𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺😍

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Learn Data Science in 2024

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

Join @datasciencefree for more

ENJOY LEARNING 👍👍

𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍

Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀

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𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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Here are five of the most commonly used SQL queries in data science:

1. SELECT and FROM Clauses
- Basic data retrieval: SELECT column1, column2 FROM table_name;

2. WHERE Clause
- Filtering data: SELECT * FROM table_name WHERE condition;

3. GROUP BY and Aggregate Functions
- Summarizing data: SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;

4. JOIN Operations
- Combining data from multiple tables:

     SELECT a.column1, b.column2
     FROM table1 a
     JOIN table2 b ON a.common_column = b.common_column;
     

5. Subqueries and Nested Queries
- Advanced data retrieval:

     SELECT column1
     FROM table_name
     WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);

Like for more ❤️

Hope it helps :)

𝗧𝗼𝗽 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

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Free Datasets to work on Power BI + SQL projects 👇👇

1. AdventureWorks Sample Database:
- Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15)
- Description: A sample database provided by Microsoft, containing sales, products, customers, and other related data.

2. Online Retail Dataset:
- Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail)
- Description: Transactional data from an online retail store, suitable for customer segmentation and sales analysis.

3. Supermarket Sales Dataset:
- Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales)
- Description: Sales data from a supermarket, useful for inventory management and sales performance analysis.

4. Yahoo Finance (Historical Stock Data):
- Link: [Yahoo Finance](https://finance.yahoo.com/)
- Description: Historical stock data for various companies, suitable for financial analysis and visualization.

5. Human Resources Analytics: Employee Attrition and Performance:
- Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
- Description: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis.

Bonus Open Sources Resources: https://t.iss.one/DataPortfolio/16

These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

Best 5 data analyst projects for freshers with free certification
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https://datasimplifier.com/best-data-analyst-projects-for-freshers/

𝟱 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.

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Free Datasets to work on Power BI + SQL projects 👇👇

1. AdventureWorks Sample Database:
- Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15)
- Description: A sample database provided by Microsoft, containing sales, products, customers, and other related data.

2. Online Retail Dataset:
- Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail)
- Description: Transactional data from an online retail store, suitable for customer segmentation and sales analysis.

3. Supermarket Sales Dataset:
- Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales)
- Description: Sales data from a supermarket, useful for inventory management and sales performance analysis.

4. Yahoo Finance (Historical Stock Data):
- Link: [Yahoo Finance](https://finance.yahoo.com/)
- Description: Historical stock data for various companies, suitable for financial analysis and visualization.

5. Human Resources Analytics: Employee Attrition and Performance:
- Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
- Description: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis.

Bonus Open Sources Resources: https://t.iss.one/DataPortfolio/16

These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

𝟱 𝗙𝗿𝗲𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗹𝗮𝗻𝘀 𝘁𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝗧𝗲𝗰𝗵 & 𝗔𝗜!😍

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🎓 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗢𝗽𝗲𝗻 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄 & 𝗨𝗽𝘀𝗸𝗶𝗹𝗹!😍

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🚀 Key Skills for Aspiring Tech Specialists

📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques

🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks

🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools

🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus

🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning

🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills

🔊 NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data