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Data Science
Harness the potential of data with our all-inclusive Data Science course.
Gain insights from industry leaders and hands-on projects.
Develop skills in analytics, visualization, and machine learning.
Boost your career prospects and become a data-driven professional!
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10 live projects + 1 capstone project
Weekly tasks and reports
40+ hours of video content access
Certificate of completion
Overview
Learn Data Science today
Data Science is an interdisciplinary field that extracts and organises data using various scientific methods and algorithms. Build the necessary foundation to become an expert in the domain of Data Science.
Course Curriculum
Discover our comprehensive Data Science course curriculum, designed to provide in-depth knowledge and practical skills.
Python
- Data Types and Operators: Understand Python’s fundamental data types and operators.
- Control Flow: Learn about conditional statements and loops.
- Scripting: Develop scripts for automating tasks using Python.
- Object-Oriented Programming (OOP) in Python: Master OOP concepts such as classes and inheritance.
Data Analysis
- Data Analysis Process: Learn the step-by-step process of data analysis.
- Python Tools for Data Analysis: Dive into data analysis libraries like NumPy and Pandas.
- Data Plotting: Use Matplotlib and Seaborn for data visualization.
Statistics for Data Analysis
- Constructs: Understand basic statistical constructs.
- Population vs Sample: Differentiate between population and sample statistics.
- Correlation vs Causation: Learn about the difference between correlation and causation.
- Hypotheses: Formulate hypotheses for statistical testing.
- Experimentation: Understand experimental design and hypothesis testing.
- Visualizing Data: Use Python libraries like Matplotlib and Seaborn for data visualization.
- Central Tendency and Variability: Explore measures of central tendency and variability.
- Standardizing and Sampling Distribution: Learn about standardization and sampling distributions.
- Estimating and Hypothesis Testing: Perform hypothesis testing and parameter estimation.
- T-Tests: Conduct T-tests for comparing means.
SQL for Data Analysis
- Basic SQL: Learn SQL syntax and commands.
- SQL Join: Understand different types of SQL joins for combining data.
- Aggregations: Perform aggregations using SQL functions.
- Subqueries & Temporary Tables: Use subqueries and temporary tables for complex queries.
- SQL Data Cleaning: Clean and preprocess data using SQL.
- Window Functions: Utilize window functions for advanced data analysis.
- SQL Advanced JOINs: Explore advanced JOIN operations.
- Performance Tuning: Optimize SQL queries for better performance.
- Accessing Database using Python: Connect Python applications to databases using SQL.
Introduction to Deep Learning
- Introduction to Deep Learning: Learn the basics of deep neural networks using Keras, TensorFlow, and PyTorch.
- Artificial Neural Networks (ANN): Understand the architecture and training of ANNs.
- Convolutional Neural Networks (CNN): Learn CNNs for image recognition tasks.
- Recurrent Neural Networks (RNN): Explore RNNs for sequential data analysis.
- Autoencoders and GANs: Study autoencoders and generative adversarial networks.
- Natural Language Processing (NLP): Apply deep learning techniques to NLP tasks.
Edu-Station Certificate
Upon successfully completing this course, you will receive a certificate of completion that helps potential employers assess your proficiency.
Pricing Plans
LExperience the premium features at an affordable price. Get industrial experience, 10+ live working projects and mentorship from top 1 percentile mentors and much more. Choose the plan that suits your needs and take your practical and outcome based learning to the next level. Join today and lead tomorrow.
FAQs
Explore our FAQ section for quick answers to common questions. Can’t find what you’re looking for? Contact us for assistance.
Are there any necessary prerequisites for this course?
No, there are no specific prerequisites for this course. The course is structured in a manner that covers the very basics of the topics as well. So if you’re a complete beginner, this is a great course to start with.
What are the requirements for the classes?
You’ll need a secure and stable internet connection on a well functioning device such as a laptop or mobile phone. We also recommend keeping a notepad and pen/pencil alongside to jot down notes.
Who do I contact for more information regarding the course?
You can get in touch with us via email – support@edustation.co.in or call us at the phone number provided in the ‘Contact Us’ section.
What are the possible career options for Data Science?
Some options are – Data Scientist, Machine Learning Scientist/Engineer, Infrastructure Architect and Data Engineer.
