task 1: Cleaning Messy Data
Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.
โ 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())
๐ก Tip: Always check for inconsistent spellings and incorrect date formats!
๐ Task 2: Analyzing Sales Trends
A company wants to know which months have the highest sales.
โ 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;
๐ก Tip: Try adding YEAR(SaleDate) to compare yearly trends!
๐ Task 3: Creating a Business Dashboard
Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.
โ Solution (Using Power BI / Tableau):
๐ Add KPI Cards to show total sales & profit
๐ Use a Line Chart for monthly trends
๐ Create a Bar Chart for top-selling products
๐ Use Filters/Slicers for better interactivity
๐ก Tip: Keep your dashboards clean, interactive, and easy to interpret!
Like this post for more content like this โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐4๐2
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ธ๐ป๐ผ๐ ๐๐ต๐ฎ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ ๐ถ๐ป ๐ฎ ๐ฟ๐ฒ๐ฎ๐น ๐ฑ๐ฎ๐๐ฎ ๐ฎ๐ป๐ฎ๐น๐๐๐ ๐ถ๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐?
๐๐ฎ๐๐ถ๐ฐ ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
๐ง๐ผ๐ผ๐น๐ ๐ฃ๐ฟ๐ผ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ ๐ฝ๐น๐ฎ๐ป๐ฎ๐๐ถ๐ผ๐ป
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
๐ฆ๐ค๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
๐๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-โญ ๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ ๐๐ฝ๐ฝ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
Like if it helps :)
๐๐ฎ๐๐ถ๐ฐ ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
๐ง๐ผ๐ผ๐น๐ ๐ฃ๐ฟ๐ผ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ ๐ฝ๐น๐ฎ๐ป๐ฎ๐๐ถ๐ผ๐ป
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
๐ฆ๐ค๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
๐๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-โญ ๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ ๐๐ฝ๐ฝ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
Like if it helps :)
๐4๐1
1. What are the different subsets of SQL?
Data Definition Language (DDL) โ It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) โ It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) โ It allows you to control access to the database. Example โ Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One โ This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One โ This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many โ This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships โ When a table has to declare a connection with itself, this is the method to employ.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
Data Definition Language (DDL) โ It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) โ It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) โ It allows you to control access to the database. Example โ Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One โ This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One โ This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many โ This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships โ When a table has to declare a connection with itself, this is the method to employ.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
๐4โค1๐1
Data Analytics Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://datacamp.pxf.io/vPyB4L
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.iss.one/excel_data
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING ๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills๐๐
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.iss.one/sqlspecialist/738
2. Python
https://www.learnpython.org/
https://t.iss.one/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://datacamp.pxf.io/vPyB4L
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.iss.one/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://t.iss.one/excel_data
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐5๐1
Why learn SQL if ChatGPT can write it?
A few reasons why you should still learn SQL:
1๏ธโฃ An understanding of the nuances of SQL is necessary to ask the Large Language Model (โLLMโ) the right questions to get a good response.
2๏ธโฃ You have to double check the LLMs response. Sometimes I get answers that uses features that have been deprecated (probably because the LLM was trained on older data). It still makes mistakes and overcomplicates problems.
3๏ธโฃ Making changes to the query requires an understanding of SQL. Without it, you might get stuck. It's important to understand the query's purpose.
So what do I use these LLMs for?
I find it a good starting point for syntax or query structure. Like โhow would I use a window function to get the latest record in a table?โ But it doesnโt understand my companyโs data models, table relationships, or business logic. This is where my SQL + business knowledge comes in.
A few reasons why you should still learn SQL:
1๏ธโฃ An understanding of the nuances of SQL is necessary to ask the Large Language Model (โLLMโ) the right questions to get a good response.
2๏ธโฃ You have to double check the LLMs response. Sometimes I get answers that uses features that have been deprecated (probably because the LLM was trained on older data). It still makes mistakes and overcomplicates problems.
