Data Science Master Program

3.5 to 4 months

Syllabus

Introduction to Data Science & Machine Learning
- Core Skills | Industries & Domain | Need
- Data Science Project (DSP) Life Cycle
- Descriptive | Diagnostic | Predictive | Prescriptive Analytics
Python Programming
- Installing Anaconda & Python v3 | Git Bash
- Data Types & Structures | List | Dictionary | Tuple | Array
- Functions & Classes | Control & Loop Statements
Python Analytics Packages
- Installing & Understanding Packages
- PANDAS
  - Pandas Data Frame Operations
  - Data Acquisition (Import & Export)
  - Indexing | Selection | Sorting | Filtering | Group By
  - Concatenation | Merge | Append | Drop
- NUMPY - Array Operations
- Data Visualization
  - Matplotlib
  - Seaborn
  - Creating Charts
    - Bar Chart
    - Histogram
    - Line Chart
    - Scatter Plots
    - Box Plots etc.
Statistics & Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Descriptive Analytics
- Measure of Central Tendency
- Dispersion
- Probability Distribution
- Inferential Statistics
- Diagnostic Analytics
- Covariance
- Data Wrangling (Munging)
- Outliers
- Missing Values
- Sparsity
- Inferential Statistics
- Correlation
- Hypothesis Testing
Time Series Forecasting
- Time Series Data
- Trend Chart
- Moving Average
- Exponential Smoothing
- ARIMA
- GARCH

Supervised Learning
- Installing & Using Scikit-Learn Package
- Linear Regression
- Multi-Linear Regression
- Stepwise Regression
- Logistic Regression
- Sigmoid Function
- Entropy
- Information Gain
- Gini Index
- Decision Tree (CART)
- Ensemble Learning
- Random Forest
- xgBoost
- K Nearest Neighbors (KNN)
- K Selection
- Distance Metrics
- Support Vector Machine (SVM)
- Kernel Functions
- Naive Bayes Classifier (NBC)
- Perceptron Learning
- Multi Layer Perceptron (MLP)
Unsupervised Learning
- Hierachical Clustering
- Dendograms
- K Means Clustering
- Association Rule Mining
- Recommendation Engine
Intro to AI & Neural Networks
- Deep Learning vs Machine Learning
- Tech Advancement
- All about Artificial Neural Networks (ANN)
- Understand How Deep Neural Network Works?
- Different variants of Gradient Descent
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
- Batch Size
- Learning Rate
- Momentum
Deep Learning in Python
- Deep Learning packages in python
- Google TensorFlow Framework
- Model Building with default TFLearn API
- Keras Vs TFLearn Vs Pycharm APIs
- Model Building with Keras API Wrapper
- Activations
- Optimizers
- Losses
- Validation
- Evaluation Metrics
- Keras Backend
- Callbacks - Early Stopping, TensorBoard
CNN - Convolutional Neural Networks
- Understanding Architecture & Visualizing a CNN
- Kernel
- Depth
- Pooling
RNN - Recurrent Neural Networks
- Recurrent Neural Network Model
- Training RNNs with Back-propagation
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)

NLP - Natural Language Processing
- Understanding Text Mining & Analyti
- Tokenization
- Stop Word Removal
- Stemming
- Lexical Analysis
Model Improvement & Validation
- Regularization
- Lasso
- Ridge
- ElasticNet
- Cross Validation
- ROC Curve
- Confusion Matrix
- Overfitting
- Underfitting Problems
- Precision vs Recall
- F1 Score
- Type I vs Type II Error
- Ensemble Modeling (Bagging | Boosting | Stacking)
- Feature Selection
- Feature Extraction
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Multi class Classification
- Linear Discriminant Analysis (LDA)
- Scree Plot
- Elbow Method
Other Analytics Tool Overviews
- Business Intelligence | Tableau | PowerBI | Advanced Excel
- Database Systems | SQL Server | MySQL | Oracle
- Cloud Services | Google Collaborative | Microsoft Azure ML | Amazon Web Services (AWS)
Projects & Case Studies
- 1) Real Estate Price Prediction
- 2) Fraud Detection Problem in BFSI domain
- 3) Disease Detection in Healthcare domain
- 4) Market Segmentation in Advertising Sector
- 5) Market Basket Analysis in Departmental Sector
- 6) Passenger Forecasting in Aviation Sector
- 7) News Classification in Media Sector
- 8) Image Recognition using MNIST Dataset
- 9) Object Recognition using CIFAR-10 Dataset
- 10) Speech Recognition (Google Voice | Alexa | Siri)
- 11) Sentiment Analysis and Word Clouds
- 12) Chat Bot Building
- 13) Dashboard Visualization