Data science kursu

Module 1: Python for Data Science

Duration: 3 weeks

Overview

Data science is a fast-growing new knowledge domain used by organizations to make data driven decisions. Data Scientists wear various hats to work with data and to derive value from it. The Python programming language is an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Python offers you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.

What You’ll Learn

At the end of this module, you’ll learn:

●       How to work with Python interactively in web notebooks

●       The essentials of Python scripting

●       Key concepts necessary to enter the world of Data Science via Python

Why Learn Python?

Python is definitely one of the most popular languages in Data Science, which can be used for data analysis, manipulation, and visualization. Python has access to many Data Science libraries, making it the perfect language for developing applications and implementing algorithms.

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built-in debugger. It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

Which companies use Python?

Many of the biggest and most popular companies use Python. Some of them are:

●       Google, NASA, Amazon

●       Social networking sites like Instagram, Reddit, Quora, etc

●       Media streaming companies like Netflix and Spotify

●       Rideshare companies like Uber and Lyft

“Python has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language.”  - Peter Norvig, Director of Research at Google Inc.

Course Outline

Chapter 1: Introduction to Python

Goal: In this chapter, you will learn about the basic concepts of Python

Topics:

●       An Overview of Python

o   Need for Programming

o   Advantages of Programming

o   About Python

o   Organizations using Python

o   Python Applications in Various Domains

o   Python Installation

o   Starting Python

o   Using the interpreter

o   Running a Python script

o   Python scripts on Unix/Windows

o   Using the editor

●       Getting Started

o   Using variables

o   Built-in functions

o   Operands and Expressions

o   Strings

o   Numbers

o   Converting among types

o   Writing to the screen

o   Command line parameters

●       Flow Control

o   About flow control

o   White space

o   Conditional expressions

o   Relational and Boolean operators

o   While loops

o   Alternate loop exits

Hands on Labs

●       Creating “Hello World” code

●       Variables

●       Demonstrating Conditional Statements

●       Demonstrating Loops

Chapter 2: Sequences, Arrays, Dictionaries, and Sets

Goal: In this chapter, you will learn how to Perform operations on Arrays, Dictionaries, Sets and learn different types of sequence structures, their usage, and execute sequence operations

Topics:

●       About sequences

●       Lists and list methods

●       Tuples

●       Indexing and slicing

●       Iterating through a sequence

●       Sequence functions, keywords, and operators

●       List comprehensions

●       Generator Expressions

●       Nested sequences

●       Working with Dictionaries

●       Working with Sets

Hands on Labs

●       Tuple - properties, related operations, compared with a list

●       List - properties, related operations

●       Dictionary - properties, related operations

●       Set - properties, related operations

Chapter 3:

Goal: In this chapter, you will learn about different types of Functions

Topics:

●       User-Defined Functions

●       Defining functions

●       Concept of Return Statement

●       Concept of __name__=” __main__”

●       Function Parameters

●       Different Types of Arguments

●       Global Variables

●       Global Keyword

●       Variable Scope and Returning Values

●       Lambda Functions

●       Various Built-In Functions

●       Nested functions

Chapter 5: Errors and Exception Handling

Goal: In this chapter, you will learn about address/exceptions in code, types of errors and how to handle these errors

Topics:

●       Syntax errors

●       Exceptions

●       Using try/catch/else/finally

●       Handling multiple exceptions

●       Ignoring exceptions

Chapter 6: Modules and packages

Goal: In this chapter, you will learn how to create generic python scripts and extract/filter content using regex.

Topics:

o   Standard Libraries

o   Packages and Import Statements

o   Reload Function

o   Important Modules in Python

o   Packages and name resolution

o   Naming conventions

o   Using imports

Chapter 7: Classes

Goal: In this chapter, you will learn about various Object-Oriented concepts such as Abstraction, Inheritance, Polymorphism, Overloading, Constructor, and so on

Topics:

●       Defining classes

●       Introduction to Object-Oriented Concepts

●       Built-In Class Attributes

●       Public, Protected and Private Attributes, and Methods

●       Class Variable and Instance Variable

●       Constructor and Destructor

●       Decorator in Python

●       Core Object-Oriented Principles

●       Inheritance and Its Types

●       Method Resolution Order

●       Overloading

●       Overriding

●       Getter and Setter Methods

●       Inheritance-In-Class Case Study

Module 2: Analytics with Python

Duration: 2 weeks

Overview

Learn advanced Python skills for data analysis and visualizations.

