Datasciencemind

Learn complete Data Science from YouTube👇 Python - Programming with Mosh https://youtu.be/_uQrJ0TkZlc?si=M1eolhB1oZAnOO7d SQL-freeCodeCamp https://youtu.be/HXV3zeQKqGY?si=s0hqzT4ZtU6kcC5k Excel - freeCodeCamp https://youtu.be/Vl0H-qTclOg?si=ua7ustH-h2OqTDH_ Maths - 3 BlueBrown, Khan Academy https://youtube.com/playlist?list=PLSQl0a2vh4HDMTztb3as8KvuIhxt1ibV3&si=ZNCSV6kmrC2dBjMA Statistics StatQuest with Josh Starmer https://youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9&si=r56H0Nf7QrgCDjgL Data Analysis - Alex the Analyst https://youtube.com/playlist?list=PLUaB-1hjhk8FE_XZ87vPPSfHqb6OcM0cF&si=i-KG_aFwX3JGFBeM ML/DL - Sentdex https://youtube.com/playlist?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&si=lvgP_8EVs1FOa3UW Data Science in general - Campus, Codebasics https://youtu.be/PFPt6PQNslE?si=VQPFC9YmkiGXgu97 . .

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); Hope this content is helpful👆

_*You don't need to know everything about every data tool. Focus on what will help you get your job done with required skills:*_ _*For Excel:*_ - IFS (all variations) - XLOOKUP - IMPORTRANGE (in GSheets) - Pivot Tables - Dynamic functions like TODAY() _*For SQL:*_ - Sum - Group By - Window Functions - CTEs - Joins *_For Tableau:_* - Calculated Columns - Sets - Groups - Formatting _*For Power BI:*_ - Power Query for data transformation - DAX (Data Analysis Expressions) for creating custom calculations - Relationships between tables - Creating interactive and dynamic dashboards - Utilizing slicers and filters effectively

*Need a help with making a perfect resume for Data Analytics!*🧾 Certainly!👇 Here’s a sample resume for a Data Analyst position: Name: [Your Name] [Your Address] [City, State, ZIP Code] [Email Address] [Phone Number] [LinkedIn Profile] [GitHub Profile] (if applicable) Objective: Detail-oriented Data Analyst with [X] years of experience in data analysis, data visualization, and statistical modeling. Seeking to leverage strong analytical and technical skills to contribute to data-driven decision-making. Education: Master of Science in Data Analytics [University Name], [City, State] Graduation Date: [Month, Year] Bachelor of Science in [Related Field] [University Name], [City, State] Graduation Date: [Month, Year] *Required Technical Skills:* Programming Languages: Python, R, SQL Data Visualization: Tableau, Power BI, Matplotlib, Seaborn Databases: MySQL, PostgreSQL, MongoDB Tools: Excel, Jupyter Notebook, Git Statistical Analysis: Regression Analysis, Hypothesis Testing, ANOVA Machine Learning: Scikit-Learn, TensorFlow, Keras Professional Experience: Data Analyst [Company Name], [City, State] [Month, Year] – Present Conduct data analysis using SQL and Python to uncover insights and support decision-making. Develop and maintain interactive dashboards using Tableau to visualize key metrics and trends. Collaborate with cross-functional teams to define business problems and deliver actionable insights. Perform statistical analysis and hypothesis testing to validate business strategies and improve processes. Automate data collection and processing workflows to increase efficiency. Junior Data Analyst [Company Name], [City, State] [Month, Year] – [Month, Year] Assisted in the analysis of large datasets to identify patterns, trends, and insights. Created and maintained reports using Excel and SQL to track business performance. Supported senior analysts in developing predictive models to forecast business outcomes. Cleaned and preprocessed data to ensure accuracy and reliability of analysis. Communicated findings and recommendations to stakeholders through presentations and reports. . . . Feel free to customize this template to better fit your experience and target job position.

