Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
๐4โค2
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
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
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
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
๐2
I have uploaded a lot of free resources on Linkedin as well
๐๐
https://www.linkedin.com/company/sql-analysts/
We're just 6k followers away from reaching 200k on LinkedIn! โค๏ธ Join us and be part of this milestone!
๐๐
https://www.linkedin.com/company/sql-analysts/
We're just 6k followers away from reaching 200k on LinkedIn! โค๏ธ Join us and be part of this milestone!
โค2๐2
โ
๐๐จ๐ฐ ๐ญ๐จ ๐๐ฎ๐ข๐ฅ๐ ๐ ๐๐๐ซ๐๐๐ซ ๐๐ฌ ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐ข๐ง ๐๐๐๐ ๐งโ๐ป
If you are thinking about becoming a data analyst, 2025 is the perfect year to start. Companies need people who can understand data and turn it into useful insights. Hereโs a simple step-by-step guide to help you start your journey.
๐. ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐ฅ๐
A data analyst collects and studies data to help companies make better decisions. They find trends, create reports, and suggest solutions to business problems.
๐. ๐๐๐๐ซ๐ง ๐๐๐๐๐ฌ๐ฌ๐๐ซ๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
๐๐ฑ๐๐๐ฅ: Start with PivotTables, VLOOKUP, and creating dashboards.
๐๐๐: Master queries to extract and manipulate data.
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐๐จ๐จ๐ฅ๐ฌ: Learn Power BI and Tableau to present insights effectively.
๐๐ฒ๐ญ๐ก๐จ๐ง: Focus on libraries like Pandas, NumPy, Matplotlib, and Seaborn.
๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ: Basic concepts- mean, median, mode, standard deviation, regression.
๐. ๐๐จ๐ซ๐ค ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
https://t.iss.one/sqlproject
https://t.iss.one/pythonspecialist
๐. ๐๐๐ข๐ง ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
Certifications add credibility to your resume. Some popular ones include:
Google Data Analytics Professional Certificate
Microsoft Certified: Data Analyst Associate
Tableau Desktop Specialist Certification
๐. ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ
๐๐ข๐ง๐ค๐๐๐๐ง: Treat your LinkedIn profile as your portfolio. Update it with skills, certifications, and projects.
๐๐ข๐ญ๐๐ฎ๐: Add links to your GitHub repositories with coding projects and Power BI/Tableau dashboards.
๐. ๐๐๐ข๐ง ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐ (๐ ๐จ๐ซ ๐ ๐ซ๐๐ฌ๐ก๐๐ซ๐ฌ)
If you're a fresher, here are some ideas to gain experience:
๐๐ง๐ญ๐๐ซ๐ง๐ฌ๐ก๐ข๐ฉ๐ฌ: Apply for internships at companies where you can work on real data problems.
๐ ๐ซ๐๐๐ฅ๐๐ง๐๐ข๐ง๐ : Offer data analysis services on platforms like Upwork, Fiverr, or Freelancer.
๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Build your own projects, such as analyzing public datasets (e.g., from Kaggle), and share them on GitHub.
๐๐ง๐ฅ๐ข๐ง๐ ๐๐จ๐ฆ๐ฉ๐๐ญ๐ข๐ญ๐ข๐จ๐ง๐ฌ: Participate in data analysis competitions on Kaggle or DrivenData to build your skills and gain recognition.
๐๐ฉ๐๐ง-๐๐จ๐ฎ๐ซ๐๐: Contribute to open-source data analysis projects on GitHub.
๐. ๐๐ญ๐๐ซ๐ญ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐ ๐๐จ๐ซ ๐๐จ๐๐ฌ
Tailor your resume and portfolio for each role. Highlight projects and key skills. Consider entry-level roles like:
Junior Data Analyst, Business Analyst, Reporting Analyst
Use platforms like LinkedIn & Naukri to apply for jobs.
If you are thinking about becoming a data analyst, 2025 is the perfect year to start. Companies need people who can understand data and turn it into useful insights. Hereโs a simple step-by-step guide to help you start your journey.
๐. ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐ฅ๐
A data analyst collects and studies data to help companies make better decisions. They find trends, create reports, and suggest solutions to business problems.
๐. ๐๐๐๐ซ๐ง ๐๐๐๐๐ฌ๐ฌ๐๐ซ๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
๐๐ฑ๐๐๐ฅ: Start with PivotTables, VLOOKUP, and creating dashboards.
๐๐๐: Master queries to extract and manipulate data.
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐๐จ๐จ๐ฅ๐ฌ: Learn Power BI and Tableau to present insights effectively.
