What You’ll Learn in This Data Science Course

  • Master the entire toolbox needed to become a data scientist.
  • Gain in-demand skills such as statistical analysis, Python programming (NumPy, Pandas, Matplotlib, Seaborn), machine learning (scikit-learn, statsmodels), and deep learning (TensorFlow).
  • Impress employers with hands-on experience in real-world business applications.
  • Pre-process and analyze data effectively.
  • Understand the mathematics behind machine learning to build models with confidence.
  • Develop business intuition while solving data-driven problems.
  • Enhance ML algorithms by tackling overfitting, underfitting, validation, and hyperparameter tuning.

Module 1: Introduction to Data Science

  • Overview of Data Science and its Disciplines
  • Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI)
  • Popular Data Science Techniques & Tools
  • Careers in Data Science

Module 2: Probability & Statistics

  • Basic Probability Concepts & Formulas
  • Bayesian Inference & Combinatorics
  • Probability Distributions (Binomial, Poisson, Normal)
  • Descriptive & Inferential Statistics
  • Hypothesis Testing & Confidence Intervals

Module 3: Python for Data Science

  • Introduction to Python Programming
  • Data Structures: Lists, Tuples, Dictionaries
  • Control Flow & Functions in Python
  • Object-Oriented Programming (OOP)
  • Using Jupyter Notebooks

Module 4: Data Manipulation & Visualization

  • Working with Pandas for Data Analysis
  • Data Cleaning & Preprocessing
  • Data Visualization with Matplotlib & Seaborn
  • Exploratory Data Analysis (EDA)

Module 5: Machine Learning Fundamentals

  • Linear & Logistic Regression
  • Supervised vs. Unsupervised Learning
  • Feature Selection & Model Evaluation
  • Overfitting, Underfitting, and Model Optimization

Module 6: Deep Learning & AI

  • Introduction to Neural Networks
  • TensorFlow & Keras for Deep Learning
  • CNNs for Image Recognition
  • Hyperparameter Tuning & Optimization

Module 7: Big Data & Software Integration

  • APIs & Data Connectivity
  • Working with SQL for Data Retrieval
  • Introduction to Cloud Computing (AWS, GCP)
  • Data Processing with Spark

Module 8: Case Studies & Projects

  • Real-World Business Applications
  • End-to-End Data Science Projects
  • Deploying Machine Learning Models
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