Learn Data Science Course with Certification

Tableau

• Connecting to data sources (Excel, CSV, databases).
• Data cleaning and preparation.
• Understanding Tableau data types and relationships.

PowerBI

• Connecting to data sources (Excel, CSV, databases).
• Data cleaning and preparation.
• Understanding Power BI data types and relationships.

Tableau

• Creating basic charts (bar, line, pie).
• Customizing charts (colors, labels, tooltips).
• Using filters and sorting data.

PowerBI

• Creating basic charts (bar, line, pie).
• Customizing charts (colors, labels, tooltips).
• Using filters and sorting data.

Tableau

• Creating advanced charts (heat maps, tree maps, bullet charts).
• Using calculated fields.
• Parameters and input controls.

PowerBI

• Creating advanced charts (heat maps, tree maps, bullet charts), using DAX (Data Analysis Expressions) for calculations, Parameters, and input controls.

Tableau

• Designing interactive dashboards.
• Adding interactivity with actions (filters, highlights).
• Creating stories for data presentation.

PowerBI

• Designing interactive dashboards.
• Adding interactivity with slicers and filters.
• Creating reports for data presentation.

Tableau

• Data blending.
• Data joins and unions.
• Handling null values and outliers.

PowerBI

• Data transformation using Power Query.
• Data joins and merges.
• Handling null values and outliers.

Tableau

• Time series analysis.
• Forecasting and trend analysis.
• Cohort analysis.

PowerBI

• Time series analysis.
• Forecasting and trend analysis.
• Cohort analysis.

Tableau

• Creating maps.
• Spatial joins and distance calculations.
• Advanced map visualizations.

PowerBI

• Creating maps.
• Spatial joins and distance calculations.
• Advanced map visualizations.

Tableau

• Exporting Tableau visualizations.
• Embedding Tableau in websites and applications.
• Connecting Tableau to R and Python for advanced analytics.

PowerBI

• Exporting Power BI visualizations.
• Embedding Power BI in websites and applications.
• Connecting Power BI to R and Python for advanced analytics.

• Best practices for effective data visualization.
• Design principles.
• Avoiding common pitfalls.
• Case studies and real-world applications.
• Reviewing industry-specific use cases.
• Hands-on case study analysis.

• Descriptive Statistics.
• Measures of central tendency: mean, median, mode.
• Measures of dispersion: range, variance, standard deviation, interquartile range.
• Data Visualization.
• Histograms, bar charts, and pie charts.
• Box plots, scatter plots.
• Probability Basics.
• Probability theory and rules.
• Conditional probability and Bayes’ theorem.
• Probability distributions: discrete and continuous.

• Discrete Distributions.
• Bernoulli.
• Binomial.
• Poisson distributions.
• Continuous Distributions.
• Uniform and Normal (Gaussian).
• Exponential distributions.
• Central Limit Theorem.
• Multivariate Distributions.
• Multinomial distribution.
• Multivariate Normal distribution.

• Sampling and Sampling Distributions.
• Point Estimation and Properties of Estimators.
• Bias.
• Variance.
• Mean Squared Error (MSE).
• Interval Estimation.
• Confidence intervals for means and proportions.
• Hypothesis Testing.
• Null and alternative hypotheses.
• Type I and Type II errors.
• p-values and significance levels.
• t-tests.
• chi-square tests.
• ANOVA.

• Bayesian vs Frequentist methods.
• Prior, likelihood, and posterior distributions.
• Bayesian inference and applications.

• Overview and history of machine learning.
• Types of machine learning: supervised, unsupervised, reinforcement learning.
• Key terminology.
• Features.
• Labels.
• Training set.
• Test set.
• Validation set.

• Data cleaning.
• Handling missing values.
• Outliers.
• Feature scaling.
• Normalization.
• Standardization.
• Encoding categorical variables.
• One-hot encoding.
• Label encoding.
• Feature engineering and selection techniques.

• Linear Models.
• Linear regression.
• Logistic regression.
• Decision Trees.
• Construction and interpretation of decision trees.
• Pruning techniques to avoid overfitting.
• Ensemble Methods.
• Bagging (Bootstrap Aggregating).
• Random Forests.
• Boosting (AdaBoost, Gradient Boosting).
• Support Vector Machines (SVM).
• Concept of hyperplane and support vectors.
• Kernel tricks: linear, polynomial, radial basis function (RBF), K-Nearest Neighbors (KNN).
• Distance metrics: Euclidean, Manhattan.
• Choosing the value of K.
• Gradient Boosting Machines (GBMs).
• XGBoost.
• LightGBM.
• CatBoost.
• Advanced SVM techniques.
• Advanced kernel techniques.
• Support Vector Regression (SVR).

