Introduction
Master the art of Data Science and Machine Learning with our comprehensive course. From Python programming to advanced Machine Learning techniques, gain the skills to analyze data, build predictive models, and make data-driven decisions. Explore real-world applications with hands-on projects and expert guidance.
Course Features
Free Books & Materials
Authorized Certification
Extra Hour for Practice
Course Tracking System
Flexible Batch Timings
Student Development
A.C. Lab
After Course Support
Campus Interviews
Job Assistance
Course Details
Python Programming
- Python basics Syntax
- Conditional statements | Loops
- User-defined functions & modules
- Data Handling with NumPy
- Data Handling with Pandas
- Data Visualization with Matplotlib
Database programming with SQL
- Introduction to MySQL
- Data Types and Constraints
- Basic SQL Operations
- Advanced SQL and Data Manipulation
- Assessment : Querying a sample dataset
- Functions in MySQL
- Case Operator in MySQL
- Group By in MySQL
- Having Clause in MySQL
- Joins in MySQL
- Subqueries in MySQL
- Union, Intersect, Except in MySQL
- Window Functions in MySQL
Statistics for Data Science
- Datatypes and Measures
- Random Variables
- Introduction to Probability
- Sampling Techniques
- Measures of Central Tendency
- Measures of Dispersion
- Skewness and Kurtosis
- Data Visualization
- Probability Distributions
- Data Preprocessing Techniques
Research Methodology
Linear Algebra
Machine Learning
Unsupervised ML
Research Methodology
- Formulating Hypothesis Statements (Null and Alternate)
- Type I and Type II Errors
- P-Value and Level of Significance
- Parametric Tests Overview
- One Sample and Two Samples T Tests
- One Sample Z Test and Paired T Test
- One-Way ANOVA
- Chi-Squared Test
- Non-Parametric Tests Overview
- One Sample Sign Test, Mann-Whitney Test, and Kruskal-Wallis Test
Linear Algebra
- Motivation – Why Learn Linear Algebra?
- Representation of Problems in Linear Algebra
- Visualizing Problems: Lines, Systems of Linear Equations, and Planes
- Matrix Basics: Terms and Operations
- Representing Problems in Matrix Form
- Solving Linear Problems: Row Echelon Form and Inverse of a Matrix
- Eigenvalues and Eigenvectors
- Finding Eigenvectors
- Use of Eigenvectors in Data Study: PCA Algorithm
- Singular Value Decomposition (SVD) of a Matrix
Machine Learning
Supervised ML – Part I (Regression Analysis)
- Correlation Analysis and Correlation Coefficient
- Introduction to Regression and Principles of Regression
- Simple Linear Regression Analysis
- Dataset Splitting: Train, Validation, and Test
- Overfitting vs. Underfitting and Generalization Error
- Multiple Linear Regression Model
- Model Adequacy Checking and Diagnostic Techniques
- Polynomial and Poisson Regression Models
- Multicollinearity, Heteroskedasticity, and Autocorrelation
- Variable Selection and Model Building
Supervised ML – Part II (Classification Analysis)
- Two-Class Classification: Logistic Regression, Neural Networks
- Decision Trees, Random Forest, Naïve-Bayes, SVM
- Multiclass Classification Techniques
- Anomaly Detection
- Case Studies I, II, III, IV & V
Unsupervised ML
- Partitioning Clustering and Hierarchical Clustering
- Clustering Validation and Evaluation with K-Means
- DBSCAN: Density-Based Clustering
- Dimensionality Reduction with PCA
- Association Rule Learning: Apriori Algorithm and Frequent Pattern Growth
- Case Studies I, II & III
Model Performance Assessment in ML
Data Visualization Tools and Techniques
Natural Language Processing
Time Series Data Study and Forecasting
Model Performance Assessment in ML
- Model Performance Metrics Overview
- Confusion Matrix, Precision, Recall, and F1-Measure
- Accuracy and Error Measures: MSE and RMSE
- Hyperparameter Optimization and Tuning
- Grid Search and Randomized Search
- Cross-Fold Validation Techniques: Leave One Out, K-Fold, Stratified K-Fold
- Ensemble Methods: Bagging and Random Forests
- Gradient Boosting and XGBoost
- AdaBoost and Voting Classifier
Data Visualization Tools and Techniques
- Data Visualization with Matplotlib and Seaborn
- Relational Plots: Scatterplot, Lineplot
- Categorical Plots: Boxplot, Violinplot, Barplot
- Distribution Plots: Jointplot, Pairplot, Distplot
- Regression Plots: Lmplot, Regplot
- Matrix Plots: Heatmap, Clustermap
- Introduction to Tableau: Basic Charts and Interactivity
- Advanced Charts and Analytical Techniques in Tableau
- Tableau Integration with Other Tools
Natural Language Processing
- Introduction to NLP and Its Applications
- NLTK Exploration: Tokenization, Stemming, Lemmatization
- Bag of Words and TF-IDF Vectorizer
- Co-occurrence Matrix and Text Similarity
- Latent Semantic Analysis (LSA) and Topic Modeling
- Latent Dirichlet Allocation (LDA)
- Text Classification: Sentiment Analysis
- Recommender Systems: Collaborative Filtering
Time Series Data Study and Forecasting
- Introduction to Time Series Data
- Correlation and Autocorrelation
- Components of Time Series and Visualization Principles
- Auto-Correlation Function (ACF) and Correlogram
- Naive Forecast Methods and Error Metrics
- Model-Based Approaches: Linear, Exponential, Quadratic Models
- Auto Regression (AR) and Moving Average (MA)
- ARMA and ARIMA Models
- Additive and Multiplicative Seasonality
- Smoothing Techniques: Moving Average and Exponential Smoothing
Exit Profile
Upon completing this course, participants will be equipped with in-demand skills in Data Science, Machine Learning, and Data Analysis, opening doors to various career opportunities.
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- NLP Specialist
- Time Series Analyst
- Database Administrator
- Python Developer
