Course
Understand the fundamentals of Data Science and its applicationsGain proficiency in Python and R programming for data analysisMaster data cleaning, preprocessing, and visualization techniquesApply statistical and mathematical concepts for data-driven decisionsDevelop predictive models using machine learning algorithmsExplore advanced topics like deep learning and neural networksLearn to manage and query structured and unstructured dataUse tools like SQL, Power BI, Tableau, and TensorFlow effectivelyBuild, train, and evaluate real-world data science projectsInterpret analytical results to drive business insightsGain knowledge of cloud platforms for deploying data modelsStrengthen problem-solving and analytical thinking abilitiesPrepare for industry certifications and job interviews in Data ScienceUnderstand ethics, privacy, and data governance principlesBecome job-ready as a certified Data Science Professional
Course Description
This program provides a comprehensive deep dive into Data Science, covering Python programming, statistical analysis, machine learning, deep learning, big data processing, and AI applications. Students will gain hands-on experience with realworld datasets, learn datadriven decision-making, and develop models for predictive analytics.
Modules
- Introduction to Data Science & Python for Data Analysis
- Statistics, Probability & Data Visualization
- Machine Learning Fundamentals
- Unsupervised Learning & Feature Engineering
- Deep Learning & AI with TensorFlow & Keras
- Big Data & Cloud Computing for Data Science
- Natural Language Processing (NLP) & AI for Business
- Capstone Project & Career Opportunities
Projects
- Perform data cleaning & visualization on a real-world dataset using Pandas & Matplotlib.
- Conduct an EDA report on a dataset and present statistical insights using visualization tools.
- Build a House Price Prediction Model using Linear Regression.
- Implement a Spam Email Classifier using Logistic Regression.
- Perform Customer Segmentation using K-Means Clustering.
- Apply PCA for Feature Reduction on a dataset.
- Implement a Handwritten Digit Recognition Model using CNN.
- Train a Sentiment Analysis Model using RNN on Movie Reviews.
- Deploy a Machine Learning Model as a REST API using Flask & AWS.
- Process a Big Data Pipeline using Apache Spark.
- Develop a Recommendation System for E-Commerce Platforms.
- Develop a real-world Data Science project on a dataset of your choice.
Assignments
- Perform data cleaning & visualization on a real-world dataset using Pandas & Matplotlib.
- Conduct an EDA report on a dataset and present statistical insights using visualization tools.
- Build a House Price Prediction Model using Linear Regression.
- Implement a Spam Email Classifier using Logistic Regression.
- Perform Customer Segmentation using K-Means Clustering.
- Apply PCA for Feature Reduction on a dataset.
- Implement a Handwritten Digit Recognition Model using CNN.
- Train a Sentiment Analysis Model using RNN on Movie Reviews.
- Deploy a Machine Learning Model as a REST API using Flask & AWS.
- Process a Big Data Pipeline using Apache Spark.
- Develop a Recommendation System for E-Commerce Platforms.
- Develop a real-world Data Science project on a dataset of your choice.
Certificate
Practical AI-integrated Project (40%)
Final Online Test (60%)
NxT Certified Data Science Professional Certificate awarded
upon successful completion.
Download Course Brochure
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