Overview

This data science course is ideal for those who aspire to become Data Scientists or Data Analysts, as well as working professionals who are interested in transitioning into the field of Data Science. While some basic Mathematics and Statistics knowledge is required, we have designed the course to be accessible to anyone with a strong motivation to learn and understand the topics.

Through this course, you will gain practical, hands-on experience in data science, and will be involved in group projects to further enhance your skills. Whether you are early in your career or seeking a career change, this course provides an excellent opportunity to learn and develop your data science expertise.

What You’ll Learn

The program curriculum is designed to cover 6 main topics: Introduction, Machine learning & Artificial intelligence, Regression, Classificaiton, Neural network & deep learning and time series. In addition, you will have the opportunity to work on a live project. 

 Week 1:

Week 1 introduces Anaconda, Jupyter Notebook, and Python programming for data science. You’ll learn key libraries, data visualization, and basic statistics while analyzing bank and finance data. By the end of the week, you’ll have a solid foundation in Python programming for data science

Week 2:

Week 2 of our data science course covers machine learning and artificial intelligence. You’ll learn about clustering techniques, the K-means algorithm, and supervised learning with different algorithms like linear regression, logistic regression, decision trees, and random forests. You’ll also explore concepts such as overfitting and cross-validation. By the end of the week, you’ll have a solid foundation in machine learning and be ready for advanced topics..

 Week 3:

Week 3 of our data science course covers supervised learning, where you’ll learn about linear regression and classification for predicting target variables. You’ll gain practical skills in implementing these techniques in Python and evaluating model performance, which can be applied to real-world problems like customer behavior and fraud detection.

By the end of the week, you’ll have a strong foundation in supervised learning, enabling you to pursue a career in data science or enhance your current skill set.

 Week 4:

In Week 4 of our data science course, you’ll learn all about classification and how it can be used to detect and prevent anomalies like fraud and spam. You’ll gain practical skills in building and training classification models using algorithms such as logistic regression, decision trees, and random forests. Join us for an exciting week of learning and gain an edge in the field of data science!

 Week 5:

Week 5 is all about neural networks and deep learning. You’ll learn about their basics and applications in various industries. You’ll also get hands-on experience building and training your own neural network model through the Tensor Flow Classification Project.

By the end of the week, you’ll have practical skills and a solid understanding of neural networks and deep learning, preparing you for advanced topics in data science.

 Week 6 :

Week 6 of our data science course covers time series analysis, including topics such as stationarity, autocorrelation, decomposition, and forecasting. Participants will work on a project analyzing time series data to apply what they’ve learned. By the end of the week, you’ll have practical skills to analyze time series data and make predictions about future trends

Week 7 – 12: 

Live Data Science projects

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