Microsoft Excel → Python:
In Excel, you'd use =AVERAGE(TableName[ColumnName]) to find the average.
In Python:
TableName['ColumnName'].mean()
One line.
Works even if you have 10 million rows.
In Excel, you'd use =AVERAGE(TableName[ColumnName]) to find the average.
In Python:
TableName['ColumnName'].mean()
One line.
Works even if you have 10 million rows.
❤5👍1
5 misconceptions about data analytics (and what's actually true):
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
👍4
I once told a hiring manager I was “proficient in SQL.”
In reality, I had watched half a YouTube tutorial on 2x speed.
In the interview, she said:
“What’s the difference between INNER JOIN and LEFT JOIN?”
I said:
“It depends on your mindset.”
I blacked out.
She smiled. I think it was pity.
Lesson?
Lie if you must. But memorize the script.
And never lie about tech. They will test you immediately.
In reality, I had watched half a YouTube tutorial on 2x speed.
In the interview, she said:
“What’s the difference between INNER JOIN and LEFT JOIN?”
I said:
“It depends on your mindset.”
I blacked out.
She smiled. I think it was pity.
Lesson?
Lie if you must. But memorize the script.
And never lie about tech. They will test you immediately.
👍3👨💻2❤1
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
I once told a hiring manager I was “proficient in SQL.” In reality, I had watched half a YouTube tutorial on 2x speed. In the interview, she said: “What’s the difference between INNER JOIN and LEFT JOIN?” I said: “It depends on your mindset.” I blacked out.…
“While noting that I labeled myself as proficient in SQL, I can’t tell you the difference right off the top of my head. A quick search to refresh my memory on JOINs would enable me to answer that for you. While I may not remember 100% of the details of SQL, I am not afraid to do research for a question or process I don’t have a clear answer to.” 😂
👍3
Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
👍3❤2
✔️📚A beginner's roadmap for learning SQL:
🔺Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔺Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔺Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔺Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔺Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔺Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔺Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔺Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔺Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔺Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔺Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔺Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔺Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔺Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔺Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning.
Happy Learning 🥳 📚
🔺Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔺Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔺Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔺Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔺Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔺Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔺Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔺Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔺Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔺Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔺Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔺Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔺Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔺Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔺Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning.
Happy Learning 🥳 📚
👍3
Data Analyst Vs Data Scientist
**Data Analyst******
Focus: Data analysts primarily work with existing data sets to extract meaningful insights and draw conclusions.
Skills: They possess strong skills in data cleaning, data visualization, and statistical analysis. They are proficient in tools like Excel, SQL, and data visualization software.
Responsibilities: Data analysts are responsible for gathering, organizing, and cleaning data. They perform exploratory data analysis, generate reports, and create visualizations to communicate findings to stakeholders.
Goals: They aim to identify trends, patterns, and correlations within the data, and provide actionable recommendations based on their analysis.
Domain Expertise: They may specialize in specific business domains and apply their analytical skills to solve domain-specific problems.
***Data Scientist:***
Focus: Data scientists are involved in both analyzing existing data and developing predictive models or algorithms to solve complex problems.
Skills: They have a strong foundation in mathematics, statistics, programming, and machine learning. They are proficient in languages like Python or R and have knowledge of advanced statistical techniques.
Responsibilities: Data scientists collect and analyze data, develop and implement predictive models and algorithms, and apply machine learning techniques to extract insights and make predictions. They also work on data preprocessing, feature engineering, and model evaluation.
Goals: They aim to uncover hidden patterns, create predictive models, and make data-driven decisions. They often deal with large volumes of unstructured or complex data.
Domain Expertise: They possess a deep understanding of statistical and machine learning concepts and can apply their expertise across various domains.
In summary, data analysts focus on analyzing and interpreting existing data sets to generate insights, while data scientists have a broader skill set and are involved in developing models and algorithms to solve complex problems. Data scientists require a deeper knowledge of mathematics, statistics, and programming, including machine learning techniques.
**Data Analyst******
Focus: Data analysts primarily work with existing data sets to extract meaningful insights and draw conclusions.
Skills: They possess strong skills in data cleaning, data visualization, and statistical analysis. They are proficient in tools like Excel, SQL, and data visualization software.
Responsibilities: Data analysts are responsible for gathering, organizing, and cleaning data. They perform exploratory data analysis, generate reports, and create visualizations to communicate findings to stakeholders.
Goals: They aim to identify trends, patterns, and correlations within the data, and provide actionable recommendations based on their analysis.
Domain Expertise: They may specialize in specific business domains and apply their analytical skills to solve domain-specific problems.
***Data Scientist:***
Focus: Data scientists are involved in both analyzing existing data and developing predictive models or algorithms to solve complex problems.
Skills: They have a strong foundation in mathematics, statistics, programming, and machine learning. They are proficient in languages like Python or R and have knowledge of advanced statistical techniques.
Responsibilities: Data scientists collect and analyze data, develop and implement predictive models and algorithms, and apply machine learning techniques to extract insights and make predictions. They also work on data preprocessing, feature engineering, and model evaluation.