• Clustering.
• K-means clustering.
• Hierarchical clustering: agglomerative and divisive.
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
• Dimensionality Reduction.
• Principal Component Analysis (PCA).
• t-Distributed Stochastic Neighbor Embedding (t-SNE).
• Linear Discriminant Analysis (LDA).
• Association Rule Learning.
• Apriori algorithm.

• Train-test split.
• Cross-validation.
• k-fold.
• Stratified k-fold.
• Evaluation metrics for regression.
• Mean Squared Error (MSE).
• Root Mean Squared Error (RMSE).
• Mean Absolute Error (MAE).
• R².
• Evaluation metrics for classification.
• Accuracy.
• Precision.
• Recall.
• F1 score.
• ROC-AUC.

• Perceptron and multi-layer perceptron.
• Activation functions.
• Sigmoid.
• Tanh.
• ReLU.
• Backpropagation and gradient descent.

• Definition and examples of time series data.
• Components of time series.
• Trend.
• Seasonality.
• Cyclic patterns.
• Irregular components.
• Time series vs. cross-sectional data.

• Plotting time series data.
• Moving averages and smoothing techniques.
• Seasonal decomposition of time series.
• STL (Seasonal and Trend decomposition using Loess).

• Definition of stationarity.
• Augmented Dickey-Fuller (ADF) test for stationarity.
• Differencing to achieve stationarity.
• Seasonality adjustment.

• Autocorrelation Function (ACF).
• Partial Autocorrelation Function (PACF).
• Identifying patterns using ACF and PACF plots.

• Autoregressive (AR) models.
• AR(p) process and Yule-Walker equations.
• Moving Average (MA) models.
• MA(q) process and invertibility.
• Autoregressive Moving Average (ARMA) models.
• ARMA(p,q) process and parameter estimation.
• Autoregressive Integrated Moving Average (ARIMA) models.
• ARIMA(p,d,q) process and model selection.
• Seasonal ARIMA (SARIMA) models.

• Splitting time series data.
• Training and testing sets.
• Walk-forward validation.
• Forecasting accuracy metrics.
• Mean Absolute Error (MAE).
• Mean Squared Error (MSE).
• Root Mean Squared Error (RMSE).
• Mean Absolute Percentage Error (MAPE).

• Convolutional Neural Networks (CNNs).
• Convolution and pooling layers.
• Famous architectures: LeNet, AlexNet, VGG, ResNet.
• Recurrent Neural Networks (RNNs).
• Vanishing gradient problem.
• Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

• Text Preprocessing.
• Tokenization.
• Stemming.
• Lemmatization.
• Stopwords removal.
• Word Embeddings.
• Word2Vec.
• GloVe.
• Sequence Models.
• RNNs.
• LSTMs.
• GRUs.
• Transformers.
• Attention mechanism.
• BERT.
• GPT models.

• Model serving and APIs
• Containerization with Docker
• Model monitoring and management
• Scaling machine learning models in production

• Overview of deep learning and neural networks.
• Key concepts.
• Neurons.
• Layers.
• Activation functions.
• Loss functions.
• Introduction to deep learning frameworks.
• TensorFlow.
• PyTorch.

• Gradient descent and variants.
• SGD.
• Adam.
• RMSprop.
• Learning rate schedules and techniques to avoid overfitting.
• Dropout.
• Regularization.
• Batch normalization and its impact on training.

• Convolutional Neural Networks (CNNs).
• Review of CNNs.
• Advanced CNN architectures (ResNet, DenseNet, EfficientNet).
• Techniques to improve CNN performance.
• Data augmentation.
• Transfer learning.
• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
• Overview of RNNs and LSTMs.
• Advanced architectures: GRU, Bidirectional RNNs, and Attention mechanisms.
• Applications of RNNs in time series and sequence modeling.

• Introduction to Attention Mechanisms.
• The need for attention in deep learning.
• Types of attention mechanisms.
• self-attention.
• cross-attention.
• Applications of attention in various domains.
• Transformers: Theory and Architecture.
• The transformer architecture.
• encoder.
• decoder.
• multi-head attention.
• Positional encoding and its importance.
• Understanding the transformer model through the “Attention is All You Need” paper.