๐๐ฒ๐ญ๐ก๐จ๐ง: Focus on libraries like Pandas, NumPy, Matplotlib, and Seaborn.
๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ: Basic concepts- mean, median, mode, standard deviation, regression.
๐. ๐๐จ๐ซ๐ค ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
https://t.iss.one/sqlproject
https://t.iss.one/pythonspecialist
๐. ๐๐๐ข๐ง ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
Certifications add credibility to your resume. Some popular ones include:
Google Data Analytics Professional Certificate
Microsoft Certified: Data Analyst Associate
Tableau Desktop Specialist Certification
๐. ๐๐ซ๐๐๐ญ๐ ๐๐จ๐ซ๐ญ๐๐จ๐ฅ๐ข๐จ
๐๐ข๐ง๐ค๐๐๐๐ง: Treat your LinkedIn profile as your portfolio. Update it with skills, certifications, and projects.
๐๐ข๐ญ๐๐ฎ๐: Add links to your GitHub repositories with coding projects and Power BI/Tableau dashboards.
๐. ๐๐๐ข๐ง ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐ (๐ ๐จ๐ซ ๐ ๐ซ๐๐ฌ๐ก๐๐ซ๐ฌ)
If you're a fresher, here are some ideas to gain experience:
๐๐ง๐ญ๐๐ซ๐ง๐ฌ๐ก๐ข๐ฉ๐ฌ: Apply for internships at companies where you can work on real data problems.
๐ ๐ซ๐๐๐ฅ๐๐ง๐๐ข๐ง๐ : Offer data analysis services on platforms like Upwork, Fiverr, or Freelancer.
๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Build your own projects, such as analyzing public datasets (e.g., from Kaggle), and share them on GitHub.
๐๐ง๐ฅ๐ข๐ง๐ ๐๐จ๐ฆ๐ฉ๐๐ญ๐ข๐ญ๐ข๐จ๐ง๐ฌ: Participate in data analysis competitions on Kaggle or DrivenData to build your skills and gain recognition.
๐๐ฉ๐๐ง-๐๐จ๐ฎ๐ซ๐๐: Contribute to open-source data analysis projects on GitHub.
๐. ๐๐ญ๐๐ซ๐ญ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐ ๐๐จ๐ซ ๐๐จ๐๐ฌ
Tailor your resume and portfolio for each role. Highlight projects and key skills. Consider entry-level roles like:
Junior Data Analyst, Business Analyst, Reporting Analyst
Use platforms like LinkedIn & Naukri to apply for jobs.
๐2โค1
Free Session to learn Data Analytics, Data Science & AI
๐๐
https://tracking.acciojob.com/g/PUfdDxgHR
Register fast, only for first few users
๐๐
https://tracking.acciojob.com/g/PUfdDxgHR
Register fast, only for first few users
๐2
Basic SQL Commands โ
โค2๐ฅ2
5 Data Analytics Project Ideas to boost your resume:
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
๐5
Essential Topics to Master Data Science Interviews: ๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some โค๏ธ if you're ready to elevate your data science journey! ๐
ENJOY LEARNING ๐๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some โค๏ธ if you're ready to elevate your data science journey! ๐
ENJOY LEARNING ๐๐
๐8โค2
Goldman Sachs senior data analyst interview asked questions
SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Hope this helps you ๐
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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
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
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
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
๐5
โฐ MySQL Data Types
MySQL provides a variety of data types to store different kinds of data. These are categorized into three main groups:
1. Numeric Data Types:
- INT, BIGINT, SMALLINT, TINYINT: For whole numbers.
- DECIMAL, FLOAT, DOUBLE: For real numbers with decimal points.
- BIT: For binary values.
- Example:
1. String Data Types:
- CHAR, VARCHAR: For fixed and variable-length strings.
- TEXT: For large text.
- BLOB: For binary large objects like images.
- Example:
1. Date and Time Data Types:
- DATE, DATETIME, TIMESTAMP: For date and time values.
- YEAR: For storing a year.
- Example:
Interview Questions:
- Q1: What is the difference between CHAR and VARCHAR?
A1: CHAR has a fixed length, while VARCHAR has a variable length. VARCHAR is more storage-efficient for varying-length data.
- Q2: When should you use DECIMAL instead of FLOAT?
A2: Use DECIMAL for precise calculations (e.g., financial data) and FLOAT for approximate values where precision is less critical.
MySQL provides a variety of data types to store different kinds of data. These are categorized into three main groups:
1. Numeric Data Types:
- INT, BIGINT, SMALLINT, TINYINT: For whole numbers.
- DECIMAL, FLOAT, DOUBLE: For real numbers with decimal points.
- BIT: For binary values.
