8 (and forever): keep learning!
The learning doesn’t stop when you start applying for jobs or even when you get one!
Always be learning something new.
Note: even with hard work, this can be a tough, time-intensive process.
Hang in there and keep the faith!
You got this.
Learn Data Analytics
The learning doesn’t stop when you start applying for jobs or even when you get one!
Always be learning something new.
Note: even with hard work, this can be a tough, time-intensive process.
Hang in there and keep the faith!
You got this.
Learn Data Analytics
❤7👍2
Imp interview Q&A for numpy in Data analyst
1.Creating an array and performing basic operations:
import numpy as np
# Creating a 1D array
array = np.array([1, 2, 3, 4, 5])
print("Original array:", array)
# Adding 10 to each element
array_plus_ten = array + 10
print("Array after adding 10:", array_plus_ten)
# Multiplying each element by 2
array_times_two = array * 2
print("Array after multiplying by 2:", array_times_two)
2. Creating a 2D array and accessing element:
# Creating a 2D array (matrix)
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Original matrix:\n", matrix)
# Accessing a specific element
element = matrix[1, 2]
print("Element at row 2, column 3:", element)
# Accessing a specific row
row = matrix[1, :]
print("Second row:", row)
# Accessing a specific column
column = matrix[:, 2]
print("Third column:", column)
3.Basic statistical operations on an array:
# Creating an array
data = np.array([10, 20, 30, 40, 50])
# Calculating the mean
mean = np.mean(data)
print("Mean:", mean)
# Calculating the median
median = np.median(data)
print("Median:", median)
# Calculating the standard deviation
std_dev = np.std(data)
print("Standard Deviation:", std_dev)
4.Creating arrays using different functions and reshaping:
# Creating an array of zeros
zeros_array = np.zeros((3, 3))
print("Array of zeros:\n", zeros_array)
# Creating an array of ones
ones_array = np.ones((2, 4))
print("Array of ones:\n", ones_array)
# Creating an array with a range of values
range_array = np.arange(10)
print("Array with a range of values:", range_array)
# Reshaping an array
reshaped_array = range_array.reshape(2, 5)
print("Reshaped array (2x5):\n", reshaped_array)
5. Element-wise operations and broadcasting:
# Creating two arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Element-wise addition
addition = array1 + array2
print("Element-wise addition:", addition)
# Element-wise multiplication
multiplication = array1 * array2
print("Element-wise multiplication:", multiplication)
# Broadcasting: adding a scalar to an array
scalar_addition = array1 + 10
print("Array after adding 10 to each element:", scalar_addition)
1.Creating an array and performing basic operations:
import numpy as np
# Creating a 1D array
array = np.array([1, 2, 3, 4, 5])
print("Original array:", array)
# Adding 10 to each element
array_plus_ten = array + 10
print("Array after adding 10:", array_plus_ten)
# Multiplying each element by 2
array_times_two = array * 2
print("Array after multiplying by 2:", array_times_two)
2. Creating a 2D array and accessing element:
# Creating a 2D array (matrix)
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Original matrix:\n", matrix)
# Accessing a specific element
element = matrix[1, 2]
print("Element at row 2, column 3:", element)
# Accessing a specific row
row = matrix[1, :]
print("Second row:", row)
# Accessing a specific column
column = matrix[:, 2]
print("Third column:", column)
3.Basic statistical operations on an array:
# Creating an array
data = np.array([10, 20, 30, 40, 50])
# Calculating the mean
mean = np.mean(data)
print("Mean:", mean)
# Calculating the median
median = np.median(data)
print("Median:", median)
# Calculating the standard deviation
std_dev = np.std(data)
print("Standard Deviation:", std_dev)
4.Creating arrays using different functions and reshaping:
# Creating an array of zeros
zeros_array = np.zeros((3, 3))
print("Array of zeros:\n", zeros_array)
# Creating an array of ones
ones_array = np.ones((2, 4))
print("Array of ones:\n", ones_array)
# Creating an array with a range of values
range_array = np.arange(10)
print("Array with a range of values:", range_array)
# Reshaping an array
reshaped_array = range_array.reshape(2, 5)
print("Reshaped array (2x5):\n", reshaped_array)
5. Element-wise operations and broadcasting:
# Creating two arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Element-wise addition
addition = array1 + array2
print("Element-wise addition:", addition)
# Element-wise multiplication
multiplication = array1 * array2
print("Element-wise multiplication:", multiplication)
# Broadcasting: adding a scalar to an array
scalar_addition = array1 + 10
print("Array after adding 10 to each element:", scalar_addition)
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Forwarded from Python Projects & Resources
Linkedin
Complete Python Topics for Data Analysts 😄👇 | Data Analytics
Complete Python Topics for Data Analysts 😄👇
Python for Data Analysis: https://t.iss.one/pythonanalyst
1. Introduction to Python:
- Variables, data types, and basic operations.
- Control structures (if statements, loops).
- Functions and modules.
2.…
Python for Data Analysis: https://t.iss.one/pythonanalyst
1. Introduction to Python:
- Variables, data types, and basic operations.