This course explores using Python for data scientists to perform exploratory data analysis and complex visualizations. In this course you’ll learn about essential mathematical and statistics libraries such as NumPy and Pandas. It also covers visualization tools like matplotlib and Seaborn.

Course Outline

Chapter 1: – Introduction to NumPy

Goal: In this chapter, you will learn about the basics of Data Analysis using two essential libraries: NumPy and Pandas. You will also understand the concept of file handling using the NumPy library.

Topics:
Topics:
Topics:
 

●       Basics of Data Analysis

●       NumPy - Arrays

●       Operations on Arrays

●       Indexing Slicing and Iterating

●       NumPy Array Attributes

●       Matrix Product

●       NumPy Functions

●       Functions

●       Array Manipulation

●       File Handling Using NumPy

Chapter 2: – Data Manipulation using pandas

Goal: In this chapter, you will gain in-depth knowledge about analyzing datasets and data manipulation using Pandas.

●       Introduction to pandas

●       Data structures in pandas

●       Series

●       Data Frames

●       Importing and Exporting Files in Python

●       Basic Functionalities of a Data Object

●       Merging of Data Objects

●       Concatenation of Data Objects

●       Types of Joins on Data Objects

●       Data Cleaning using pandas

●       Exploring Datasets

●       Analysing a dataset

Chapter 3: – Data Visualization using Matplotlib

Goal: In this chapter, you will learn Data Visualization using Matplotlib.

●       Why Data Visualization?

●       Matplotlib Library

●       Line Plots

●       Multiline Plots

●       Bar Plot

●       Histogram

●       Pie Chart

●       Scatter Plot

●       Boxplot

●       Saving Charts

●       Customizing Visualizations

●       Saving Plots

●       Grids

●       Subplots

●       Rendering

Module Project:

Project 1:

Preparing an analytical report based on available data to help producers of educational programs effectively build a strategy for updating and improving courses.

Project 2:

Preparing an analytical report for the HR department. Based on the analytics, it is necessary to draw up recommendations for the HR department on recruitment strategy and interaction with employees.

Module 3: Statistics for Data Science

Duration: 3 weeks

Overview

The self-paced Statistics module has been designed in such a manner that it is easy for a Data Scientist to get a solid foundation on the concepts. The complete mechanism of Data Science is explained in detail in terms of Statistics and Probability. Data and its types are discussed along with different kind of sampling procedures.

Other essential concepts of Statistics (statistical inference, testing, clustering) are emphasized here as well since that’s a very important part of being a Data Scientist.

Module Objectives

After the completion of this course, you should be able to:

●       Analyze different types of data

●       Master different sampling techniques

●       Illustrate Descriptive statistics

●       Apply probabilistic approach to solve real life complex problems

●       Explain and derive Bayesian inference

●       Understand Clustering techniques

●       Understand Regression modelling

●       Master Hypothesis

●       Illustrate Testing the data

Why learn Statistics?

Statistics and its methods are the backend of Data Science to "understand, analyze and predict actual phenomena". Machine learning employs different techniques and theories drawn from statistical & probabilistic fields. This Statistics Essentials for Analytics Course enables you to gain knowledge of the essential statistics required for analytics and Data Science, understand the mechanism of popular Machine Learning Algorithms like K-Means Clustering, Regression. The course also takes you through the glimpse of hypothesis testing and its methods enabling you perform test on alternative hypothesis.

Chapter 1: Understanding the Data

Learning Objectives:

At the end of this module, you should be able to:

●       Understand various data types

●       Learn Various variable types

●       List the uses of variable types

●       Explain Population and Sample

●       Discuss sampling techniques

●       Understand Data representation

Topics:

●       Introduction to Data Types

●       Numerical parameters to represent data

●       Mean

●       Mode

●       Median

●       Sensitivity

●       Information Gain

●       Entropy

●       Statistical parameters to represent data

Hands-on Labs

●       Estimating mean, median and mode using Python

●       Calculating Information Gain and Entropy

Chapter 2: Probability

Learning Objectives:

At the end of this module, you should be able to:

●       Understand rules of probability

●       Learn about dependent and independent events

●       Implement conditional, marginal, and joint probability using Bayes Theorem

●       Discuss probability distribution

●       Explain Central Limit Theorem

Topics:

●       Uses of probability

●       Need of probability

●       Bayesian Inference

●       Density Concepts

●       Normal Distribution Curve

Hands-on Labs

●       Calculating probability using python

●       Conditional, Joint and Marginal Probability using Python

●       Plotting a Normal distribution curve

Chapter 3: Statistical Inference

Learning Objectives: In this module, you will learn about different statistical techniques and terminologies used in data analysis.