- Example:
CREATE TABLE numeric_example (
id INT,
amount DECIMAL(10, 2)
);
1. String Data Types:
- CHAR, VARCHAR: For fixed and variable-length strings.
- TEXT: For large text.
- BLOB: For binary large objects like images.
- Example:
CREATE TABLE string_example (
name VARCHAR(100),
description TEXT
);
1. Date and Time Data Types:
- DATE, DATETIME, TIMESTAMP: For date and time values.
- YEAR: For storing a year.
- Example:
CREATE TABLE datetime_example (
created_at DATETIME,
year_of_joining YEAR
);
Interview Questions:
- Q1: What is the difference between CHAR and VARCHAR?
A1: CHAR has a fixed length, while VARCHAR has a variable length. VARCHAR is more storage-efficient for varying-length data.
- Q2: When should you use DECIMAL instead of FLOAT?
A2: Use DECIMAL for precise calculations (e.g., financial data) and FLOAT for approximate values where precision is less critical.
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Essential questions related to Data Analytics ๐๐
Question 1: What is the first skill a fresher should learn for a Data Analytics job?
Answer: SQL. Itโs the foundation for retrieving, manipulating, and analyzing data stored in databases.
Question 2: Which SQL database query should we learn - MySQL, PostgreSQL, PL-SQL, etc.?
Answer: Core SQL concepts are consistent across platforms. Focus on joins, aggregations, subqueries, and window functions.
Question 3: How much Python is required?
Answer: Learn basic syntax, loops, conditional statements, functions, and error handling. Then focus on Pandas and Numpy very well for data handling and analysis. Working Knowledge of Python + Good knowledge of Data Analysis Libraries is needed only.
Question 4: What other skills are required?
Answer: MS Excel for data cleaning and analysis, and a BI tool like Power BI or Tableau for creating dashboards.
Question 5: Is knowledge of Macros/VBA required?
Answer: No. Most Data Analyst roles donโt require it.
Question 6: When should I start applying for jobs?
Answer: Apply after acquiring 50% of the required skills and gaining practical experience through projects or internships.
Question 7: Are certifications required?
Answer: No. Projects and hands-on experience are more valuable.
Question 8: How important is data visualization in a Data Analyst role?
Answer: Very important. Use tools like Tableau or Power BI to present insights effectively.
Question 9: Is understanding statistics important for data analysis?
Answer: Yes. Learn descriptive statistics, hypothesis testing, and regression analysis for better insights.
Question 10: How much emphasis should be placed on machine learning?
Answer: A basic understanding is helpful but not essential for Data Analyst roles.
Question 11: What role does communication play in a Data Analyst's job?
Answer: Itโs crucial. You need to present insights in a clear and actionable way for stakeholders.
Question 12: Is data cleaning a necessary skill?
Answer: Yes. Cleaning and preparing raw data is a major part of a Data Analystโs job.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
Question 1: What is the first skill a fresher should learn for a Data Analytics job?
Answer: SQL. Itโs the foundation for retrieving, manipulating, and analyzing data stored in databases.
Question 2: Which SQL database query should we learn - MySQL, PostgreSQL, PL-SQL, etc.?
Answer: Core SQL concepts are consistent across platforms. Focus on joins, aggregations, subqueries, and window functions.
Question 3: How much Python is required?
Answer: Learn basic syntax, loops, conditional statements, functions, and error handling. Then focus on Pandas and Numpy very well for data handling and analysis. Working Knowledge of Python + Good knowledge of Data Analysis Libraries is needed only.
Question 4: What other skills are required?
Answer: MS Excel for data cleaning and analysis, and a BI tool like Power BI or Tableau for creating dashboards.
Question 5: Is knowledge of Macros/VBA required?
Answer: No. Most Data Analyst roles donโt require it.
Question 6: When should I start applying for jobs?
Answer: Apply after acquiring 50% of the required skills and gaining practical experience through projects or internships.
Question 7: Are certifications required?
Answer: No. Projects and hands-on experience are more valuable.
Question 8: How important is data visualization in a Data Analyst role?
Answer: Very important. Use tools like Tableau or Power BI to present insights effectively.
Question 9: Is understanding statistics important for data analysis?
Answer: Yes. Learn descriptive statistics, hypothesis testing, and regression analysis for better insights.
Question 10: How much emphasis should be placed on machine learning?
Answer: A basic understanding is helpful but not essential for Data Analyst roles.
Question 11: What role does communication play in a Data Analyst's job?
Answer: Itโs crucial. You need to present insights in a clear and actionable way for stakeholders.
Question 12: Is data cleaning a necessary skill?
Answer: Yes. Cleaning and preparing raw data is a major part of a Data Analystโs job.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐10
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.
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