Data Science with Python
Learn data science with real-world projects & industry tools
About the Course
Gain expertise in data science by mastering Python, statistics, machine learning, and data visualization. Work on real datasets and build portfolio-ready projects.
Tip: Hybrid mentorship pairs you with an expert to review code, unlock roadblocks, and plan your portfolio.
Prerequisites
- Basic Python programming
- Math & Statistics fundamentals
- Laptop + stable internet
- Interest in data-driven problem solving
What You’ll Learn
Data cleaning & preprocessing
Exploratory Data Analysis (EDA)
Probability, statistics & hypothesis testing
Regression, classification & clustering models
Feature engineering & model evaluation
Big data tools & intro to deep learning
Capstone projects with real datasets
Who This Course is For
Great fit for students.
Great fit for fresh graduates.
Great fit for working professionals.
Great fit for career switchers.
Curriculum
▸Module 1 · Introduction to Data Science
- • What is Data Science?
- • Roles of Data Scientist, Analyst & Engineer
- • Data lifecycle & workflow
- • Case studies in data science
▸Module 2 · Python for Data Science
- • NumPy for arrays
- • Pandas for data handling
- • Matplotlib & Seaborn for visualization
- • Basic data wrangling
▸Module 3 · Data Cleaning & Preprocessing
- • Handling missing values
- • Data transformation & scaling
- • Encoding categorical variables
- • Outlier detection
▸Module 4 · Exploratory Data Analysis (EDA)
- • Summary statistics
- • Data distributions & visualization
- • Correlation analysis
- • Insights generation
▸Module 5 · Probability & Statistics
- • Descriptive vs Inferential statistics
- • Probability distributions
- • Hypothesis testing
- • ANOVA & chi-square tests
▸Module 6 · Regression & Classification
- • Linear & Logistic Regression
- • Decision Trees & Random Forests
- • Support Vector Machines (SVM)
- • Evaluation metrics
▸Module 7 · Clustering & Recommendation Systems
- • K-means clustering
- • Hierarchical clustering
- • Principal Component Analysis (PCA)
- • Recommendation systems basics
▸Module 8 · Feature Engineering & Model Tuning
- • Feature selection methods
- • Dimensionality reduction
- • Hyperparameter tuning (Grid Search, Random Search)
- • Cross-validation techniques
▸Module 9 · Introduction to Big Data & Deep Learning
- • Spark & PySpark basics
- • Intro to TensorFlow/Keras
- • Neural networks for data science
- • Use cases of deep learning in DS
▸Module 10 · Capstone Project
- • Select dataset
- • Apply data science pipeline
- • Model training & evaluation
- • Project report & presentation
Projects
Predict future sales using regression models.
Use clustering techniques to segment customers.
Build a recommendation system using collaborative filtering.
Classify fraudulent vs genuine transactions.
Analyze patient data for disease prediction.
Testimonials
“I transitioned from accounting to data science with this course. The projects were eye-opening.”
“The EDA and ML modules were very practical. I now use these skills at work daily.”
“Capstone project helped me showcase real experience in job interviews.”
Instructor
Data Scientist with 12+ years experience in analytics, ML, and big data. Mentor for Fortune 500 professionals.
LinkedIn →FAQs
Do I need advanced math for this course?
Only basic statistics and probability are required. We cover essentials step by step.
What datasets will I work with?
You’ll use open-source datasets in finance, healthcare, e-commerce, and more.
Is this course beginner friendly?
Yes, the course starts with Python basics and gradually moves to advanced topics.
Do I get a certificate?
Yes, after completing assessments and the capstone project.
How will this course help in jobs?
You will build a portfolio of data science projects that employers value.