Introduction to Data Science Course

Learning Objectives:

In this module, you will get an overview of basics like mean (expected value), median and mode and will also learn about distribution of data in terms of variance, standard deviation and interquartile range. Get basic summaries about the data and its measures, together with simple graphics analysis.

Go on to explore the basics of probability with daily life examples. Learn about Marginal probability and its importance with respect to data science. Learn Baye's theorem and conditional probability, and alternate and null hypotheses with various types of errors.

Topics Covered:

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

Hands-on:

Learn to implement statistical techniques with Microsoft Excel.

Learning Objectives:

This module, which covers the basics of Python, teaches how to install Python distribution - Anaconda and gives an understanding of the basic data types, strings & regular expressions.

Topics Covered:

• Install Anaconda
• Data Types & Variables
• String & Regular Expressions

Learning Objectives:

Understand the various Data structures that are used in Python.

Topics Covered:

• Python list
• Python dictionaries
• Python set
• Python tuple
• Comprehensions

Hands-on:

Write Python Code to understand and implement Python Data Structures.

Learning Objectives:In this module, you will learn all about loops and control statements in Python.

Topics Covered:

• For Loop
• While Loop
• Break Statement
• Next Statements
• Repeat Statement
• if, if…else Statements
• Switch Statement

Hands-on:

Write Python Code to implement loop and control structures in R.

 Learning Objectives:

Here you will learn to write user-defined functions in Python, including Lambda function. Also learn the object oriented way of writing classes & objects.

Topics Covered:

• Writing your own functions (UDF)
• Calling Python Functions
• Functions with Arguments
• Calling Python Functions by passing Arguments
• Lambda Functions
• Classes & Objects

Hands-on:

Write Python Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required.

Learning Objectives:

Learn how to import datasets into Python. Also learn how to write output into files from Python.

Topics Covered:

• Reading files with Python
• Writing files from Python
• Reading files using Pandas library
• Saving Data using Pandas library

Hands-on:

Write Python Code to read and write data from/to Python.

Learning Objectives:

Learn to manipulate & analyze data using Pandas library. Learn how to generate insights from your data.

Topics Covered:

• Clean & Prepare Datasets
• Manipulate DataFrame
• Summarize Data
• Churn Insights from Data

Hands-on:

Write Python code to manipulate data frames and churn insights using various Python libraries.

Learning Objectives:

You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization.

Topics Covered:

• Charts using Matplotlib
• Charts using Seaborn
• Charts using ggplot

Hands-on:

Use Python visualization libraries like Matplotlib, Seaborn & ggpplot.

Learning Objectives:

This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.

Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.

Going further, you will explore Binomial Logistic Regression for Binomial Classification Problems, including evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value. You will work with a real-life case study with Binomial Logistic Regression.

Next, you will learn about KNN Algorithm for Classification Problem, including techniques that are used to find the optimum value for K. You will see a real-life case study with KNN Decision Trees, to help you understand regression & classification problems. At the end of this module you will have working knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID, among others.

Topics Covered:

• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA
• Logistic Regression (MLE)
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree

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.
• 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 to be proactive in collecting dues.
• Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

In this module, you will understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data; also work with the Exponential Smoothing Model and know when to use the same.

You will know how to use Holt's model when your data has Constant Data, Trend Data and Seasonal Data and learn how to select the right smoothing constants for each set of circumstances.Finally, you will use Autoregressive Integrated Moving Average Model for building a Time Series Model and carry out a real-life case study with ARIMA.

Topics Covered:

• 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:

Work on a 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:

This module gives you a good understanding of the fundamental issues and challenges of machine learning. You will learn types of machine learning such as Supervised and Unsupervised Learning and get an understanding of the strengths and weaknesses of many popular machine learning approaches.

Learn to appreciate the underlying relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning. Cement your understanding of all these concepts with a case study using Scikit Learn libraries for data manipulation & pre-processing.

Topics Covered:

• What is Machine Learning?
• Supervised Learning
• Unsupervised Learning
• Using Scikit-learn
• Scikit-learn classes
• Case Study: Machine Learning Algorithm

Hands-on:

Use complex datasets to manipulate, prepare and preprocess data for model building exercise. Analyze and treat missing values using various missing value imputation strategies.