- Control structures (if statements, loops).
- Functions and modules.
2.…
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Complete Data Analytics Mastery: From Basics to Advanced 🚀
Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials
Grasp these essentials in just a week to build a solid foundation in data analytics.
Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics
Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.
Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics
These advanced concepts can be mastered in a couple of weeks with focused study and practice.
Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets
Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.
Best platforms to learn:
- Intro to Data Analysis
- Udacity's Data Analyst Nanodegree
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Resume Checklist
- Learning SQL FREE Book
Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩💻👨💻
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials
Grasp these essentials in just a week to build a solid foundation in data analytics.
Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics
Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.
Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics
These advanced concepts can be mastered in a couple of weeks with focused study and practice.
Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets
Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.
Best platforms to learn:
- Intro to Data Analysis
- Udacity's Data Analyst Nanodegree
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Resume Checklist
- Learning SQL FREE Book
Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩💻👨💻
Join @free4unow_backup for more free resources.
Like this post if it helps 😄❤️
ENJOY LEARNING 👍👍
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Some basic concepts regarding data and database
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
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10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
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Data Analysis is not Power BI.
Data Analysis is not Python.
Data Analysis is not Excel.
Data Analysis is not SQL.
Data Analysis is the silent hero pulling strings behind the curtain to transform raw, unstructured data into meaningful insights.
It's an art form that goes beyond the tools. Perhaps it's time we shift our focus from the tools to the art and science of data analysis itself.
Data Analysis is not Python.
Data Analysis is not Excel.
Data Analysis is not SQL.
Data Analysis is the silent hero pulling strings behind the curtain to transform raw, unstructured data into meaningful insights.
It's an art form that goes beyond the tools. Perhaps it's time we shift our focus from the tools to the art and science of data analysis itself.
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Being analytical is a skill, but it's more of a mindset and a second nature
Focusing on just numbers could be analysis, but doesn't necessarily mean you're analytical. E.g. "Sales dropped in Q1 by 5% as compared to Q1 last year in XYZ Region."
What caused this exactly? Season? Event? Product reviews/quality? Customer service decline? Marketing spend? PR? Supply chain? Stock depletion? Price increase? Rebranding?
Or, when validating the data and understanding the root causes, having a very limited approach. "If that data is missing, it's missing..."
Why is it missing? Is it the source? Is it the business decision to not undertake an activity for a time period? Was it there yesterday? Was it supposed to be there? Who can I talk to for understanding the root cause?
A LOT of business users I know are more analytical than the data people in their teams.
So what makes you analytical?
- It's the questions you ask yourself
- It's the dots you connect
- It's the different avenues you explore
- It's the inferences you make
- It's the bigger picture you look at
It's not just numbers or data.
Focusing on just numbers could be analysis, but doesn't necessarily mean you're analytical. E.g. "Sales dropped in Q1 by 5% as compared to Q1 last year in XYZ Region."
What caused this exactly? Season? Event? Product reviews/quality? Customer service decline? Marketing spend? PR? Supply chain? Stock depletion? Price increase? Rebranding?
Or, when validating the data and understanding the root causes, having a very limited approach. "If that data is missing, it's missing..."
Why is it missing? Is it the source? Is it the business decision to not undertake an activity for a time period? Was it there yesterday? Was it supposed to be there? Who can I talk to for understanding the root cause?
A LOT of business users I know are more analytical than the data people in their teams.
So what makes you analytical?
- It's the questions you ask yourself
- It's the dots you connect
- It's the different avenues you explore
- It's the inferences you make
- It's the bigger picture you look at
It's not just numbers or data.
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I hate to tell you this but...
Bootcamps that tell you they can get you a 6-figure data analyst job within 6 weeks (or even 6 months) are lying to you.
Don't focus on the salary that you might get.
Instead, focus on...
- learning the tools
- starting your portfolio
- revamping your resume
- getting active on LinkedIn
- putting the skills into practice
I guarantee you'll be more successful.
Bootcamps that tell you they can get you a 6-figure data analyst job within 6 weeks (or even 6 months) are lying to you.
Don't focus on the salary that you might get.
Instead, focus on...
- learning the tools
- starting your portfolio
- revamping your resume
- getting active on LinkedIn
- putting the skills into practice
I guarantee you'll be more successful.
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Forwarded from Data Analytics
Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview?
I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.
The right time to start applying for data analyst positions depends on a few factors:
1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.
2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.
3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.
4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.
Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.
Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.
Hope it helps :)
I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.
The right time to start applying for data analyst positions depends on a few factors:
1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.
2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.
3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.
4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.
Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.
Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.
Hope it helps :)
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Websites to practice SQL queries
👇👇
https://www.linkedin.com/posts/sql-analysts_learning-sql-is-not-enough-you-need-to-practice-activity-7217873462596386816-E65q?
Like for more ❤️
👇👇
https://www.linkedin.com/posts/sql-analysts_learning-sql-is-not-enough-you-need-to-practice-activity-7217873462596386816-E65q?
Like for more ❤️
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