CERTIFICATE PROGRAM IN DATA SCIENCE

  • Get trained on Python, Statistics and Machine Learning algorithms.
  • Comprehensive Course Syllabus.
  • 4 Months Course Duration.
  • Live Virtual Class led by Experienced Trainers.

About the Course

The Data Science program offered at our academy is designed to train the learners with the desired skills and techniques required to become a professional data scientist. The program covers a wide range of topics, including statistical analysis, data wrangling and cleaning, data visualization, machine learning and various other aspects of data science.

The program is made fully online with live and interactive sessions handled by industry experienced instructors.

Statistics and Probability: This module covers the fundamental principles of statistics and probability, which are essential for working with data. Students will learn about probability distributions, hypothesis testing, and regression analysis.

Data Wrangling and Cleaning: This module imparts knowledge to collect, clean, and transform data into a format that is suitable for analysis. This includes techniques for handling missing data, dealing with outliers, and merging datasets.

Data Visualization: The module covers how to create effective visualizations that communicate insights from data. Students will learn about different types of visualizations and the best practices for creating them.

Machine Learning: This module is about the different types of machine learning techniques, including supervised and unsupervised learning. Students will learn how to use machine learning algorithms to create predictive models and make data-driven decisions.

Data Ethics and Privacy: This module covers the ethical and legal considerations that come with working with data. Students will learn about privacy concerns and the responsible use of data.

Project-based Learning: Throughout the course, students will work on real-world projects that will help them apply the skills they have learned in a practical setting.

Course Syllabus

PYTHON FOR DATA SCIENCE STATISTICS FOR DATA SCIENCE MACHINE LEARNING
Machine Learning Introduction Linear Regression (Hands-on) Logistic Regression (Hands-on) K Nearest Neighbour Classifier (KNN) K Means Clustering Decision Trees Naive Bayes Classifier Support Vector Machine (SVM) Artificial Neural Network Time Series Forecasting Principal Component Analysis (PCA) Other ML Concepts Feature Engineering ML Model Deployment
GIT FOR DATA SCIENTISTS PROJECT WORK

To know more about the course & fee Details, check the Brouchure