3๏ธโฃ Making changes to the query requires an understanding of SQL. Without it, you might get stuck. It's important to understand the query's purpose.
So what do I use these LLMs for?
I find it a good starting point for syntax or query structure. Like โhow would I use a window function to get the latest record in a table?โ But it doesnโt understand my companyโs data models, table relationships, or business logic. This is where my SQL + business knowledge comes in.
๐4
Guys, Big Announcement!
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
๐4๐2โค1๐1
Once you've learned/mastered the fundamentals of SQL, try learning these:
- ๐๐๐๐๐ฌ: LEFT, RIGHT, INNER, OUTER joins.
- ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- ๐๐๐๐ ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ: Use conditional logic to tailor query results.
- ๐๐๐ญ๐ ๐๐ข๐ฆ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Master manipulating dates and times for precise analysis.
Next, explore advanced methods to structure and reuse SQL code effectively:
- ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ (๐๐๐๐ฌ): Simplify complex queries into manageable parts to increase the readability.
- ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Nest queries for more granular data retrieval.
- ๐๐๐ฆ๐ฉ๐จ๐ซ๐๐ซ๐ฒ ๐๐๐๐ฅ๐๐ฌ: Create and manipulate temporary data sets for specific tasks.
Then, move on to advanced ones:
- ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Perform advanced calculations over sets of rows with ease.
- ๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ: Create reusable SQL routines for streamlined operations.
- ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Automate database actions based on specific events.
- ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐๐๐ฌ: Solve complex problems using recursive queries.
- ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ: Techniques to enhance performance and efficiency.
- ๐๐๐๐๐ฌ: LEFT, RIGHT, INNER, OUTER joins.
- ๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Utilize SUM, COUNT, AVG, and others for efficient data summarization.
- ๐๐๐๐ ๐๐๐๐ ๐๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ๐ฌ: Use conditional logic to tailor query results.
- ๐๐๐ญ๐ ๐๐ข๐ฆ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Master manipulating dates and times for precise analysis.
Next, explore advanced methods to structure and reuse SQL code effectively:
- ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ (๐๐๐๐ฌ): Simplify complex queries into manageable parts to increase the readability.
- ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Nest queries for more granular data retrieval.
- ๐๐๐ฆ๐ฉ๐จ๐ซ๐๐ซ๐ฒ ๐๐๐๐ฅ๐๐ฌ: Create and manipulate temporary data sets for specific tasks.
Then, move on to advanced ones:
- ๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Perform advanced calculations over sets of rows with ease.
- ๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ: Create reusable SQL routines for streamlined operations.
- ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Automate database actions based on specific events.
- ๐๐๐๐ฎ๐ซ๐ฌ๐ข๐ฏ๐ ๐๐๐๐ฌ: Solve complex problems using recursive queries.
- ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ: Techniques to enhance performance and efficiency.
๐4๐2
Powerful One-Liners in Python You Should Know!
