Data Science
Rs. 40,000
Quantity
1
Data Science
A data science course typically covers a broad range of topics, including statistical analysis, machine learning, data visualization, and data manipulation. Here's an outline of what you might expect from a comprehensive data science course:
**1. Introduction to Data Science**
- Overview of data science, its applications, and the data science workflow.
- Introduction to Python programming language and its libraries for data science (e.g., NumPy, Pandas, Matplotlib).
**2. Data Manipulation and Cleaning**
- Data cleaning techniques: handling missing values, outliers, and inconsistencies.
- Data manipulation using Pandas: filtering, sorting, merging, and transforming datasets.
**3. Exploratory Data Analysis (EDA)**
- Descriptive statistics: mean, median, mode, variance, and standard deviation.
- Data visualization using Matplotlib and Seaborn: histograms, box plots, scatter plots, etc.
**4. Statistical Analysis**
- Probability distributions and hypothesis testing.
- Statistical inference techniques: t-tests, chi-square tests, ANOVA, etc.
**5. Machine Learning Fundamentals**
- Supervised learning: linear regression, logistic regression, decision trees, random forests, etc.
- Unsupervised learning: k-means clustering, hierarchical clustering, principal component analysis (PCA), etc.
**6. Model Evaluation and Validation**
- Cross-validation techniques: k-fold cross-validation, holdout validation, etc.
- Evaluation metrics for regression and classification models: mean squared error, accuracy, precision, recall, F1-score, ROC curves, etc.
**7. Feature Engineering**
- Feature selection and extraction techniques.
- Handling categorical variables: one-hot encoding, label encoding, etc.
- Text data processing: tokenization, stemming, TF-IDF vectorization, etc.
**8. Advanced Machine Learning Techniques**
- Ensemble methods: bagging, boosting, stacking.
- Neural networks and deep learning: introduction to TensorFlow or PyTorch.
**9. Big Data Processing**
- Introduction to distributed computing frameworks like Apache Spark.
- Processing and analyzing large datasets using Spark's DataFrame API.
**10. Data Science Projects**
- Working on real-world data science projects to apply learned concepts and techniques.
- Projects may involve data analysis, predictive modeling, and/or machine learning tasks.
**11. Capstone Project**
- A comprehensive data science project that integrates concepts and skills learned throughout the course.
- Developing an end-to-end data science solution from data acquisition and cleaning to model deployment and evaluation.
**12. Ethical and Legal Considerations**
- Understanding ethical issues related to data collection, privacy, and bias.
- Compliance with data protection regulations (e.g., GDPR, HIPAA).
**Additional Components:**
- Hands-on exercises, coding assignments, and quizzes to reinforce learning.
- Access to online resources, tutorials, and reference materials.
- Mentorship and support from experienced instructors and data scientists.
- Networking opportunities with peers and industry professionals.
**Certification:**
**1. Introduction to Data Science**
- Overview of data science, its applications, and the data science workflow.
- Introduction to Python programming language and its libraries for data science (e.g., NumPy, Pandas, Matplotlib).
**2. Data Manipulation and Cleaning**
- Data cleaning techniques: handling missing values, outliers, and inconsistencies.
- Data manipulation using Pandas: filtering, sorting, merging, and transforming datasets.
**3. Exploratory Data Analysis (EDA)**
- Descriptive statistics: mean, median, mode, variance, and standard deviation.
- Data visualization using Matplotlib and Seaborn: histograms, box plots, scatter plots, etc.
**4. Statistical Analysis**
- Probability distributions and hypothesis testing.
- Statistical inference techniques: t-tests, chi-square tests, ANOVA, etc.
**5. Machine Learning Fundamentals**
- Supervised learning: linear regression, logistic regression, decision trees, random forests, etc.
- Unsupervised learning: k-means clustering, hierarchical clustering, principal component analysis (PCA), etc.
**6. Model Evaluation and Validation**
- Cross-validation techniques: k-fold cross-validation, holdout validation, etc.
- Evaluation metrics for regression and classification models: mean squared error, accuracy, precision, recall, F1-score, ROC curves, etc.
**7. Feature Engineering**
- Feature selection and extraction techniques.
- Handling categorical variables: one-hot encoding, label encoding, etc.
- Text data processing: tokenization, stemming, TF-IDF vectorization, etc.
**8. Advanced Machine Learning Techniques**
- Ensemble methods: bagging, boosting, stacking.
- Neural networks and deep learning: introduction to TensorFlow or PyTorch.
**9. Big Data Processing**
- Introduction to distributed computing frameworks like Apache Spark.
- Processing and analyzing large datasets using Spark's DataFrame API.
**10. Data Science Projects**
- Working on real-world data science projects to apply learned concepts and techniques.
- Projects may involve data analysis, predictive modeling, and/or machine learning tasks.
**11. Capstone Project**
- A comprehensive data science project that integrates concepts and skills learned throughout the course.
- Developing an end-to-end data science solution from data acquisition and cleaning to model deployment and evaluation.
**12. Ethical and Legal Considerations**
- Understanding ethical issues related to data collection, privacy, and bias.
- Compliance with data protection regulations (e.g., GDPR, HIPAA).
**Additional Components:**
- Hands-on exercises, coding assignments, and quizzes to reinforce learning.
- Access to online resources, tutorials, and reference materials.
- Mentorship and support from experienced instructors and data scientists.
- Networking opportunities with peers and industry professionals.
**Certification:**
.