Python Detailed Roadmap 🚀

📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)

📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules

📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON

📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation

📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions

📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators

📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)

📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)

📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI

📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)

📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub

📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews

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This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries

🚀 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()

▎Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Denoscriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

🤖 How Artificial Intelligence Works...

When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:

➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.

➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.

➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?

➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.

➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.

➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got.

➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- Talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?

➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. What new skills did you gain, and what would you do differently next time?

➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.

By preparing your project details thoroughly and understanding what the interviewer is looking for, you can talk about your experience in a way that really showcases your skills and increases your chances of getting the job.

Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

🚀 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()