If you're serious about getting into Data Science with Python, follow this 5-step roadmap.
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
👍5❤2
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://t.iss.one/sqlspecialist
Hope this helps you 😊
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://t.iss.one/sqlspecialist
Hope this helps you 😊
❤9
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
❤10
Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
❤8🔥2
Essential Topics to Master Data Analytics Interviews: 🚀
SQL:
1. Foundations
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some ❤️ if you're ready to elevate your data analytics journey! 📊
ENJOY LEARNING 👍👍
SQL:
1. Foundations
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some ❤️ if you're ready to elevate your data analytics journey! 📊
ENJOY LEARNING 👍👍
❤10👍2
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 — 𝗪𝗵𝗶𝗰𝗵 𝗣𝗮𝘁𝗵 𝗶𝘀 𝗥𝗶𝗴𝗵𝘁 𝗳𝗼𝗿 𝗬𝗼𝘂? 🤔
In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.
🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:
🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Analyzing historical data to inform decisions.
• Skills: SQL, basic stats, data visualization, reporting.
• Tools: Excel, Tableau, Power BI, SQL.
🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
• Focus: Predictive modeling, ML, complex data analysis.
• Skills: Programming, ML, deep learning, stats.
• Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Bridging business needs with data insights.
• Skills: Communication, stakeholder management, process modeling.
• Tools: Microsoft Office, BI tools, business process frameworks.
👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:
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.
🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.
In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.
🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:
🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Analyzing historical data to inform decisions.
• Skills: SQL, basic stats, data visualization, reporting.
• Tools: Excel, Tableau, Power BI, SQL.
🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
• Focus: Predictive modeling, ML, complex data analysis.
• Skills: Programming, ML, deep learning, stats.
• Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Bridging business needs with data insights.
• Skills: Communication, stakeholder management, process modeling.
• Tools: Microsoft Office, BI tools, business process frameworks.
👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:
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.
🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.
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Python for Data Analytics - Quick Cheatsheet with Cod e Example 🚀
1️⃣ Data Manipulation with Pandas
2️⃣ Numerical Operations with NumPy
3️⃣ Data Visualization with Matplotlib & Seaborn
4️⃣ Exploratory Data Analysis (EDA)
5️⃣ Working with Databases (SQL + Python)
React with ❤️ for more
1️⃣ Data Manipulation with Pandas
import pandas as pd
df = pd.read_csv("data.csv")
df.to_excel("output.xlsx")
df.head()
df.info()
df.describe()
df[df["sales"] > 1000]
df[["name", "price"]]
df.fillna(0, inplace=True)
df.dropna(inplace=True)
2️⃣ Numerical Operations with NumPy
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr.shape)
np.mean(arr)
np.median(arr)
np.std(arr)
3️⃣ Data Visualization with Matplotlib & Seaborn
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])
plt.bar(["A", "B", "C"], [5, 15, 25])
plt.show()
import seaborn as sns
sns.heatmap(df.corr(), annot=True)
sns.boxplot(x="category", y="sales", data=df)
plt.show()
4️⃣ Exploratory Data Analysis (EDA)
df.isnull().sum()
df.corr()
sns.histplot(df["sales"], bins=30)
sns.boxplot(y=df["price"])
5️⃣ Working with Databases (SQL + Python)
import sqlite3
conn = sqlite3.connect("database.db")
df = pd.read_sql("SELECT * FROM sales", conn)
conn.close()
cursor = conn.cursor()
cursor.execute("SELECT AVG(price) FROM products")
result = cursor.fetchone()
print(result)
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Call for papers on AI to AI Journey* conference journal has started!
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
📖 The journal:
• Indexed in the largest bibliographic databases of scientific citations
• Accessible to an international audience and published in the world’s digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
Prize for the best scientific paper - 1 million roubles!
Selected papers will be published in the scientific journal Doklady Mathematics.
📖 The journal:
• Indexed in the largest bibliographic databases of scientific citations
• Accessible to an international audience and published in the world’s digital libraries
Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!
More detailed information can be found in the Selection Rules -> AI Journey
*AI Journey - a major online conference in the field of AI technologies
👍4❤2
©How fresher can get a job as a data scientist?©
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
❤6
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
React ❤️ for more
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
React ❤️ for more
❤7
📌 Roadmap to Master Machine Learning in 6 Steps
Whether you're just starting or looking to go pro in ML, this roadmap will keep you on track:
1️⃣ Learn the Fundamentals
Build a math foundation (algebra, calculus, stats) + Python + libraries like NumPy & Pandas
2️⃣ Learn Essential ML Concepts
Start with supervised learning (regression, classification), then unsupervised learning (K-Means, PCA)
3️⃣ Understand Data Handling
Clean, transform, and visualize data effectively using summary stats & feature engineering
4️⃣ Explore Advanced Techniques
Delve into ensemble methods, CNNs, deep learning, and NLP fundamentals
5️⃣ Learn Model Deployment
Use Flask, FastAPI, and cloud platforms (AWS, GCP) for scalable deployment
6️⃣ Build Projects & Network
Participate in Kaggle, create portfolio projects, and connect with the ML community
React ❤️ for more
Whether you're just starting or looking to go pro in ML, this roadmap will keep you on track:
1️⃣ Learn the Fundamentals
Build a math foundation (algebra, calculus, stats) + Python + libraries like NumPy & Pandas
2️⃣ Learn Essential ML Concepts
Start with supervised learning (regression, classification), then unsupervised learning (K-Means, PCA)
3️⃣ Understand Data Handling
Clean, transform, and visualize data effectively using summary stats & feature engineering
4️⃣ Explore Advanced Techniques
Delve into ensemble methods, CNNs, deep learning, and NLP fundamentals
5️⃣ Learn Model Deployment
Use Flask, FastAPI, and cloud platforms (AWS, GCP) for scalable deployment
6️⃣ Build Projects & Network
Participate in Kaggle, create portfolio projects, and connect with the ML community
React ❤️ for more
❤6
If you're serious about getting into Data Science with Python, follow this 5-step roadmap.
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
Each phase builds on the previous one, so don’t rush.
Take your time, build projects, and keep moving forward.
Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.
✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).
Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.
✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.
Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.
✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.
Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.
✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.
Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.
✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()
– Final Checkpoint:
Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub
Don’t just complete tutorialsm create things.
Explain your work.
Build your GitHub.
Write a blog.
That’s how you go from “learning” to “landing a job
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
👍3❤2
What is the difference between data scientist, data engineer, data analyst and business intelligence?
🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month
🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse
📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region
📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department
🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers
🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.
🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month
🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse
📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region
📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department
🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers
🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.
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