Data Science
At Divine Tech Skills, our Data Science course is designed to equip professionals and aspiring data enthusiasts with the most in-demand skills in today’s data-driven world. This comprehensive program blends theory with practical learning, enabling learners to extract valuable insights from complex datasets and make data-driven decisions confidently.
The course covers key areas such as Python for Data Science, data visualization, statistics, machine learning, data wrangling, and model deployment, ensuring a complete skill set. Whether you’re a beginner or looking to upskill, our hands-on projects and real-world case studies provide practical experience that aligns with industry needs.
Delivered by industry experts, this course also emphasizes corporate application of data science—helping organizations make strategic decisions backed by data. With personalized mentorship, project-based learning, and post-course career guidance, learners will be ready to take on roles like Data Analyst, Data Scientist, Machine Learning Engineer, or Business Analyst.
Join Divine Tech Skills to unlock the power of data and accelerate your career in the evolving world of analytics and artificial intelligence.
Module 1: Introduction to Data Science
- What is Data Science?
- Data Science workflow
- Applications in various industries
- Role of a Data Scientist
Module 2: Python for Data Science
- Installing Python & Jupyter Notebooks (Anaconda, VS Code)
- Python Basics:
1. Variables, Data types
2.Control structures (if, for, while)
3. Functions & lambda
4. List, Tuple, Dictionary, Set - File handling (CSV, TXT, JSON)
- Exception handling
Module 3: Data Analysis with NumPy
- Introduction to NumPy
- Arrays and Vectorized computation
- Array indexing and slicing
- Mathematical and statistical operations
- Broadcasting
Module 4: Data Manipulation with Pandas
- Series and DataFrames
- Reading/writing data (CSV, Excel, SQL)
- Data cleaning & wrangling:
1. Handling missing values
2. Filtering, sorting, groupby
3. Merging, joining, concatenation - Date/time handling
Module 5: Data Visualization
- Matplotlib:
1. Line, bar, scatter plots
2. Customizing plots - Seaborn:
1. Histograms, boxplots, violin plots
2. Correlation heatmaps
3. Pairplots and categorical plots - Intro to Plotly (for interactive visuals)
Module 6: Statistics for Data Science
- Descriptive statistics
- Probability concepts
- Distributions (normal, binomial, etc.)
- Inferential statistics:
1. Hypothesis testing
2. t-tests, chi-square - Confidence intervals
Module 7: Exploratory Data Analysis (EDA)
- Understanding the dataset
- Univariate & multivariate analysis
- Outlier detection
- Feature relationships
- Visual storytelling with data
Module 8: Machine Learning with Scikit-learn
- ML Workflow (Problem → Data → Model → Evaluate)
- Supervised Learning:
1. Linear/Logistic Regression
2. Decision Trees & Random Forest
3. K-Nearest Neighbors
4. SVM - Unsupervised Learning:
1. K-Means Clustering
2. Hierarchical Clustering
3. PCA (Dimensionality Reduction) - Model Evaluation:
1. Confusion matrix
2. Accuracy, precision, recall, F1 score
3. Cross-validation
Module 9: Feature Engineering & Model Tuning
- Handling categorical data (Label Encoding, One-Hot Encoding)
- Scaling and normalization
- Feature selection methods
- Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)
- Pipeline creation
Module 10: Working with Real-world Datasets
- Kaggle Datasets / UCI Datasets
- End-to-end project walkthrough
- Cleaning, EDA, Modeling, Deployment (basic)
Module 11: Introduction to Deep Learning (Optional)
- What is Deep Learning?
- Intro to TensorFlow / Keras
- Simple Neural Networks
- Image/Text classification basics
Module 12: Data Science Project & Portfolio Building
- Choosing a domain
- Project planning and execution
- GitHub portfolio setup
- Resume building for Data Science roles
- Preparing for interviews
Tools & Libraries Used
- Python
- Jupyter Notebook
- NumPy, Pandas
- Matplotlib, Seaborn, Plotly
- Scikit-learn
- TensorFlow/Keras (optional)
- Git & GitHub
- Streamlit (for app deployment, optional)
Duration: 40 hours
- Complete training in Python, statistics, machine learning, and data visualization
- Real-world projects and case studies for hands-on experience
- Industry-relevant tools: Pandas, NumPy, Matplotlib, Scikit-learn, and more
- Practical exposure to data cleaning, modeling, and deployment techniques
- Expert-led sessions with mentorship from data professionals
- Designed for beginners and professionals looking to upskill
- Focus on real-time business problem solving with data
- Interactive Q&A sessions and performance assessments
- Certification upon completion
- Career guidance and job readiness support
