Full Stack Data Science & Machine Learning BootCamp Course

Learn Python data structures and string manipulation—lists, tuples, sets, dictionaries, indexing, slicing, and key operations—plus tokenization, bag-of-words, and churn prediction contexts.

Master Python function concepts, including lambda expressions, map, filter, and reduce, for compact data processing. Explore recursion, function definitions, arguments, scope, and evaluation metrics like confusion matrix, precision, and recall.

Learn Python data analysis with libraries, perform exploratory data analysis and descriptive statistics on open source datasets, and build logistic regression model with evaluation metrics like AUC, precision, and recall.

Explore descriptive statistics, probability, and the normal distribution, and learn data types, central tendencies, and dispersion with mean, median, mode, range, variance, standard deviation, skewness, and kurtosis.

Explore the central limit theorem, sampling from population data to form sample means, and how Gaussian distributions, mean, median, and variance illuminate data analysis.

Explore distributions and correlations with Python, covering Gaussian and uniform distributions, z-scores, p-values, hypothesis testing, and data preprocessing for analytics.

Explore pdf and cdf, histograms and distributions, box plots, outliers, and apply hypothesis testing with p-values and z-scores to compare groups.

Master time series forecasting fundamentals, including trend, seasonality, and stationarity, then build forecasts for sales and prices using ARIMA, moving averages, autocorrelation, differencing, and Prophet.

Explore probability theory and statistics for time series analysis, covering central limit theorem, central tendencies, distributions, hypothesis testing, p-values, A/B testing, and forecasting with ARIMA and Prophet.

Explore a UK road accident time series analysis from 2005 to 2014 using Kaggle and UK government data; identify trends, seasonality, regional effects, and future risk.

Explore time series forecasting for uk road accidents using arima, lstm, prophet, and neural networks; preprocess data, assess stationarity, and compare models to forecast two years of casualties.

Learn how logistic regression, though named regression, uses nonlinear transformation and a sigmoid to classify data, with geometric, probabilistic, and loss interpretations plus regularization and cross-validation.

Explore tokenization and vectorization for text data, including bag-of-words, tf-idf, and word2vec, and apply them to NLP tasks on covid-19 datasets from Kaggle.

Learn to clean and preprocess text data from Amazon reviews, applying tokenization, stop-word removal, stemming, and normalization, then build a logistic regression model using bag-of-words and tf-idf for binary sentiment.

Explore linear regression theory and practical implementation, including tf-idf and bag-of-words features, regularization, and parameter tuning for sentiment reviews.

Explore the theory of decision tree classifier and regression, including root and leaf nodes, entropy, information gain, Gini impurity, and practical tips to combat overfitting.

Pair ensemble learning with a Flask-based loan prediction app, exploring random forest, bagging, and bias-variance in model design.

Learn to perform sentiment analysis on a large movie review dataset using NLP—data cleaning, tokenization, bag-of-words and TF-IDF modeling—and explore EDA with univariate, multivariate, and correlation analyses.

Master unsupervised learning with the k means algorithm, including k means plus plus initialization and elbow method, and apply time series forecasting to real-world data such as stock prices.

Predict flight fares with machine learning using Kaggle dataset, performing data cleaning, preprocessing, feature engineering, and modeling on train and test sets from journey date, route, stops, and airline data.

Apply feature engineering on flight data by extracting hours and minutes, converting durations, and encoding stops and airlines, then train classical ML models with cross-validation.

Deploy the model with a Flask framework to predict flight prices using a simple HTML UI and GET/POST inputs for departure, arrival, stops, and airline.

Explore mushroom classification through exploratory data analysis, data cleaning, and preprocessing using the UCI mushroom dataset to distinguish edible from poisonous mushrooms with feature engineering like cap shape and color.

Explore data preprocessing and cleaning for mushroom classification. Drop non-informative features, impute missing values, encode categoricals with one-hot encoding, and evaluate models from logistic regression to XGBoost.

Classify nursery school admission decisions using a multiclass model with features such as parent, housing, finance, and health. Perform EDA, preprocessing, one-hot encoding, and benchmark modeling to assess performance.

Explore baseline and benchmark models such as logistic regression, decision tree, and svm, using ten-fold cross-validation and hyperparameter tuning after one-hot encoding to evaluate accuracy and predict nursery school recommendations.

Explore multi-label toxic comment classification using the Kaggle Jigsaw Wikipedia comments dataset, covering exploratory data analysis, data cleaning, preprocessing, and model building to detect toxic, insult, and identity hate.

Explore tokenization mechanisms with regular expressions and the cross library, converting text to numerical data, normalizing to lowercase, and handling stop words, plus word clouds for toxic comment analysis.

Refine and optimize nb, svm, and lr with feature weights to classify toxic comments from the Kaggle Jigsaw Wikipedia dataset, covering insults and identity hate through training and testing data.

Explore uk road accident time series forecasting through eda on 2005–2014 data, using kaggle datasets to analyze trends, regions, seasons, and casualties for safety insights.

Explore forecasting UK road accident rates by casualties using SARIMA, Prophet, and LSTM variants, detailing data prep, decomposition, model selection, and two-year predictions.

Master the fundamentals of SQL syntax and relational databases, and learn to retrieve data via queries. Explore integrity constraints, normalization, and how to install or access MySQL for hands-on practice.

Demonstrate how data definition, manipulation, and control languages shape database structure, constraints, and access; showcase keys, normalization, and slowly changing dimensions type two, with practical SQL table operations.

Explore data definition language and data manipulation language concepts, including creating and altering tables, constraints, referential integrity, and slowly changing dimensions with practical sql examples.

Explore data manipulation language (DML) to manage data retrieval and modification in databases, comparing declarative and procedural approaches, and using select, insert, update, and delete commands.

Explore data control language (DCL) and transaction control commands, including commit, rollback, and save point, and learn domain constraints and integrity constraints such as not null, unique, and check.

Explore primary keys and foreign keys, including composite keys, constraints, and table alterations, and practice set operations such as union, union all, and intersect to filter data in SQL.

Master SQL conditional expressions and where filters, using operators like equals, greater than, in, between, not in, exists, and null, plus having with group by.

Learn how to group data with group by and order by, apply aggregation functions, use having and grouping sets, limit/offset, and operators like like and is null in SQL.

Learn how to join multiple tables using inner, left, right, full, and cross joins, using primary keys and aliases to combine data and handle nulls.

Master sql rank, dense_rank, and row_number using over and partition by to rank data, handle ties, and leverage views to store queries.

Explore sql triggers, including before and after events, and row-level or statement-level execution, with salary-change logging examples. Learn stored procedures, subqueries, and indexing basics for data workflows.

Analyze IMDb movie reviews data with SQLite in a SQL capstone, writing and optimizing complex queries, joins, nested selects, and data analytics on movie details, ratings, and cast.