Data Science with Python - Training & Certification

Module 1- Intro to Data Science

Learning Objectives: Get an idea of what is data science. Why data science is "Rosy" or "Handy" or "Fascinating"
Get acquainted with various analysis and visualization tools used in  data science

Topics

• What is Data Science?
• Analytics Landscape
• Life Cycle of a Data Science Projects
• Data Science Tools & Technologies

Hands-on:  No hands-on

Module 2- Mastering Python

Learning Objectives:  Learn how to install Python distribution - Anaconda Learn basic data types, strings & regular expressions
Data structures that are used in Python
Learn all about loops and control statements in Python
Write user-defined functions in Python. Learn about Lambda function. Learn object oriented way of writing classes & objects
Learn how to import datasets into Python. Also learn how to write output into files from Python
Manipulate & analyze data using Pandas library. Learn generating insights from your data
Use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization
Hands-on session on a real-life case study

Topics

• Python Basics
• Data Structures in Python
• Control & Loop Statements in Python
• Functions & Classes in Python
• Working with Data
• Analyze Data using Pandas
• Visualize Data
• Case Study
  

Hands-on:

• Know how to install python distribution like anaconda and other libraries
• Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects
• write python code to import dataset into python notebook
• Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset       
  

Learning Objectives:  Visit basics like mean (expected value), median and mode
Distribution of data in terms of variance, standard deviation and interquartile range
Basic summaries about the data and the measures. Together with simple graphics analysis
Basics of probability with daily life examples
Marginal probability and its importance with respective to datascience
Learn baye's theorem and conditional probability
Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value,

Topics

• Measures of Central Tendency
• Measures of Dispersion
• Descriptive Statistics
• Probability Basics
• Marginal Probability
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing

Hands-on: Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Module 4- Advanced Statistics & Predictive Modeling - I

Learning Objectives:  Analysis of Variance and its practical use
Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. It covers strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization
Real Life Case Study with Linear Regression
Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
Real-Life case study with PCA & FA

Topics

• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA
  

Hands-on:

• With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
• Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling
  

Module 5- Advanced Statistics & Predictive Modeling - II

Learning Objectives: Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value
Real Life Case Study with Binomial Logistic Regression
KNN Algorith for Classification Problem. Covers techniques that are used to find the optimum value for K
Real Life Case Study with KNN
Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
Real Life Case Study with Decision Tree

Topics

• Logistic Regression
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree

Hands-on:

• With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues
• Predict if a patient is likely to get any chronic kidney disease depending on the health metrics
• Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees)

Learning Objectives: Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
Real Life Case Study with ARIMA

Topics

• Understand Time Series Data
• Visualizing TIme Series Components
• Exponential Smoothing
• Holt's Model
• Holt-Winter's Model
• ARIMA
• Case Study: Time Series Modeling on Stock Price

Hands-on:

• Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
• Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smooting constants.
• Write python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives: An industry mentor guided group project to handle a real-life project. The same way you would execute a data science project in any business problem

Topics

• Industry relevant capstone project under experienced industry-expert mentor

Hands-on: Project to be selected by candidates.

Projects

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques