๐ ๐๐๐ฒ๐ฌ ๐ญ๐จ ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐๐ฌ
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
โค3
A - Always check your assumptions
B - Backup your data
C - Check your code
D - Do you know your data?
E - Evaluate your results
F - Find the anomalies
G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers
J - Justify your methods
K - Keep your data clean
L - Let your data tell a story
M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis
P - Practice good documentation
Q - Quality control is key
R - Review your work
S - Stay organized
T - Test your assumptions
U - Use the right tools
V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data
Y - Yield to the evidence
Z - Zero in on your findings
If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.
B - Backup your data
C - Check your code
D - Do you know your data?
E - Evaluate your results
F - Find the anomalies
G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers
J - Justify your methods
K - Keep your data clean
L - Let your data tell a story
M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis
P - Practice good documentation
Q - Quality control is key
R - Review your work
S - Stay organized
T - Test your assumptions
U - Use the right tools
V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data
Y - Yield to the evidence
Z - Zero in on your findings
If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.
โค3๐1๐ฆ1
Exploratory Data Analysis (EDA)
EDA is the process of analyzing datasets to summarize key patterns, detect anomalies, and gain insights before applying machine learning or reporting.
1๏ธโฃ Descriptive Statistics
Descriptive statistics help summarize and understand data distributions.
In SQL:
Calculate Mean (Average):
Find Mode (Most Frequent Value)
Calculate Variance & Standard Deviation
In Python (Pandas):
Mean, Median, Mode
Variance & Standard Deviation
2๏ธโฃ Data Visualization
Visualizing data helps identify trends, outliers, and patterns.
In SQL (For Basic Visualization in Some Databases Like PostgreSQL):
Create Histogram (Approximate in SQL)
In Python (Matplotlib & Seaborn):
Bar Chart (Category-Wise Sales)
Histogram (Salary Distribution)
Box Plot (Outliers in Sales Data)
Heatmap (Correlation Between Variables)
3๏ธโฃ Detecting Anomalies & Outliers
Outliers can skew results and should be identified.
In SQL:
Find records with unusually high salaries
In Python (Pandas & NumPy):
Using Z-Score (Values Beyond 3 Standard Deviations)
Using IQR (Interquartile Range)
4๏ธโฃ Key EDA Steps
Understand the Data โ Check missing values, duplicates, and column types
Summarize Statistics โ Mean, Median, Standard Deviation, etc.
Visualize Trends โ Histograms, Box Plots, Heatmaps
Detect Outliers & Anomalies โ Z-Score, IQR
Feature Engineering โ Transform variables if needed
Mini Task for You: Write an SQL query to find employees whose salaries are above two standard deviations from the mean salary.
Here you can find the roadmap for data analyst: https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#sql
EDA is the process of analyzing datasets to summarize key patterns, detect anomalies, and gain insights before applying machine learning or reporting.
1๏ธโฃ Descriptive Statistics
Descriptive statistics help summarize and understand data distributions.
In SQL:
Calculate Mean (Average):
SELECT AVG(salary) AS average_salary FROM employees;
Find Median (Using Window Functions) SELECT salary FROM ( SELECT salary, ROW_NUMBER() OVER (ORDER BY salary) AS row_num, COUNT(*) OVER () AS total_rows FROM employees ) subquery WHERE row_num = (total_rows / 2);
Find Mode (Most Frequent Value)
SELECT department, COUNT(*) AS count FROM employees GROUP BY department ORDER BY count DESC LIMIT 1;
Calculate Variance & Standard Deviation
SELECT VARIANCE(salary) AS salary_variance, STDDEV(salary) AS salary_std_dev FROM employees;
In Python (Pandas):
Mean, Median, Mode
df['salary'].mean() df['salary'].median() df['salary'].mode()[0]
Variance & Standard Deviation
df['salary'].var() df['salary'].std()
2๏ธโฃ Data Visualization
Visualizing data helps identify trends, outliers, and patterns.
