Data frame in Pandas, control structure and Functions – if else, for loop, while loop, slicing, dicing and filter operations.
- Introduction to AI and Data Science
- Data Science Toolkit
- Job outlook
- Prerequisite, Target Audience
- Data Science Project Lifecycle-CRISP-DM
- Model
- Random variables and Type of Random Variables
- Central Tendencies- Mean, Mode, Medan
- Probability, Probability Distribution of Random variables, PMF, pdf, cdf.
- Type of RV- Normal, Ordinal, Interval, Ratio, Variance, Standard Division Normal Distribution, Standard Normal Distribution, Binomial Distribution Poisson Distribution.
- Inferential Statistics
- Sampling Distribution
- Central Limit Theorem and simulation
- Null and Alternative Hypothesis, Hypothesis Testing
- 1 Tail test and 2 tail test, Type 1 and Type II error and Z-test & t test
- Anaconda & Spyder
- Installation and Configuration
- Data Structure in Python
- Applied Statistics in Python (Lab)
- Graphics and Data Visualization libraries in Python
- Statistics Essentials
- Advanced Statistics
- Python Programming for Data Science (Lab)
- Tuples
- Array in NumPy
- Matrices
- Data frame in Pandas, control structure and Functions – if else, for loop, while loop, slicing, dicing and filter operations
- Simulation
- Hypothesis Testing and other statistical concepts using Python
- Matplotlib, Seabom
- Other useful packages. Functions in Python Exploratory Data Analysis exercises in Py
