Data Science helps businesses make smarter decisions using AI and Machine Learning. Turn data into powerful insights and build your future as a Data Scientist.
Course Highlights
What role does a Data Science play?
AI and ML
Artificial Intelligence and Machine Learning is the new future.
No Technical Background Required
You must need not be a scientist or an engineer to learn the Data Science. Anyone can become the Data Scientist, who wants to foresee the future.
High Market Demand
Data Scientist' job is one of the most demanded and is highly paid jobs in the industry. Almost all the sectors of the industry uses it.
Key Highlights
50+ Live interactive sessions across 7 months
218 Hrs Self-paced High Quality Videos
50+ Industry relevant Projects & Quizzes
Live Classes from Industry Experts
Prestigious Certification from iHub IIT Roorkee
Career Services for Freshers & Professionals
2 Days Campus Immersion at iHub IIT Roorkee
24/7 Dedicated Support & Mentor Guidance
Quality Material & Smart Digital Notes
Concept-wise Assignments & Tests
Lifetime Validity - Learn at your Pace
Small Batches for Personalized Focus
Market Analysis
| Job Title | No. of Jobs Listed | Annual Salary | |||
|---|---|---|---|---|---|
| Naukri | Shine | Times Job | Monster Jobs | ||
| Data Analyst | 1,10,000+ | 85,000+ | 60,000+ | 35,000+ | 3 - 8 LPA |
| Data Scientist | 95,000+ | 70,000+ | 55,000+ | 30,000+ | 6 - 18 LPA |
| Machine Learning Engineer | 80,000+ | 65,000+ | 45,000+ | 25,000+ | 8 - 20 LPA |
| AI / Gen AI Engineer | 60,000+ | 45,000+ | 30,000+ | 20,000+ | 10 - 25 LPA |
| Prompt Engineer | 25,000+ | 18,000+ | 12,000+ | 8,000+ | 6 - 20 LPA |
| Business Intelligence Analyst | 50,000+ | 40,000+ | 30,000+ | 18,000+ | 4 - 12 LPA |
| Data Engineer | 70,000+ | 55,000+ | 40,000+ | 22,000+ | 6 - 18 LPA |
| AI Research Assistant | 20,000+ | 15,000+ | 10,000+ | 6,000+ | 5 - 15 LPA |
Python & Libraries
- Variables
- Data types
- Operators
- if statements
- Loops
- String
- Lists, Tuples
- Dictionaries
- Sets
- Conditional expressions
- Modules
- Functions
- File handling
- Object Oriented Concept
- Exception Handling
- GUI Progragramming in Python
- 5 Mini Projects
- Introduction to Pandas
- Series and DataFrames
- Data Exploration
- Data Manipulation
- Data Cleaning
- Grouping and Aggregation
- Data Visualization with Pandas
- Time Series Data
- Handling Categorical Data
- Reading and writing data to various file formats: CSV, Excel, JSON,SQL Database
- Introduction to NumPy
- NumPy Arrays and Operations
- Linear Algebra with NumPy
- Data Processing with NumPy
- NumPy and Statistics
- NumPy and Visualization
- NumPy and Machine Learning
Data Visualization
- SUM, AVERAGE, MEAN, MEDIAN, SUMPRODUCT, CONCATENATE
- VLOOKUP, INDEX, MATCH, IF, COUNTIF, SUMIF, Remove duplicates and conditional formatting
- Charts, filters, sort and slicers, Pivot tables and pivot charts
- Introduction to Matplotlib
- Basic Plotting with Matplotlib
- Different Types of Plots
- Advanced Plot Customization
- 3D Plotting
- Working with Images
- Animations
- Integration with Pandas and NumPy
- Introduction to Seaborn
- Seaborn Plots
- Customizing Plots
- Statistical
- Estimation and Visualization
- Data Exploration and Analysis
- Advanced Visualization Techniques
- Working with Time Series Data
- Integration with Pandas and NumPy
Math & Statistics
- Arithmetic
- Weighted average
- Cumulative sum
- Percentile
- Vectors and Matrices
- Matrix Decompositions
- Vector Space
- Linear Mappings
- Eigenvalues and Eigenvectors
- Maxima and minima
- Mean value theorem
- Product and chain rule
- Derivatives
- Gradients of Matrices
- Taylor’s series
- Probability basics
- Probability distributions – Binomial
- Poisson
- Normal
- Bayes theorem
- Descriptive vs Inferential statistics
- Continuous vs Discrete data
- Nominal vs Ordinal data
- Nominal vs Ordinal data
- Standard Deviation and Variance
- Hypothesis testing: T-test, Paired T-test, p-value, F-Test, z-test
Database
- Introduction to MySQL
- Basic MySQL syntax
- Schema Design Methodology
- Create / Select / Drop /Copy of Database
- Create Table
- Constraints in MySQL
- Alter Table
- Insert / Select / Delete / Update Records
- Rollback, Commit and Grant
Machine Learning
- Handling NA values
- Outlier treatment
- Data normalization
- One hot encoding
- Label encoding
- Feature engineering
- Train test split
- Cross validation
- Supervised, Unsupervised
- Regression, Classification, Linear models, Nonlinear models (tree-based models), Model evaluation
- GridSearchCV, RandomSearchCV
- K means, Hierarchical clustering, Dimensionality reduction (PCA)
Deep Learning
- What is a neural network?
- Forward propagation
- Back propagation
- Building multilayer perceptron
- Special neural network architectures: Convolutional neural network (CNN)
- Sequence models: RNN, LSTM
Natural Language Processing (NLP)
- Regex
- Text presentation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings
- Text classification: Naïve Bayes
- Fundamentals of Spacy & NLTP library
- One end to end project
Control Version
- Intro to VCS
- Types of VCS
- What is Git, GitHub
- Git Installation
- Configuring Git
- Connecting Git with GitHub
- Basic Git & GitHub Workflow
- Git Branching & Merging
- Git Clone
- Resolve Merge Conflict
- How to setup & upload Project on GitHub
- How to PUSH & Pull Project
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