Data Science with Python Training

This Hands-on training program on Data Science drives participants through the basics of Python and Statistics before diving in and exploring Data Science in depth. It takes participants through exploratory as well as Real time scenarios in Data Science and also touches base on introduction to Machine Learning.

3 Days

This training requires participants to have a basic understanding in Python Programming. A knowledge in Mathematics & Statistics would be helpful to attend the training program.

  • Data Science Introduction
  • Data Science Toolkit
  • Job outlook
  • Prerequisite, Target Audience
  • Data Science Project Lifecycle – CRISP-DM Model

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

Sampling
Inferential Statistics
Sampling Distribution
Central Limit Theorem
Simulation
Null and Alternative Hypothesis
Hypothesis Testing
1 tail test and 2 tail test, type I and Type II error
z test & t test

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

Graphics and Data Visualization libraries in Python
– Plotly
– Matplotlib
– Seaborn
– other useful packages/functions in Python
Exploratory Data Analysis Exercise in Python

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
– ARIMA 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.

ML Case Studies on
– Regression
– Classification
– Decision Tree
– Random Forest
– Clustering
– Time Series Analysis