Goals: They aim to uncover hidden patterns, create predictive models, and make data-driven decisions. They often deal with large volumes of unstructured or complex data.
Domain Expertise: They possess a deep understanding of statistical and machine learning concepts and can apply their expertise across various domains.
In summary, data analysts focus on analyzing and interpreting existing data sets to generate insights, while data scientists have a broader skill set and are involved in developing models and algorithms to solve complex problems. Data scientists require a deeper knowledge of mathematics, statistics, and programming, including machine learning techniques.
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7 Essential Data Analysis Techniques You Need to Know in 2025
✅ Exploratory Data Analysis (EDA) – Uncover patterns, spot anomalies, and visualize distributions before diving deeper
✅ Time Series Analysis – Analyze trends over time, forecast future values (using ARIMA or Prophet)
✅ Hypothesis Testing – Use statistical tests (T-tests, Chi-square) to validate assumptions and claims
✅ Regression Analysis – Predict continuous variables using linear or non-linear models
✅ Cluster Analysis – Group similar data points using K-means or hierarchical clustering
✅ Dimensionality Reduction – Simplify complex datasets using PCA (Principal Component Analysis)
✅ Classification Algorithms – Predict categorical outcomes with decision trees, random forests, and SVMs
Mastering these will give you the edge in any data analysis role.
Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
✅ Exploratory Data Analysis (EDA) – Uncover patterns, spot anomalies, and visualize distributions before diving deeper
✅ Time Series Analysis – Analyze trends over time, forecast future values (using ARIMA or Prophet)
✅ Hypothesis Testing – Use statistical tests (T-tests, Chi-square) to validate assumptions and claims
✅ Regression Analysis – Predict continuous variables using linear or non-linear models
✅ Cluster Analysis – Group similar data points using K-means or hierarchical clustering
✅ Dimensionality Reduction – Simplify complex datasets using PCA (Principal Component Analysis)
✅ Classification Algorithms – Predict categorical outcomes with decision trees, random forests, and SVMs
Mastering these will give you the edge in any data analysis role.
Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Excel vs SQL vs Python (pandas):
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
👍5❤2
Forwarded from SQL Programming Resources
SQL Guide with Free Resources.pdf
5.1 MB
React with ♥️ for more free resources
❤13👍1
I’m a data analyst
2022:
. Got my first analyst job
. Never used PowerBi
. Only knew Pivot tables
. Didn’t really understand SQL
2025:
. 2 years data consulting
. Lead analyst for $100m project
. Love my job and look forward to Mondays
A lot can change in 3 years - Never Give Up.
2022:
. Got my first analyst job
. Never used PowerBi
. Only knew Pivot tables
. Didn’t really understand SQL
2025:
. 2 years data consulting
. Lead analyst for $100m project
. Love my job and look forward to Mondays
A lot can change in 3 years - Never Give Up.
🔥7👍3
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.iss.one/pythonanalyst
ENJOY LEARNING 👍👍
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.iss.one/pythonanalyst
ENJOY LEARNING 👍👍
👍4
Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_analyst
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/jobs_SQL
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/machinelearning_deeplearning
Data Engineering: https://t.iss.one/sql_engineer
Data Analysts: https://t.iss.one/sqlspecialist
Hope it helps :)
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_analyst
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/jobs_SQL
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/machinelearning_deeplearning
Data Engineering: https://t.iss.one/sql_engineer
Data Analysts: https://t.iss.one/sqlspecialist
Hope it helps :)
❤2👍1
Want to make a transition to a career in data?
Here is a 7-step plan for each data role
Data Scientist
Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.
Data Analyst
Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.
Data Engineer
SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.
#data
Here is a 7-step plan for each data role
Data Scientist
Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.
Data Analyst
Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.
Data Engineer
SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.
#data
❤5
9 secrets about Data Storytelling every analyst should know (number 6 is a must):
1/ Start with the end in mind—what’s the key takeaway?
2/ Don’t just present numbers—explain the 'so what' behind them.
3/ Data should drive decisions—frame your analysis as a solution to a problem.
#DataAnalytics
4/ Visualise trends over time to tell a story.
5/ Add context to your data—it makes your insights relevant.
6/ Speak the language of your audience—simplify complex terms.
7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon.
8/ Include both the big picture and the details—it appeals to different stakeholders.
9/ Conclude with a call to action—what should they do next?
1/ Start with the end in mind—what’s the key takeaway?
2/ Don’t just present numbers—explain the 'so what' behind them.
3/ Data should drive decisions—frame your analysis as a solution to a problem.
#DataAnalytics
4/ Visualise trends over time to tell a story.
5/ Add context to your data—it makes your insights relevant.
6/ Speak the language of your audience—simplify complex terms.
7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon.
8/ Include both the big picture and the details—it appeals to different stakeholders.
9/ Conclude with a call to action—what should they do next?
👍6