Module 1: Foundation for Data Science

Get an idea of what is data science. Get acquainted with various analysis and visualization tools used in data science.

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Hands-on: No hands-on

Module 2: Probability and Statistics

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.

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Hands-on: Learn to implement statistical techniques with Microsoft Excel.

Module 3: Basics of Python

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.

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Module 4: Python Built-In Data Structures

Learning Objectives:
Understand the various Data structures that are used in Python.

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Hands-on: Write Python Code to understand and implement Python Data Structures.

Module 5: Control and Loop Statements in Python

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

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Hands-on: Write Python Code to implement loop and control structures in R.

Module 6: Functions and Classes in Python

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:

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.

Module 7: Working with Data

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

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Hands-on: Write Python Code to read and write data from/to Python.

Module 8: Analyze Data using Pandas

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

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Hands-on: Write Python code to manipulate data frames and chu insights using various Python libraries.

Module 9: Visualize Data

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

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Hands-on: Use Python visualization libraries like Matplotlib, Seabo & ggpplot.

Module 10: Advanced Statistics and Predictive Modeling

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 least 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.

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Hands-on:

Module 11: Time Series Forecasting

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.

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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.

Module 12: Introduction to Machine Learning

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.

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Hands-on:

Prerequisites

Certification