Data Science

📖 The Data Analyst Course: Complete Data Analyst Bootcamp 🌟 4.5 - 20848 votes 💰 Original Price: $87.99

📖 Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization

🔊 Taught By: 365 Careers 🔗 Download Full Course 📤 Download All Courses

💡 How to choose the right graph for data visualization

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📂 Full description In this course, Scott Simpson explores the intricacies of using SQL to manipulate and alter the schema of existing databases. Learn how to add, modify, and remove columns efficiently, expand field lengths, and update data types by completing practical code challenges. Discover how to manage and structure data for a text-based chat application. Gain hands-on experience with SQL commands such as ALTER TABLE, CREATE TABLE, and UPDATE statements, as well as techniques to ensure data integrity and correct functionality. Use the interactive format of the course to test your solutions and immediately see the results in a practical learning experience. This course equips you with the necessary skills to maintain and optimize existing databases effectively.

🔅 Advanced SQL Practice: Schema Changes 🌐 Author: Scott Simpson 🔰 Level: Advanced ⏰ Duration: 9m

🌀 Learn how to manage data for a text-based chat application by practicing schema modifications and data manipulation through interactive code challenges.

📗 Topics: Data Manipulation, SQL 📤 Join Data Analysis and Databases for more courses

Key Concepts for Data Science Interviews 1. Data Cleaning and Preprocessing: Master techniques for cleaning, transforming, and preparing data for analysis, including handling missing data, outlier detection, data normalization, and feature engineering. 2. Statistics and Probability: Have a solid understanding of descriptive and inferential statistics, including distributions, hypothesis testing, p-values, confidence intervals, and Bayesian probability. 3. Linear Algebra and Calculus: Understand the mathematical foundations of data science, including matrix operations, eigenvalues, derivatives, and gradients, which are essential for algorithms like PCA and gradient descent. 4. Machine Learning Algorithms: Know the fundamentals of machine learning, including supervised and unsupervised learning. Be familiar with key algorithms like linear regression, logistic regression, decision trees, random forests, SVMs, and k-means clustering. 5. Model Evaluation and Validation: Learn how to evaluate model performance using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. Understand techniques like cross-validation and overfitting prevention. 6. Feature Engineering: Develop the ability to create meaningful features from raw data that improve model performance. This includes encoding categorical variables, scaling features, and creating interaction terms. 7. Deep Learning: Understand the basics of neural networks and deep learning. Familiarize yourself with architectures like CNNs, RNNs, and frameworks like TensorFlow and PyTorch. 8. Natural Language Processing (NLP): Learn key NLP techniques such as tokenization, stemming, lemmatization, and sentiment analysis. Understand the use of models like BERT, Word2Vec, and LSTM for text data. 9. Big Data Technologies: Gain knowledge of big data frameworks and tools like Hadoop, Spark, and NoSQL databases that are used to process large datasets efficiently. 10. Data Visualization and Storytelling: Develop the ability to create compelling visualizations using tools like Matplotlib, Seaborn, or Tableau. Practice conveying your data findings clearly to both technical and non-technical audiences through visual storytelling. 11. Python and R: Be proficient in Python and R for data manipulation, analysis, and model building. Familiarity with libraries like Pandas, NumPy, Scikit-learn, and tidyverse is essential. 12. Domain Knowledge: Develop a deep understanding of the specific industry or domain you're working in, as this context helps you make more informed decisions during the data analysis and modeling process.

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Probability for Data Science

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SQL Cheatsheet ✅