SuperseedeLearning

Data Science Masterclass

Become a data science expert with Python, machine learning, and deep learning.

Duration
14 Weeks
Level
Intermediate to Advanced

About this Course

This masterclass provides an in-depth journey into data science, covering Python programming for data analysis, statistical modeling, machine learning algorithms, and deep learning concepts. You'll work on real-world datasets and build a strong portfolio.

What You'll Learn

  • Master Python for data manipulation, analysis, and visualization.
  • Apply statistical methods and hypothesis testing to real-world problems.
  • Build and evaluate various machine learning models.
  • Develop and train deep learning models for image and text data.
  • Understand and implement Natural Language Processing techniques.
  • Deploy data science solutions and manage their lifecycle.

Course Modules

Module 1: Python for Data Science
  • Python fundamentals and advanced features
  • NumPy for numerical computing
  • Pandas for data manipulation and analysis
  • Data loading, cleaning, and preprocessing
Module 2: Statistical Foundations for Data Science
  • Descriptive Statistics
  • Probability and Probability Distributions
  • Inferential Statistics (Hypothesis Testing, A/B Testing)
  • Regression Analysis
Module 3: Data Visualization & Exploratory Data Analysis (EDA)
  • Matplotlib and Seaborn for static plots
  • Plotly and Bokeh for interactive visualizations
  • Techniques for effective EDA
  • Storytelling with data
Module 4: Machine Learning Fundamentals
  • Introduction to Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Linear Regression, Logistic Regression
  • Decision Trees, Random Forests, Gradient Boosting
  • Support Vector Machines (SVM)
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score, RMSE)
Module 5: Unsupervised Learning & Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Principal Component Analysis (PCA) for dimensionality reduction
Module 6: Deep Learning with TensorFlow/Keras
  • Introduction to Neural Networks
  • Perceptrons, Activation Functions
  • Building Feedforward Neural Networks
  • Convolutional Neural Networks (CNNs) for Image Processing
  • Recurrent Neural Networks (RNNs) for Sequence Data
Module 7: Natural Language Processing (NLP)
  • Text Preprocessing (Tokenization, Stemming, Lemmatization)
  • Word Embeddings (Word2Vec, GloVe)
  • Sentiment Analysis
  • Text Classification
  • Introduction to Transformer models for NLP
Module 8: Big Data & Deployment
  • Introduction to Big Data concepts (Hadoop, Spark)
  • Building and deploying machine learning models (Flask, FastAPI)
  • MLOps concepts
  • Capstone Project: End-to-end data science project.

Course Overview

Prerequisites

  • Basic programming knowledge, high school level mathematics.

Target Audience

  • Aspiring Data Scientists
  • Data Analysts looking to advance their skills
  • Software Engineers interested in AI/ML
  • Researchers and Academics

Technologies Covered

PythonPandasNumPyScikit-learnTensorFlowKerasSQLMatplotlibSeaborn