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In this course, you will learn :
- You'll start with Python basics, with a focus on CSV files in Python, covering topics like data preprocessing and exploratory data analysis (EDA).
- You'll concentrate on predictive and inferential analysis using statistical and machine learning techniques.
- How these techniques can aid in the resolution of business problems.
Syllabus :
1. What is Data Science
- The Buzzword "Data Science"
- Data Science Lifecycle
- Python for Data Science
2. Python Basics
- Hello World
- Variables and Data Types
- Operators
- Conditional Statements
- Functions
- Lists
- Loops
- Packages and Modules
3. Handling Tabular Data in Python
- Importing Data in CSV Files with Pandas
- Indexing and Selection
- Filtering Data
- Applying Functions to Data
- Aggregating Data
- Grouping Data
- Pivot Tables
- Plotting Data 1: Univariate Plots
- Plotting Data 2: Bivariate Plots
4. Data Cleaning
- Introduction to Data Cleaning
- Data Types
- Missing Values
- Duplicates
- Inconsistent Data
- Outliers
- Outlier Detection and Removal
5. Exploratory Data Analysis
- Analyzing Individual Quantities
- Exploring Categorical Quantities
- Exploring Numerical Quantities
- Correlation and Heatmaps
6. Statistical Inference
- The Basics of Statistical Inference
- Confidence Intervals
- Hypothesis Testing
- One Sample t-Test
- Two Sample t-Test
- Paired t-Test
7. Predictive Models
- A Simple Model
- Model Fitting on a Loss Function
- Gradient Descent
- Optimization with Gradient Descent
- Simple Linear Regression
- Multiple Linear Regression
- Evaluating Regression Models
- Logistic Regression
- Evaluating Logistic Regression Models
8. Machine Learning
- Why Machine Learning
- Machine Learning Pipeline
- Decision Trees
- Random Forests
- Support Vector Machines
- Ensembles: Bagging vs Boosting
- Clustering for Unsupervised Learning
- K-Means on Two-Dimensional Data
- K-Means on n-Dimensional Data
- Test your Knowledge