1. Swap Two Numbers
n1, n2 = n2, n1
2. Reverse a String
reversed_string = input_string[::-1]
3. Factorial of a Number
fact = lambda n: [1, 0][n > 1] or fact(n - 1) * n
4. Find Prime Numbers (2 to 10)
primes = list(filter(lambda x: all(x % y != 0 for y in range(2, x)), range(2, 10)))
5. Check if a String is Palindrome
palindrome = input_string == input_string[::-1]
Free Python Resources: https://t.iss.one/pythonproz
1. Swap Two Numbers
n1, n2 = n2, n1
2. Reverse a String
reversed_string = input_string[::-1]
3. Factorial of a Number
fact = lambda n: [1, 0][n > 1] or fact(n - 1) * n
4. Find Prime Numbers (2 to 10)
primes = list(filter(lambda x: all(x % y != 0 for y in range(2, x)), range(2, 10)))
5. Check if a String is Palindrome
palindrome = input_string == input_string[::-1]
Free Python Resources: https://t.iss.one/pythonproz
๐4
๐ Free Power BI Course by Microsoft
https://learn.microsoft.com/en-us/power-bi/
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โค1
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ Bayes' Theorem & conditional probability
โ Permutations & combinations
โ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ Mean, median, mode
โ Standard deviation and variance
โ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ A/B experimentation
โ T-test, Z-test, Chi-squared tests
โ Type 1 & 2 errors
โ Sampling techniques & biases
โ Confidence intervals & p-values
โ Central Limit Theorem
โ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ Logistic & Linear regression
โ Decision trees & random forests
โ Clustering models
โ Feature engineering
โ Feature selection methods
โ Model testing & validation
โ Time series analysis
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ Bayes' Theorem & conditional probability
โ Permutations & combinations
โ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ Mean, median, mode
โ Standard deviation and variance
โ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ A/B experimentation
โ T-test, Z-test, Chi-squared tests
โ Type 1 & 2 errors
โ Sampling techniques & biases
โ Confidence intervals & p-values
โ Central Limit Theorem
โ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ Logistic & Linear regression
โ Decision trees & random forests
โ Clustering models
โ Feature engineering
โ Feature selection methods
โ Model testing & validation
โ Time series analysis
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
๐4๐1
Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://t.iss.one/free4unow_backup
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐4โค2
Learning data analytics in 2025 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics:
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
Give credits while sharing: https://t.iss.one/learndataanalysis
๐2
Power BI DAX Cheatsheet ๐
1๏ธโฃ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2๏ธโฃ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3๏ธโฃ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4๏ธโฃ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5๏ธโฃ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6๏ธโฃ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7๏ธโฃ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8๏ธโฃ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9๏ธโฃ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
๐ Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
React with โค๏ธ for free cheatsheets
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1๏ธโฃ Basics of DAX (Data Analysis Expressions)
DAX is used to create custom calculations in Power BI.
It works with tables and columns, not individual cells.
Functions in DAX are similar to Excel but optimized for relational data.
2๏ธโฃ Aggregation Functions
SUM(ColumnName): Adds all values in a column.
AVERAGE(ColumnName): Finds the mean of values.
MIN(ColumnName): Returns the smallest value.
MAX(ColumnName): Returns the largest value.
COUNT(ColumnName): Counts non-empty values.
COUNTROWS(TableName): Counts rows in a table.
3๏ธโฃ Logical Functions
IF(condition, result_if_true, result_if_false): Conditional statement.
SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.
AND(condition1, condition2): Returns TRUE if both conditions are met.
OR(condition1, condition2): Returns TRUE if either condition is met.
4๏ธโฃ Time Intelligence Functions
TODAY(): Returns the current date.
YEAR(TODAY()): Extracts the year from a date.
TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.
SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.
DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.
5๏ธโฃ Filtering Functions
FILTER(Table, Condition): Returns a filtered table.
ALL(TableName): Removes all filters from a table.
ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.
KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.
6๏ธโฃ Ranking & Row Context Functions
RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.
TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.
7๏ธโฃ Iterators (Row-by-Row Calculations)
SUMX(Table, Expression): Iterates over a table and sums calculated values.
AVERAGEX(Table, Expression): Iterates over a table and finds the average.
MAXX(Table, Expression): Finds the maximum value based on an expression.
8๏ธโฃ Relationships & Lookup Functions
RELATED(ColumnName): Fetches a related column from another table.
LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.
9๏ธโฃ Variables in DAX
VAR variableName = Expression RETURN variableName
Improves performance by reducing redundant calculations.
๐ Advanced DAX Concepts
Calculated Columns: Created at the column level, stored in the data model.
Measures: Dynamic calculations based on user interactions in Power BI visuals.
Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.
Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
React with โค๏ธ for free cheatsheets
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐6โค2
Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
โค5๐1
๐๐Data Analytics skills and projects to add in a resume to get shortlisted
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
๐ผTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.
2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.
3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.
4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.
5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.
6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.
7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.
8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.
9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.
10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.
11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.
12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.
๐ผTailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
๐4
Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐2๐1