In SQL (For Basic Visualization in Some Databases Like PostgreSQL):
Create Histogram (Approximate in SQL)
SELECT salary, COUNT(*) FROM employees GROUP BY salary ORDER BY salary;
In Python (Matplotlib & Seaborn):
Bar Chart (Category-Wise Sales)
import matplotlib.pyplot as plt
import seaborn as sns
df.groupby('category')['sales'].sum().plot(kind='bar')
plt.title('Total Sales by Category')
plt.xlabel('Category')
plt.ylabel('Sales')
plt.show()
Histogram (Salary Distribution)
sns.histplot(df['salary'], bins=10, kde=True)
plt.title('Salary Distribution')
plt.show()
Box Plot (Outliers in Sales Data)
sns.boxplot(y=df['sales'])
plt.title('Sales Data Outliers')
plt.show()
Heatmap (Correlation Between Variables)
sns.heatmap(df.corr(), annot=True, cmap='coolwarm') plt.title('Feature Correlation Heatmap') plt.show() 3๏ธโฃ Detecting Anomalies & Outliers
Outliers can skew results and should be identified.
In SQL:
Find records with unusually high salaries
SELECT * FROM employees WHERE salary > (SELECT AVG(salary) + 2 * STDDEV(salary) FROM employees);
In Python (Pandas & NumPy):
Using Z-Score (Values Beyond 3 Standard Deviations)
from scipy import stats df['z_score'] = stats.zscore(df['salary']) df_outliers = df[df['z_score'].abs() > 3]
Using IQR (Interquartile Range)
Q1 = df['salary'].quantile(0.25)
Q3 = df['salary'].quantile(0.75)
IQR = Q3 - Q1
df_outliers = df[(df['salary'] < (Q1 - 1.5 * IQR)) | (df['salary'] > (Q3 + 1.5 * IQR))]
4๏ธโฃ Key EDA Steps
Understand the Data โ Check missing values, duplicates, and column types
Summarize Statistics โ Mean, Median, Standard Deviation, etc.
Visualize Trends โ Histograms, Box Plots, Heatmaps
Detect Outliers & Anomalies โ Z-Score, IQR
Feature Engineering โ Transform variables if needed
Mini Task for You: Write an SQL query to find employees whose salaries are above two standard deviations from the mean salary.
Here you can find the roadmap for data analyst: https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#sql
โค5๐3๐1
Common Mistakes Data Analysts Must Avoid โ ๏ธ๐
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1๏ธโฃ Ignoring Data Cleaning ๐งน
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2๏ธโฃ Relying Only on Averages ๐
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3๏ธโฃ Confusing Correlation with Causation ๐
Just because two things move together doesnโt mean one causes the other. Validate assumptions before making decisions.
4๏ธโฃ Overcomplicating Visualizations ๐จ
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5๏ธโฃ Not Understanding Business Context ๐ฏ
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6๏ธโฃ Ignoring Outliers Without Investigation ๐
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7๏ธโฃ Using Small Sample Sizes โ ๏ธ
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8๏ธโฃ Failing to Communicate Insights Clearly ๐ฃ๏ธ
Great analysis means nothing if stakeholders donโt understand it. Tell a story with dataโdonโt just dump numbers.
9๏ธโฃ Not Keeping Up with Industry Trends ๐
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and youโll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1๏ธโฃ Ignoring Data Cleaning ๐งน
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2๏ธโฃ Relying Only on Averages ๐
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3๏ธโฃ Confusing Correlation with Causation ๐
Just because two things move together doesnโt mean one causes the other. Validate assumptions before making decisions.
4๏ธโฃ Overcomplicating Visualizations ๐จ
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5๏ธโฃ Not Understanding Business Context ๐ฏ
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6๏ธโฃ Ignoring Outliers Without Investigation ๐
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7๏ธโฃ Using Small Sample Sizes โ ๏ธ
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8๏ธโฃ Failing to Communicate Insights Clearly ๐ฃ๏ธ
Great analysis means nothing if stakeholders donโt understand it. Tell a story with dataโdonโt just dump numbers.
9๏ธโฃ Not Keeping Up with Industry Trends ๐
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and youโll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1๐1
SQL Advanced Concepts for Data Analyst Interviews
1. Window Functions: Gain proficiency in window functions like
2. Common Table Expressions (CTEs): Understand how to use CTEs with the
3. Recursive CTEs: Learn how to use recursive CTEs to solve hierarchical or recursive data problems, such as navigating organizational charts or bill-of-materials structures.
4. Advanced Joins: Master complex join techniques, including self-joins (joining a table with itself), cross joins (Cartesian product), and using multiple joins in a single query.
5. Subqueries and Correlated Subqueries: Be adept at writing subqueries that return a single value or a set of values. Correlated subqueries, which reference columns from the outer query, are particularly powerful for row-by-row operations.
