Data Science
Unlock the power of data with our comprehensive data science program, empowering your website with cutting-edge analytics and insights
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2-3 Months
Program Duration
Certifications
2
Industrial Projects
4-6
Internship Partners
100+
Quiz/ Assignments
Lifetime
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Program Curriculum
Explore a career in web development, a dynamic field that combines creativity and essential skills for a promising professional journey.
Week 1: Introduction to Data Science, Python Basics, and Libraries
Introduction to Data Science
- What is Data Science?
- Applications of Data Science
- Data Science Workflow
- Tools and Technologies in Data Science
Python Basics for Data Science
- Python Basics:
- Data Types (int, float, string, list, tuple, dictionary, set)
- Variables and Operators
- Conditional Statements (if, else, elif)
- Loops (for, while)
- Functions and Modules
Python Libraries for Data Science
- Introduction to Numpy:
- Creating Arrays, Array Manipulation, Array Operations
- Introduction to Pandas:
- DataFrames, Series, Importing Data, Basic Data Operations
Week 2: Data Visualization and Data Handling
Data Visualization
- Introduction to Data Visualization
- Using Matplotlib:
- Basic Plots (Line, Bar, Scatter, Histogram)
- Customizing Plots (Titles, Labels, Legends, Colors)
- Using Seaborn:
- Advanced Plots (Heatmap, Pairplot, Boxplot)
- Styling and Customizing Seaborn Plots
Data Importing and Cleaning
- Importing Datasets (CSV, Excel, JSON, SQL)
- Handling Missing Values
- Data Cleaning Techniques
- Data Manipulation with Pandas (Sorting, Filtering, Aggregation)
Exploratory Data Analysis (EDA)
- Understanding Data Distribution
- Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
- Detecting Outliers
- Correlation and Covariance
Week 3: Probability and Hypothesis Testing
Introduction to Probability
- Basic Probability Concepts
- Mutually Exclusive and Independent Events
- Conditional Probability
- Bayes’ Theorem with Real-World Examples
Probability Distributions
- Discrete Probability Distributions:
- Binomial Distribution
- Poisson Distribution
- Continuous Probability Distributions:
- Normal Distribution (Bell Curve)
Hypothesis Testing and Estimation
- Null and Alternative Hypotheses
- Type I and Type II Errors
- Significance Level (Alpha)
- P-Value Interpretation
- Estimation Methods (Point and Interval Estimation)
Week 4: Machine Learning Basics
Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- ML Workflow: Data Collection, Preparation, Modeling, Evaluation
Linear Regression
- Understanding Linear Regression
- Simple Linear Regression: Equation and Interpretation
- Multiple Linear Regression
- Model Evaluation Metrics (R-Squared, RMSE)
Logistic Regression
- Difference between Linear and Logistic Regression
- Sigmoid Function and Logistic Regression Equation
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
K-Nearest Neighbors (KNN) and Naive Bayes
- K-Nearest Neighbors (KNN):
- Distance Calculation (Euclidean, Manhattan)
- Choosing Optimal K Value
- Naive Bayes Algorithm:
- Concept and Working Principle
- Types (Gaussian, Multinomial, Bernoulli)
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Earn credentials from IBM — give the exam, get certified, and stand out.
Master the language of the web with IBM’s globally recognized certification
What You’ll Get:
- IBM digital certificate recognized globally
- Increased trust and credibility with employers
- Better job opportunities and professional edge
- Stronger profile in web development roles
- Proof of your commitment to upskilling
Price : ₹3000
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