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Complete Roadmap to learn Data Science 1. Foundational Knowledge Mathematics and Statistics - Linear Algebra: Understand vectors, matrices, and tensor operations. - Calculus: Learn about derivatives, integrals, and optimization techniques. - Probability: Study probability distributions, Bayes' theorem, and expected values. - Statistics: Focus on descriptive statistics, hypothesis testing, regression, and statistical significance. Programming - Python: Start with basic syntax, data structures, and OOP concepts. Libraries to learn: NumPy, pandas, matplotlib, seaborn. - R: Get familiar with basic syntax and data manipulation (optional but useful). - SQL: Understand database querying, joins, aggregations, and subqueries. 2. Core Data Science Concepts Data Wrangling and Preprocessing - Cleaning and preparing data for analysis. - Handling missing data, outliers, and inconsistencies. - Feature engineering and selection. Data Visualization - Tools: Matplotlib, seaborn, Plotly. - Concepts: Types of plots, storytelling with data, interactive visualizations. Machine Learning - Supervised Learning: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors. - Unsupervised Learning: K-means clustering, hierarchical clustering, PCA. - Advanced Techniques: Ensemble methods, gradient boosting (XGBoost, LightGBM), neural networks. - Model Evaluation: Train-test split, cross-validation, confusion matrix, ROC-AUC. 3. Advanced Topics Deep Learning - Frameworks: TensorFlow, Keras, PyTorch. - Concepts: Neural networks, CNNs, RNNs, LSTMs, GANs. Natural Language Processing (NLP) - Basics: Text preprocessing, tokenization, stemming, lemmatization. - Advanced: Sentiment analysis, topic modeling, word embeddings (Word2Vec, GloVe), transformers (BERT, GPT). Big Data Technologies - Frameworks: Hadoop, Spark. - Databases: NoSQL databases (MongoDB, Cassandra). 4. Practical Experience Projects - Start with small datasets (Kaggle, UCI Machine Learning Repository). - Progress to more complex projects involving real-world data. - Work on end-to-end projects, from data collection to model deployment. Competitions and Challenges - Participate in Kaggle competitions. - Engage in hackathons and coding challenges. 5. Soft Skills and Tools Communication - Learn to present findings clearly and concisely. - Practice writing reports and creating dashboards (Tableau, Power BI). Collaboration Tools - Version Control: Git and GitHub. - Project Management: JIRA, Trello. 6. Continuous Learning and Networking Staying Updated - Follow data science blogs, podcasts, and research papers. - Join professional groups and forums (LinkedIn, Reddit, Data Science Central). Certifications and Courses - Online courses (Coursera, edX, Udacity). - Professional certificates (Google Data Analytics, IBM Data Science Professional Certificate). 7. Specialization After gaining a broad understanding, you might want to specialize in areas such as: - Data Engineering - Business Analytics - Computer Vision - AI and Machine Learning Research Resources and Courses Books - "Python for Data Analysis" by Wes McKinney. - "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. - "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Online Platforms - Coursera: Data Science Specialization by Johns Hopkins, Deep Learning Specialization by Andrew Ng. - edX: MIT’s MicroMasters in Statistics and Data Science. - Udacity: Data Scientist Nanodegree. By following this roadmap, you can build a strong foundation in data science and progressively advance to more complex and specialized areas. Remember that practical experience and continuous learning are key to success in this field.

the five key Excel topics to cover in data analytics: 1. Data Cleaning and Preparation - Removing duplicates, handling missing data, data validation, text functions for cleaning. 2. Sorting, Filtering, and Conditional Formatting - Basic and custom sorting, AutoFilter, Advanced Filter, creating rules for conditional formatting. 3. Lookup and Reference Functions - VLOOKUP, HLOOKUP, INDEX, MATCH, and XLOOKUP for advanced data retrieval. 4. PivotTables and Data Visualization - Creating and customizing PivotTables and PivotCharts, using various chart types for data visualization. 5. Data Analysis Tools and Automation - Descriptive statistics, Analysis ToolPak, Goal Seek, Solver, and using macros and VBA for automation.