6. Indexing Strategies: Learn advanced indexing strategies, such as covering indexes, composite indexes, and partial indexes. Understand how to optimize query performance by designing the right indexes and when to use
7. Query Optimization and Execution Plans: Develop skills in reading and interpreting SQL execution plans to understand how queries are executed. Use tools like
8. Stored Procedures: Understand how to create and use stored procedures to encapsulate complex SQL logic into reusable, modular code. Learn how to pass parameters, handle errors, and return multiple result sets from a stored procedure.
9. Triggers: Learn how to create triggers to automatically execute a specified action in response to certain events on a table (e.g.,
10. Transactions and Isolation Levels: Master the use of transactions to ensure that a series of SQL operations are executed as a single unit of work. Understand different isolation levels (
11. PIVOT and UNPIVOT: Use the
12. Dynamic SQL: Learn how to write dynamic SQL queries that are constructed and executed at runtime. This is useful when the exact SQL query cannot be determined until runtime, such as in scenarios involving user-defined filters or conditional logic.
13. Data Partitioning: Understand how to implement data partitioning strategies, such as range partitioning or list partitioning, to manage large tables efficiently. Partitioning can significantly improve query performance and manageability.
14. Temporary Tables: Learn how to create and use temporary tables to store intermediate results within a session. Understand the differences between local and global temporary tables, and when to use them.
15. Materialized Views: Use materialized views to store the result of a query physically and update it periodically. This can drastically improve performance for complex queries that need to be executed frequently.
16. Handling Complex Data Types: Understand how to work with complex data types such as JSON, XML, and arrays. Learn how to store, query, and manipulate these types in SQL databases, including using functions like
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Window Functions: Gain proficiency in window functions like
ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), and LAG()/LEAD(). These functions allow you to perform calculations across a set of table rows related to the current row without collapsing the result set into a single output.2. Common Table Expressions (CTEs): Understand how to use CTEs with the
WITH clause to create temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs improve the readability and maintainability of complex queries.3. Recursive CTEs: Learn how to use recursive CTEs to solve hierarchical or recursive data problems, such as navigating organizational charts or bill-of-materials structures.
4. Advanced Joins: Master complex join techniques, including self-joins (joining a table with itself), cross joins (Cartesian product), and using multiple joins in a single query.
5. Subqueries and Correlated Subqueries: Be adept at writing subqueries that return a single value or a set of values. Correlated subqueries, which reference columns from the outer query, are particularly powerful for row-by-row operations.
6. Indexing Strategies: Learn advanced indexing strategies, such as covering indexes, composite indexes, and partial indexes. Understand how to optimize query performance by designing the right indexes and when to use
CLUSTERED versus NON-CLUSTERED indexes.7. Query Optimization and Execution Plans: Develop skills in reading and interpreting SQL execution plans to understand how queries are executed. Use tools like
EXPLAIN or EXPLAIN ANALYZE to identify performance bottlenecks and optimize query performance.8. Stored Procedures: Understand how to create and use stored procedures to encapsulate complex SQL logic into reusable, modular code. Learn how to pass parameters, handle errors, and return multiple result sets from a stored procedure.
9. Triggers: Learn how to create triggers to automatically execute a specified action in response to certain events on a table (e.g.,
AFTER INSERT, BEFORE UPDATE). Triggers are useful for maintaining data integrity and automating workflows.10. Transactions and Isolation Levels: Master the use of transactions to ensure that a series of SQL operations are executed as a single unit of work. Understand different isolation levels (
READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE) and their impact on data consistency and concurrency.11. PIVOT and UNPIVOT: Use the
PIVOT operator to transform row data into columnar data and UNPIVOT to convert columns back into rows. These operations are crucial for reshaping data for reporting and analysis.12. Dynamic SQL: Learn how to write dynamic SQL queries that are constructed and executed at runtime. This is useful when the exact SQL query cannot be determined until runtime, such as in scenarios involving user-defined filters or conditional logic.
13. Data Partitioning: Understand how to implement data partitioning strategies, such as range partitioning or list partitioning, to manage large tables efficiently. Partitioning can significantly improve query performance and manageability.
14. Temporary Tables: Learn how to create and use temporary tables to store intermediate results within a session. Understand the differences between local and global temporary tables, and when to use them.
15. Materialized Views: Use materialized views to store the result of a query physically and update it periodically. This can drastically improve performance for complex queries that need to be executed frequently.
