Data Science Course 2nd November – 30th November 2024 (Weekends: Saturdays)

The course provides the entire toolbox you need to become a data scientist.

Fill up your resume with in demand data science skills and impress interviewers by showing an understanding of the data science field.

You will learn;

– How to pre-process data

– Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

– Start coding in Python and learn how to use it for statistical analysis

– Perform linear and logistic regressions in Python

– Carry out cluster and factor analysis

– Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

– Apply your skills to real-life business cases

– Unfold the power of deep neural networks

– Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.

A typical data science course is designed to cover various modules or topics. Here are some modules you will find in our data science course:

1. Introduction to Data Science: This module usually covers the fundamentals of data science, its applications, and the role of a              data scientist.

2. Mathematics and Statistics: This module often includes topics like linear algebra, calculus, probability, and statistical analysis,          which are essential for understanding data.

3. Programming: You’ll likely learn programming languages such as Python or R, which are widely used in data science.

4. Data Collection and Cleaning: This module focuses on gathering data from various sources and cleaning it to make it suitable for      analysis.

5. Data Analysis and Visualization: You’ll learn how to use tools and libraries to analyze data and create visualizations for better            understanding.

6. Machine Learning: This is a crucial module covering the theory and practical application of machine learning algorithms.

7. Big Data Technologies: Some courses include modules on big data tools like Hadoop and Spark for handling large datasets.

8. Deep Learning: For more advanced courses, deep learning modules cover neural networks and their applications.
9. Data Ethics and Privacy: You’ll learn about the ethical considerations and legal aspects of working with data.

10. Project Work: Many courses involve a final project where you apply what you’ve learned to a real-world data problem.

11. Domain-specific Applications: Depending on the course, there may be modules focusing on data science applications in specific fields, like healthcare, finance, or marketing.

12. Tools and Libraries: You’ll be introduced to various data science tools and libraries, like Jupyter, Pandas, NumPy, scikit-learn, and more.

These modules provide a well-rounded education in data science, preparing students to tackle a wide range of data-related tasks and problems.