Data Science Mastery:10-in-1 Data Interview Projects showoff

Explore the concept of exploratory data analysis and apply Python tools to the Google Play Store Apps dataset, covering data collection, cleaning, exploration, visualization, and drawing insights.

Master data cleaning and preprocessing for the Google Play Store Apps dataset by handling missing values, removing duplicates and outliers, and applying normalization, standardization, and encoding for analysis.

Explore data visualization techniques for exploratory data analysis using the Google Play Store dataset, applying Matplotlib and Seaborn to create bar, line, scatter, and box plots.

Apply descriptive statistics, correlation, and covariance to explore the Google Play Store Apps dataset. Conduct hypothesis testing with t tests, chi-square tests, and ANOVA to draw data driven conclusions.

Learn to craft data storytelling with clear objectives, engaging narratives, and effective visualizations, using the Google Play Store dataset to drive insights and recommendations.

Wrap up your EDA journey by documenting, sharing, and reproducing the analysis of the Kaggle Google Play Store apps dataset, including data cleaning, visualizations, statistical analysis, and hypothesis testing.

Learn feature extraction for sentiment analysis, turning text into numerical features with bag-of-words and tf-idf approaches using scikit-learn tools like CountVectorizer and TfidfVectorizer.

Explore building sentiment analysis models with supervised learning and deep learning, using bag-of-words and tf-idf features, RNNs and LSTMs, word embeddings, and practical Python tools like scikit-learn and TensorFlow.

Evaluate sentiment analysis models using accuracy, precision, recall, F1 score, and confusion matrices; optimize with cross-validation and grid or random search, then deploy via web app, API, or cloud.

Explore the Titanic dataset with pandas, matplotlib, and seaborn to visualize survival, handle missing values, remove outliers, apply one-hot encoding for sex, and standardize age and fare for preprocessing.

Evaluate Titanic dataset model selection with logistic regression, decision trees, random forests, and support vector machines, using a 20% test split and 42 random state, assess accuracy, precision, recall, F1.

Train logistic regression, decision tree, and random forest on the Titanic dataset with train test split; tune hyperparameters via grid and random search; evaluate with accuracy, precision, recall, and F1.

Deploy trained predictive models to production, save and load them with Joblib, and generate predictions on unseen Titanic data while analyzing feature importance.

Analyze bitcoin price time series with rolling averages, seasonal plots, and autocorrelation and partial autocorrelation analyses to reveal trend, seasonality, and residuals using pandas, NumPy, matplotlib, Tableau, Seaborn, and statsmodels.

Explore time series forecasting with arima, sarima, and prophet using bitcoin data, including stationarity, model fitting, diagnostics, and out-of-sample forecasts in Python.

Explore time series forecasting with bitcoin data by evaluating models with MAE and RMSE, decomposing trends and seasonality with STL, detecting anomalies via z-scores, and building an LSTM predictor.

Explore big data analytics with Apache Spark, set up a Spark session, and load the New York City taxi trip duration dataset to understand schema and data frames.

Learn data transformation and feature engineering with Apache Spark by creating new features like pickup hour and day of week, then aggregate and visualize average trip durations to reveal patterns.

Visualize big data insights by converting a Spark data frame to pandas, plotting trip duration distribution and hourly averages to reveal patterns and optimize taxi scheduling.

Conclude the data analytics journey with Apache Spark, revealing NYC taxi trip duration insights via histograms and pickup-hour patterns, and outline next steps in predictive modeling and real-time analytics.

Explore exploratory data analysis on a Kaggle playground dataset using pandas and numpy. Load train and test data, drop problematic columns, and identify categorical and numeric features for preprocessing.

Explore data transformation and visualization by preprocessing data with a pipeline, imputing missing values, scaling numerical features, and one-hot encoding categorical variables before visualizing distributions.

Split data into training and validation sets, compare models (decision tree, random forest, SVC, logistic regression) with cross-validation, and select the best performing model.

Explore XGBoost training with hyperparameter tuning and cross-validation to find the optimal boosting rounds and prevent overfitting, using early stopping and n_estimators for a robust model.

Explore ensemble modeling with bagging and boosting, combining random forests and XGBoost through averaging probabilistic predictions and stacking, then evaluate with cross-validation using accuracy, precision, recall, and F1.

Learn to download Kaggle data directly into Google Colab by mounting Google Drive, using a Google API token, and downloading and extracting datasets with the Kaggle API.

Explore image classification with transfer learning and fine tuning, balancing a cats and dogs dataset and preparing train, validation, and optional test sets using TensorFlow and Plotly.

Learn to preprocess image data with the Keras ImageDataGenerator, rescale pixels, and create train, validation, and test generators using flow_from_directory, then visualize samples with a custom plot_data function.

Explore fraud detection on 280,000 credit card transactions, conducting exploratory data analysis to assess class imbalance, missing values, feature distributions, and guide feature engineering.

Learn advanced fraud detection techniques, blending ensembling, anomaly detection, and deep learning (LSTM, autoencoders) to improve credit card fraud classification beyond baseline models.