16. Handling Complex Data Types: Understand how to work with complex data types such as JSON, XML, and arrays. Learn how to store, query, and manipulate these types in SQL databases, including using functions like
JSON_EXTRACT(), XMLQUERY(), or array functions.Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2โ1
Common Mistakes Data Analysts Must Avoid โ ๏ธ๐
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1๏ธโฃ Ignoring Data Cleaning ๐งน
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2๏ธโฃ Relying Only on Averages ๐
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3๏ธโฃ Confusing Correlation with Causation ๐
Just because two things move together doesnโt mean one causes the other. Validate assumptions before making decisions.
4๏ธโฃ Overcomplicating Visualizations ๐จ
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5๏ธโฃ Not Understanding Business Context ๐ฏ
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6๏ธโฃ Ignoring Outliers Without Investigation ๐
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7๏ธโฃ Using Small Sample Sizes โ ๏ธ
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8๏ธโฃ Failing to Communicate Insights Clearly ๐ฃ๏ธ
Great analysis means nothing if stakeholders donโt understand it. Tell a story with dataโdonโt just dump numbers.
9๏ธโฃ Not Keeping Up with Industry Trends ๐
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and youโll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Even experienced analysts can fall into these traps. Avoid these mistakes to ensure accurate, impactful analysis!
1๏ธโฃ Ignoring Data Cleaning ๐งน
Messy data leads to misleading insights. Always check for missing values, duplicates, and inconsistencies before analysis.
2๏ธโฃ Relying Only on Averages ๐
Averages hide variability. Always check median, percentiles, and distributions for a complete picture.
3๏ธโฃ Confusing Correlation with Causation ๐
Just because two things move together doesnโt mean one causes the other. Validate assumptions before making decisions.
4๏ธโฃ Overcomplicating Visualizations ๐จ
Too many colors, labels, or complex charts confuse your audience. Keep it simple, clear, and focused on key takeaways.
5๏ธโฃ Not Understanding Business Context ๐ฏ
Data without context is meaningless. Always ask: "What problem are we solving?" before diving into numbers.
6๏ธโฃ Ignoring Outliers Without Investigation ๐
Outliers can signal errors or valuable insights. Always analyze why they exist before deciding to remove them.
7๏ธโฃ Using Small Sample Sizes โ ๏ธ
Drawing conclusions from too little data leads to unreliable insights. Ensure your sample size is statistically significant.
8๏ธโฃ Failing to Communicate Insights Clearly ๐ฃ๏ธ
Great analysis means nothing if stakeholders donโt understand it. Tell a story with dataโdonโt just dump numbers.
9๏ธโฃ Not Keeping Up with Industry Trends ๐
Data tools and techniques evolve fast. Keep learning SQL, Python, Power BI, Tableau, and machine learning basics.
Avoid these mistakes, and youโll stand out as a reliable data analyst!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1๐1
Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค4๐1
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐๐ ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ โ ๐ช๐ต๐ถ๐ฐ๐ต ๐ฃ๐ฎ๐๐ต ๐ถ๐ ๐ฅ๐ถ๐ด๐ต๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐? ๐ค
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
In todayโs data-driven world, career clarity can make all the difference. Whether youโre starting out in analytics, pivoting into data science, or aligning business with data as an analyst โ understanding the core responsibilities, skills, and tools of each role is crucial.
๐ Hereโs a quick breakdown from a visual I often refer to when mentoring professionals:
๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Analyzing historical data to inform decisions.
๓ ฏโข๓ Skills: SQL, basic stats, data visualization, reporting.
๓ ฏโข๓ Tools: Excel, Tableau, Power BI, SQL.
๐น ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
๓ ฏโข๓ Focus: Predictive modeling, ML, complex data analysis.
๓ ฏโข๓ Skills: Programming, ML, deep learning, stats.
๓ ฏโข๓ Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
๐น ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐
๓ ฏโข๓ Focus: Bridging business needs with data insights.
๓ ฏโข๓ Skills: Communication, stakeholder management, process modeling.
๓ ฏโข๓ Tools: Microsoft Office, BI tools, business process frameworks.
๐ ๐ ๐ ๐๐ฑ๐๐ถ๐ฐ๐ฒ:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
๐ ๐ง๐ฎ๐ธ๐ฒ ๐๐ถ๐บ๐ฒ ๐๐ผ ๐๐ฒ๐น๐ณ-๐ฎ๐๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ ๐ฎ ๐ฝ๐ฎ๐๐ต ๐๐ต๐ฎ๐ ๐ฒ๐ป๐ฒ๐ฟ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐, not just one thatโs trending.
