Certification in Data Science using Python

Master data manipulation and cleaning with Python by reshaping, transforming, and handling missing values, duplicates, and outliers using pandas, NumPy, and scikit-learn to prepare analysis-ready datasets.

Master data import and export in Python using pandas to read and write CSV, Excel, JSON, SQL and NoSQL databases, ensuring data integrity, encoding, and reproducibility for data driven workflows.

Master data cleaning and preprocessing with pandas, NumPy, seaborn, matplotlib, and scikit-learn, covering missing value imputation, duplicates removal, outlier handling, normalization, standardization, and encoding for analysis-ready data.

Apply data manipulation and cleaning with Python to address missing data, duplicates, outliers, and inconsistencies, preparing high-quality datasets for machine learning and analytics using pandas, NumPy, and scikit-learn.

Explore exploratory data analysis (EDA) with Python, using pandas, NumPy, matplotlib, and seaborn to visualize data, uncover patterns, detect missing values and outliers, and perform feature engineering.

Learn to visualize data science with matplotlib and seaborn using bar, line, scatter plots, plus heat maps and pair plots for deeper insights.

Load a csv into a pandas dataframe, inspect shape and data types, and apply descriptive statistics to summarize central tendency, variability, and frequency distribution.

Explore data exploration with box plot for outliers, value counts for categorical features, and correlation and multivariate analyses, plus feature engineering and one-hot encoding in Python using IQR.

Explore exploratory data analysis to reveal distributions, relationships, and anomalies using pandas, NumPy, Matplotlib, and Seaborn. EDA provides descriptive statistics, outlier detection, and feature engineering for machine learning.

Explore statistical analysis with Python using NumPy, Pandas, SciPy, statsmodels, and Seaborn to perform descriptive and inferential statistics, visualize with histograms and scatter plots, and enable end-to-end data insights.

Learn hypothesis testing to evaluate population assumptions with sample data using t tests and ANOVA, plus non-parametric options like mann-whitney and kruskal-wallis, guided by p values and a 0.05 threshold.

Delve into statistical modelling with Python, covering correlation and regression analysis, including linear, multiple, and logistic regression, normal and binomial distributions, and time series analysis.

Apply descriptive and inferential statistics with Python to perform hypothesis testing, correlation, and regression. Utilize pandas, NumPy, SciPy, Statsmodels, and seaborn for data preparation, time series forecasting, and visualizations.

Learn machine learning basics, a subset of AI that learns from data to detect patterns and make predictions. Explore data features, model training, testing, and supervised, unsupervised, and reinforcement learning.

Discover how machine learning, a subset of AI, learns from data to make predictions. Understand supervised, unsupervised, and reinforcement learning and the ML workflow from data collection to deployment.

Explore supervised and unsupervised learning fundamentals, including regression, classification, clustering, and key evaluation metrics, with practical Python scikit-learn examples.

Learn to build and evaluate machine learning models using train/test splits and cross-validation, tune hyperparameters with grid or random search, assess metrics, and deploy via API frameworks.

Master the foundations of machine learning, including supervised, unsupervised, and reinforcement learning, and see how regression, classification, clustering, and dimensionality reduction drive data-driven predictions, evaluation, and deployment.

Learn machine learning algorithms with Python, from supervised methods like linear and logistic regression, decision trees, and SVMs, to unsupervised clustering and PCA, plus neural networks with TensorFlow and PyTorch.

Explore supervised learning algorithms including decision trees, random forests, SVM with linear kernel, and k-NN, using iris data and sklearn to compare accuracies.

Master unsupervised learning with k-means clustering and hierarchical clustering, then apply PCA for dimensionality reduction, understanding centroids, dendrograms, and explained variance ratios.

Master machine learning algorithms with Python to build predictive models using scikit-learn, TensorFlow, and PyTorch, covering regression, classification, clustering, and deep learning with NumPy, pandas, and matplotlib.

Explore advanced data science topics, including deep learning, natural language processing, big data analytics, reinforcement learning, explainable AI, computer vision, and AutoML to boost model accuracy and interpretability.

Feature engineering and selection enhance model performance by creating features, encoding categorical variables, scaling, and applying PCA or autoencoders, with scikit-learn examples.

Explore deep learning and neural networks—feedforward, CNNs, RNNs, transformers like Bert and GPT—with Python TensorFlow and Keras, plus NLP, ARIMA time series, and graph data science concepts.

Explore reinforcement learning with MDPs, Q-learning, and deep Q-networks (DQN), and examine anomaly detection, AutoML tools like Teapot, and quantum machine learning with Qiskit.

Explore explainable AI techniques like Shap and Shapley additive explanations, with local model-agnostic interpretations, using Python on a random forest with iris data and post-hoc methods.

Explore how deep learning, natural language processing, reinforcement learning, and explainable AI shape the future of data science, enabling scalable, interpretable, and innovative data-driven solutions.

Master deep learning with Python by using neural networks to automatically learn hierarchical features for tasks like image recognition and natural language understanding, with TensorFlow, PyTorch, and Keras.

Explore deep learning fundamentals, including neural networks, activation and loss functions, optimization with gradient descent and Adam, and practical Python examples using TensorFlow, Keras, and PyTorch.

Explore building deep learning models with TensorFlow and Keras, including CNNs for image classification, RNNs and LSTMs for sequential data, GANs for synthetic data, and transfer learning with pre-trained models.

Master hyperparameter tuning in deep learning with grid search, random search, and Bayesian optimization via Keras tuner to boost accuracy and generalization; deploy models with Flask, TensorFlow serving, and Onnx.

Conclude deep learning with python by covering neural networks, CNNs, RNNs, GANs, transfer learning, and frameworks like TensorFlow and PyTorch, plus deployment and trends such as transformers and federated learning.

Master big data analytics with Python to process massive data sets, reveal patterns with pandas, NumPy, SciPy, and enable real-time analytics, predictive modeling, and actionable insights with Spark and Dask.

Explore big data technologies and the five V's—volume, velocity, variety, veracity, value. Learn python ecosystems like pandas, dask, PySpark, Hadoop streaming, Kafka, TensorFlow, and PyTorch for distributed data processing.

Explore basic PySpark transformations and actions on Spark data frames and RDDs, including selection, filtering, and aggregation, and understand the lazy execution model that optimizes large-scale data processing.

Analyze big data with spark using PySpark MLlib, including vector assembler and logistic regression for scalable predictions. Explore distributed data frames and cloud analytics on AWS.

Master big data analytics with Python by combining data manipulation, distributed processing, and cloud computing using PySpark, Kafka, and AWS to derive scalable insights.

This lecture explains applied data science projects as the practical bridge between theory and real-world impact, covering data acquisition, cleaning, EDA, modeling, validation, deployment, and continuous optimization across industries.

Learn real world data science projects, from churn prediction and sales forecasting to sentiment analysis and fraud detection, through end-to-end pipelines and deployment with Python.

Learn traffic prediction with deep learning and real-time data, healthcare predictive analytics, surveillance object detection, AI resume screening, and stock price forecasting using Python and TensorFlow.

applied data science projects enable end-to-end workflows from business requirements to deployment, emphasizing data cleaning, feature engineering, and storytelling to drive value.

Develop a capstone project in Python that predicts telecom customer churn using supervised learning, data cleaning, EDA, feature engineering, and models like logistic regression, random forest, and XGBoost.