What you'll learn
✔️ Master Python programming for data analysis
✔️ Work with NumPy & Pandas for data handling
✔️ Create beautiful visualizations with Matplotlib & Seaborn
✔️ Perform Exploratory Data Analysis (EDA) on real datasets
✔️ Apply statistics & probability for data-driven decision making
✔️ Preprocess data & engineer features for machine learning models
✔️ Build Supervised ML models: Linear/Logistic Regression, Decision Trees, Random Forests
✔️ Apply Unsupervised ML models: Clustering (K-Means), Dimensionality Reduction (PCA)
✔️ Explore Deep Learning basics with TensorFlow/Keras
✔️ Perform NLP tasks like sentiment analysis
✔️ Build a Capstone Project to showcase in your portfolio
✔️ Gain the confidence to crack data science interviews and land your first job
✔️ Work with NumPy & Pandas for data handling
✔️ Create beautiful visualizations with Matplotlib & Seaborn
✔️ Perform Exploratory Data Analysis (EDA) on real datasets
✔️ Apply statistics & probability for data-driven decision making
✔️ Preprocess data & engineer features for machine learning models
✔️ Build Supervised ML models: Linear/Logistic Regression, Decision Trees, Random Forests
✔️ Apply Unsupervised ML models: Clustering (K-Means), Dimensionality Reduction (PCA)
✔️ Explore Deep Learning basics with TensorFlow/Keras
✔️ Perform NLP tasks like sentiment analysis
✔️ Build a Capstone Project to showcase in your portfolio
✔️ Gain the confidence to crack data science interviews and land your first job
Who is this for
✅ Beginners who want to start a career in Data Science, Machine Learning, or AI
✅ Students preparing for Data Science & Python certifications
✅ Software developers who want to transition into data science
✅ Analysts who want to upgrade from Excel to Python for data analysis
✅ Professionals & graduates looking to add in-demand data science skills to their profile
✅ Students preparing for Data Science & Python certifications
✅ Software developers who want to transition into data science
✅ Analysts who want to upgrade from Excel to Python for data analysis
✅ Professionals & graduates looking to add in-demand data science skills to their profile
Course Content
- Course Overview: What is Data Science? (video)
- Applications in business, research, AI/ML (video)
- Setting up Python (Anaconda/Jupyter Notebook/VS Code) (video)
- Python Basics: variables, data types, operators (video)
- Control Flow: if, for, while (video)
- Functions & Modules (video)
- Data Structures in Python (lists, tuples, sets, dictionaries) (video)
- String operations (video)
- List comprehensions (video)
- Working with Libraries: NumPy basics (arrays, indexing, slicing) (video)
- NumPy operations and functions (video)
- Introduction to Pandas DataFrame & Series (video)
- Importing data (CSV, Excel, JSON) (video)
- Data exploration (head, info, describe) (video)
- Data cleaning (handling missing values, duplicates, outliers) (video)
- Data transformations (apply, map, groupby) (video)
- Introduction to Data Visualization (video)
- Matplotlib basics (line, bar, scatter, pie charts) (video)
- Seaborn for advanced visualization (video)
- Heatmaps, pair plots, boxplots, histograms (video)
- Customizing plots for reports (video)
- EDA workflow & importance (video)
- Descriptive statistics, distributions (video)
- Correlation & covariance (video)
- Case Study: Perform EDA on a real dataset (Titanic / Sales data) (video)
- Feature extraction insights (video)
- Probability basics, random variables (video)
- Mean, median, mode, variance, standard deviation (video)
- Normal distribution (video)
- Hypothesis Testing (t-test, chi-square, ANOVA) (video)
- Confidence intervals & p-values (video)
- Feature scaling (normalization, standardization) (video)
- Encoding categorical variables (LabelEncoder, OneHotEncoder) (video)
- Feature selection techniques (video)
- Train-Test Split & Cross-validation (video)
- Handling imbalanced data (video)
- What is Machine Learning? Types (Supervised, Unsupervised, Reinforcement) (video)
- ML workflow & pipeline (video)
- Linear Regression (theory + implementation in Python) (video)
- Multiple Regression & evaluation metrics (RMSE, R²) (video)
- Logistic Regression (classification) (video)
- Decision Trees (video)
- Random Forests & Ensemble Methods (video)
- Model evaluation (confusion matrix, precision, recall, F1-score, ROC curve) (video)
- Clustering (K-Means, Hierarchical) (video)
- Choosing optimal clusters (Elbow method, Silhouette score) (video)
- Dimensionality Reduction (PCA, t-SNE) (video)
- Applications in real datasets (video)
- Introduction to Neural Networks (video)
- Basics of Deep Learning (TensorFlow/Keras overview) (video)
- Natural Language Processing (NLP) basics (video)
- Text preprocessing & sentiment analysis mini-project (video)
- Capstone Project: End-to-End Data Science Project (options: Sales Prediction, Customer Segmentation, Sentiment Analysis, Fraud Detection) (video)
- Applying EDA, preprocessing, ML models (video)
- Project Presentation & Feedback (video)
- Career Guidance: How to build a Data Science portfolio (video)
- Preparing for Data Science Interviews (video)
Requirements
No prior programming experience required — we’ll teach you Python from scratch
A computer with internet access (Windows/Mac/Linux)
Curiosity and eagerness to work with data
(Optional) Basic math/statistics knowledge is a plus but not mandatory
A computer with internet access (Windows/Mac/Linux)
Curiosity and eagerness to work with data
(Optional) Basic math/statistics knowledge is a plus but not mandatory
Description
Do you want to become a Data Scientist and work on real-world projects that make an impact?
This Data Science with Python A-Z Masterclass is designed to take you from beginner to job-ready Data Scientist. You’ll start with Python basics and progress step-by-step into data analysis, visualization, statistical modeling, machine learning, and hands-on projects.
Over 12 weeks (48 hours of live training), you’ll learn how to use Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, and TensorFlow/Keras to analyze datasets, build predictive models, and gain insights from data.
By the end of this course, you’ll have a capstone project, mini-projects, and portfolio-ready skills that will help you land opportunities in data science, analytics, AI, and machine learning.
