Comprehensive and organized modules
Self-paced learning with hands-on coding
Data Science Certification
This course covers a wide range of topics in Data Science such as Data Wrangling, Data Analysis, Machine learning, Probability, Statistics, etc. You can enhance the capabilities of machines to imitate human-like behavior through Deep Learning, Computer Vision, and NLP modules. At the end of the last module, two capstone projects will allow you to implement your overall learning and solve real-world Data Science problems. We have broken down the mathematics behind the algorithms to simplify your learning experience.
35+ Real-world projects
2 Capstone Projects
12 Comprehensive and organized modules
Talk to learning advisor
Sample CertificationClick here to view
Why This Course?
Data Science offers various job profiles like Data Scientist, Data Engineer, Data Analyst, and Machine Learning Engineer. According to Indeed, entry-level Data Scientists earn up to $101,571 per year,while experienced Data Scientists earn up to $138,397.
Data Science is a buzzword for the 21st century, and the number of jobs in this field is increasing tremendously. According to the U.S. Bureau of Labor Statistics, there will be over 11.5 Million job opportunities in Data Science by 2026. Amazon, Facebook, Apple, and Microsoft are some of the top firms that are actively hiring Data Scientists.
Importance of Data Science
Harvard Business Review (HBR) asserts Data Scientist as 'the sexiest job of the 21st Century '. Data Science combines the significant knowledge of programming and mathematics to provide insights that drive modern business processes. Data science allows the user to submit an incredible quantity of data into algorithms, and makes predictions based exclusively on the input.
Why AI Probably?
Gamified approach for the understanding of concepts and memory retention based on Cognitive Neuroscience
Get hands-on experience on different real-world problems with an in-depth explanation
A modular LMS designed with an easy-to-navigate user interface
Taught by Industry Experts
Get on-demand video lectures and learn from renowned industry experts
Number of hours
Get more than 70 hours of explicit content in the form of lectures and code-along videos
24 x 7 Learning Support
Get learning support at any point of time during your enrolment in the course
Introduction to Python programming fundamentals, Object-oriented programming, and data analytics using NumPy, Pandas, and Matplotlib.
- Data Types & Data Structures
- Conditional Structures
Introduction to Databases, SQL queries, and reformatting the data to make it a useful input for the data exploring phase.
- PostgreSQL basics
- CRUD operations
- Aggregate Functions
- String operations
Learn the mathematical concepts that drive data science
- Hypothesis testing
Engineer the features of data and explore multiple methods to scale up or down its effect on the analysis.
- Feature Scaling
- Handling missing and null values
- Feature Transformation
- Feature Selection
- Handling imbalanced dataset
Explore, enact and comprehend various algorithms in supervised machine learning.
- Regression models
- Classification models
- Testing and Training data
- Model evaluation techniques
Focus more on the models and let the machines learn without human intervention with unsupervised and reinforcement learning.
- Clustering and Classification
- Partition clustering
- Hierarchical Clustering
- Reinforcement learning and applications
- Q learning
- Recommendation systems
Create models that work upon time series and derive better predictions with time series algorithms.
- Basics of Time Series and Forecasting
- Linear model
- Exponential Smoothing Methods
- ARIMA models
Explore Deep Neural Networks with the help of PyTorch.
- Fundamentals of PyTorch
- Forward and Backward Propagation
- ANN, CNN, and RNN
- GRU and Applications
Learn to extract visual information and allow machines to imitate the optical systems of humans.
- Face application
- Object Classification
- Identification verification
- Image segmentation and semantic segmentation cv in healthcare
- Working with videos
- Image search engine
Recognize how computers understand and comprehend the human language.
- NLP Applications
- Text Processing
- Feature Extraction Techniques
- Python Packages for NLP
- Topic Modeling
- Pre-trained Word Embeddings
Use Transfer Learning to improve the efficiency of a new model by transferring knowledge from a pre-learned model.
- What is Transfer Learning
- What, When and How to do Transfer Learning
- Inductive Bias
- Types of Transfer Learning in Deep Learning
Learn about the best standard practices of the Software Engineering industry to enable modularity, simplicity, and readability of the code.
- Coding Practices
- Testing and Debugging
- Version control
Frequently Asked Questions
All you need is a stable internet connection and a laptop to stream the video classes. The course starts with the most basic concepts of Python.
Your system should have a minimum of 4 GB RAM. You can also run your programs in Google Colab or Kaggle Notebooks.
You will be able to build analytic and predictive models in your daily work and develop programs of a faster magnitude. You will also be able to optimize the performance of your algorithms effectively.
Whether you are a student or a professional, the knowledge you gain from the course will open a bundle of opportunities, in terms of job and amazing problem-solving skills.
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