Description
DATA SCIENCE WITH PYTHON COURSE CONTENT
Introduction to Data Science
Data science introduction
Data science toolkit
Job outlook
Prerequisite, Target audience
Data science project lifecycle – CRISP-DM
Model
Basics of Statistics
Statistics concepts
Random variable
Type of Random variables
Central Tendencies – Mean, Mode, Median
Probability, Probability Distribution of
Random variables, PMF, PDF, CDF
Type of RV – Nominal, Ordinal, Interval, Ratio:
Variance, Standard Deviation
Normal Distribution, Standard Normal
Distribution
Binomial Distribution
Poisson Distribution
Advanced Statistics
Sampling
Inferential Statistics
Sampling Distribution
Central Limit Theorem
Simulation
Null and Alternative Hypothesis
Hypothesis Testing
1 tail test and 2 tail test, type 1 and type error
z test & t-test
Python Programming for Data Science (Lab)
Introduction to Python, Anaconda & Spyder
Installation & Configuration
Data Structures in Python
-List
-Tuples
-Array in NumPy
-Matrices
-Data frame in Pandas
Control Structure & Functions – If- Else, For, Loop, While loop
Slicing, dicing & filter operations
Applied Statistics in Python (Lab)
Normal distribution
Simulation
Hypothesis testing
Other statistical concepts using Python
Graphics and Data Visualization, Exploratory Data Analysis in Python(Lab)
Graphics and Data Visualization libraries in python
-Plotly
-Matplotlib
-Seaborn
-Other useful packages/functions in Python
Exploratory Data Analysis Exercise in Python
Machine Learning Concepts
Introduction to machine learning
Supervised and Unsupervised ML
Parametric/Non-parametric Machine Learning
Algorithms
Machine Learning Models
– Linear Regression
– Logistic Regression
– Classification & KNN
– Decision trees
– Random Forest
– Clustering – K Means & hierarchical clustering
– Time Series Analysis
– ARMA Models
– Support vector Machine
Model Validation/Cross-validation techniques
parameter tuning
Model evaluation metrics, MSE, RMSE, R
square, Adjusted R Square
Confusion Matrix
Bias and Variance
Underfitting, Over Fitting
Real-World Data Science & Machine Case Studies in Python (Lab)
ML Case Studies on
– Regression
– Classification
– Decision Tree
– Random Forest
– Clustering
– Time Series Analysis