โค1
๐ฅ Step-by-step Data Analysis Projects with SQL
Below are popular data projects from Kaggle, GitHub and Medium and YouTube. They will:
- Help you gain skills in working with real data
- Introduce you to SQL for data analysis
- Inspire you to undertake your own data analysis projects
๐บ Real World Fake Data Analysis
๐ Housing sales in Nashville
๐ Walmart Sales Analysis SQL Project
๐งณ Alex the Analyst SQL Project
๐ค Superstore Sales Analysis using SQL
๐ธ International Debt Analysis using SQL
โฝ๏ธ Soccer Game Analysis using SQL
๐ World Population Analysis 2015 using SQL
๐ SQL Project for Data Analysis
๐ Public Transportation Data Analysis using SQL
๐ธ Instagram User Data Analysis using SQL
๐ HR Data Analysis using SQL
๐ฌ Data Analyst Project: Step-by-step analysis with SQL
๐ผ Music Store Data Analysis Project Using SQL
โ Top 10 SQL Projects with Datasets
โ Roadmap to Master SQL
#DataAnalyst #DataAnalytics #DataAnalysis #data_analyst #sql
If you find this useful, give it a๐
Below are popular data projects from Kaggle, GitHub and Medium and YouTube. They will:
- Help you gain skills in working with real data
- Introduce you to SQL for data analysis
- Inspire you to undertake your own data analysis projects
๐บ Real World Fake Data Analysis
๐ Housing sales in Nashville
๐ Walmart Sales Analysis SQL Project
๐งณ Alex the Analyst SQL Project
๐ค Superstore Sales Analysis using SQL
๐ธ International Debt Analysis using SQL
โฝ๏ธ Soccer Game Analysis using SQL
๐ World Population Analysis 2015 using SQL
๐ SQL Project for Data Analysis
๐ Public Transportation Data Analysis using SQL
๐ธ Instagram User Data Analysis using SQL
๐ HR Data Analysis using SQL
๐ฌ Data Analyst Project: Step-by-step analysis with SQL
๐ผ Music Store Data Analysis Project Using SQL
โ Top 10 SQL Projects with Datasets
โ Roadmap to Master SQL
#DataAnalyst #DataAnalytics #DataAnalysis #data_analyst #sql
If you find this useful, give it a๐
โค4
If you are trying to transition into the data analytics domain and getting started with SQL, focus on the most useful concept that will help you solve the majority of the problems, and then try to learn the rest of the topics:
๐๐ป Basic Aggregation function:
1๏ธโฃ AVG
2๏ธโฃ COUNT
3๏ธโฃ SUM
4๏ธโฃ MIN
5๏ธโฃ MAX
๐๐ป JOINS
1๏ธโฃ Left
2๏ธโฃ Inner
3๏ธโฃ Self (Important, Practice questions on self join)
๐๐ป Windows Function (Important)
1๏ธโฃ Learn how partitioning works
2๏ธโฃ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3๏ธโฃ Use Cases of LEAD & LAG functions
4๏ธโฃ Use cases of Aggregate window functions
๐๐ป GROUP BY
๐๐ป WHERE vs HAVING
๐๐ป CASE STATEMENT
๐๐ป UNION vs Union ALL
๐๐ป LOGICAL OPERATORS
Other Commonly used functions:
๐๐ป IFNULL
๐๐ป COALESCE
๐๐ป ROUND
๐๐ป Working with Date Functions
1๏ธโฃ EXTRACTING YEAR/MONTH/WEEK/DAY
2๏ธโฃ Calculating date differences
๐๐ปCTE
๐๐ปViews & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐๐ป Basic Aggregation function:
1๏ธโฃ AVG
2๏ธโฃ COUNT
3๏ธโฃ SUM
4๏ธโฃ MIN
5๏ธโฃ MAX
๐๐ป JOINS
1๏ธโฃ Left
2๏ธโฃ Inner
3๏ธโฃ Self (Important, Practice questions on self join)
๐๐ป Windows Function (Important)
1๏ธโฃ Learn how partitioning works
2๏ธโฃ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3๏ธโฃ Use Cases of LEAD & LAG functions
4๏ธโฃ Use cases of Aggregate window functions
๐๐ป GROUP BY
๐๐ป WHERE vs HAVING
๐๐ป CASE STATEMENT
๐๐ป UNION vs Union ALL
๐๐ป LOGICAL OPERATORS
Other Commonly used functions:
๐๐ป IFNULL
๐๐ป COALESCE
๐๐ป ROUND
๐๐ป Working with Date Functions
1๏ธโฃ EXTRACTING YEAR/MONTH/WEEK/DAY
2๏ธโฃ Calculating date differences
๐๐ปCTE
๐๐ปViews & Triggers (optional)
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค3
Data Analytics Pattern Identification....;;
Trend Analysis: Examining data over time to identify upward or downward trends.
Seasonal Patterns: Identifying recurring patterns or trends based on seasons or specific time periods
Correlation: Understanding relationships between variables and how changes in one may affect another.
Outlier Detection: Identifying data points that deviate significantly from the overall pattern.
Clustering: Grouping similar data points together to find natural patterns within the data.
Classification: Categorizing data into predefined classes or groups based on certain features.
Regression Analysis: Predicting a dependent variable based on the values of independent variables.
Frequency Distribution: Analyzing the distribution of values within a dataset.
Pattern Recognition: Identifying recurring structures or shapes within the data.
Text Analysis: Extracting insights from unstructured text data through techniques like sentiment analysis or topic modeling.
These patterns help organizations make informed decisions, optimize processes, and gain a deeper understanding of their data.
Trend Analysis: Examining data over time to identify upward or downward trends.
Seasonal Patterns: Identifying recurring patterns or trends based on seasons or specific time periods
Correlation: Understanding relationships between variables and how changes in one may affect another.
Outlier Detection: Identifying data points that deviate significantly from the overall pattern.
Clustering: Grouping similar data points together to find natural patterns within the data.
Classification: Categorizing data into predefined classes or groups based on certain features.
Regression Analysis: Predicting a dependent variable based on the values of independent variables.
Frequency Distribution: Analyzing the distribution of values within a dataset.
Pattern Recognition: Identifying recurring structures or shapes within the data.
Text Analysis: Extracting insights from unstructured text data through techniques like sentiment analysis or topic modeling.
These patterns help organizations make informed decisions, optimize processes, and gain a deeper understanding of their data.
โค1
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MEE6 in Telegram ๐ฅ
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Stop juggling bots โT22 is MissRose x GroupHelp x Safeguard with a mini-app dashboard!
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Discover T22 ๐
By MEE6 Creator
๐ค T22 - The best-in-class telegram group bot!
Stop juggling bots โT22 is MissRose x GroupHelp x Safeguard with a mini-app dashboard!
๐ Verification & Captcha
๐ก Advanced Moderation Tools
๐ Leveling System
๐ฌ Smart Welcome Flows
๐ฆ Twitter Raids
๐ง Mini-App Dashboard
๐ฆ Miss Rose Config Importer
Discover T22 ๐
By MEE6 Creator
โค2
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Here is the list of best curated Telegram Channels for free education ๐๐
Free Courses with Certificate
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Data Science & Machine Learning
Programming Free Books
Python Free Courses
Python Interview Resources
Ethical Hacking & Cyber Security
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Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Learn Digital Marketing
Crack your coding Interviews
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To effectively learn SQL for a Data Analyst role, follow these steps:
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.
2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.
3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."
4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.
5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.
By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.
โค5
Start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
Start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
๐๐ฎ๐ง๐ข๐จ๐ซ ๐ฏ๐ฌ. ๐๐๐ง๐ข๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
Whatโs the real difference between Junior and Senior Data Analyst?
Itโs not just SQL skills or years on the job โ itโs how they think.
๐Juniors code right away
๐ง Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
๐Juniors follow orders
๐ง Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
๐Juniors patch data
๐ง Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
๐Juniors stall in chaos
๐ง Seniors make a plan
Example: Juniors wait, Seniors step up.
๐Juniors focus on tasks
๐ง Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
๐Juniors guess
๐ง Seniors clarify
Example: Juniors assume, Seniors ask the team.
๐Juniors stick to old tools
๐ง Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
๐Juniors give data
๐ง Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
Whatโs the real difference between Junior and Senior Data Analyst?
Itโs not just SQL skills or years on the job โ itโs how they think.
๐Juniors code right away
๐ง Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
๐Juniors follow orders
๐ง Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
๐Juniors patch data
๐ง Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
๐Juniors stall in chaos
๐ง Seniors make a plan
Example: Juniors wait, Seniors step up.
๐Juniors focus on tasks
๐ง Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
๐Juniors guess
๐ง Seniors clarify
Example: Juniors assume, Seniors ask the team.
๐Juniors stick to old tools
๐ง Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
๐Juniors give data
๐ง Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
โค3
๐ Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
๐๐
https://t.iss.one/sqlspecialist/379
ENJOY LEARNING ๐๐
โค1