At the end of this module, you should be able to:

●       Understand concept of point estimation using confidence margin

●       Draw meaningful inferences using margin of error

●       Explore hypothesis testing and its different levels

Topics:

●       What is Statistical Inference?

●       Terminologies of Statistics

●       Point Estimation

●       Confidence Margin

●       Hypothesis Testing

●       Levels of Hypothesis Testing

Hands-on Labs

●       Calculating and generalizing point estimates using python

Chapter 4: Testing the Data

Learning Objectives:

At the end of this module, you should be able to:

●       Understand Parametric and Non-parametric Testing

●       Learn various types of parametric testing

●       Discuss experimental designing

●       Explain a/b testing

Topics:

●       Parametric Test

●       Parametric Test Types

●       Non- Parametric Test

●       Experimental Designing

●       A/B testing

Hands-on Labs

●       Perform p test and t tests in Python

●       A/B testing in Python

Chapter 5: Data Clustering

Learning Objectives:

At the end of this module, you should be able to:

●       Understand concept of association and dependence

●       Explain causation and correlation

●       Learn the concept of covariance

●       Discuss Simpson’s paradox

●       Illustrate Clustering Techniques

Topics:

●       Association and Dependence

●       Causation and Correlation

●       Covariance

●       Simpson’s Paradox

●       Clustering Techniques

Hands-on Labs

●       Correlation and Covariance in Python

●       Hierarchical clustering in Python

●       K means clustering in Python

Chapter 6: Regression Modelling

Learning Objectives:

At the end of this module, you should be able to:

●       Understand the concept of Linear Regression

●       Explain Logistic Regression

●       Implement WOE

●       Differentiate between heteroscedasticity and homoscedasticity

●       Learn concept of residual analysis

Topics:

●       Logistic and Regression Techniques

●       Problem of Collinearity

●       WOE and IV

●       Residual Analysis

●       Heteroscedasticity

●       Homoscedasticity

Hands-on Labs

●       Perform Linear and Logistic Regression in Python

●       Analyze the residuals using Python

Module 4: Data Science

Duration: 4 weeks

Chapter 1: Introduction to Data Science

Learning Objectives:

Get an introduction to Data Science module and see how Data Science helps to analyze large and unstructured data with different tools.

Topics:

●       What is Data Science?

●       What does Data Science involve?

●       Era of Data Science

●       Business Intelligence vs Data Science

●       Life cycle of Data Science

●       Tools of Data Science

●       Introduction to Big Data and Hadoop

●       Introduction to R

●       Introduction to Spark

●       Introduction to Machine Learning

Hands-on Labs

●       No lab

Chapter 2: Introduction to Machine Learning

Learning Objectives:

Get an introduction to Machine Learning as part of this chapter. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms

Topics:

●       Python Revision (numpy, Pandas, scikit learn, matplotlib)

●       What is Machine Learning?

●       Machine Learning Use-Cases

●       Machine Learning Process Flow

●       Machine Learning Categories

●       Linear Regression

●       Logistic Regression

●       Gradient descent

Hands-on Labs

●       Implementing Linear Regression model

●       Implementing Logistic Regression model

Chapter 4: Supervised Learning

Learning Objectives:

In this chapter, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Topics:

●       What are Classification and its use cases?

●       What is Decision Tree?

●       Algorithm for Decision Tree Induction

●       Creating a Perfect Decision Tree

●       Confusion Matrix

●       What is Random Forest?

●       What is Naïve Bayes?

●       How Naïve Bayes works?

●       Implementing Naïve Bayes Classifier

●       What is Support Vector Machine?

●       Illustrate how Support Vector Machine works?

●       Hyperparameter Optimization

●       Grid Search vs Random Search

●       Implementation of Support Vector Machine for Classification

Hands-on Labs

●       Implementing Decision Tree model

●       Implementing Linear Random Forest

●       Implementing Navies Bayes model

●       Implementing Support Vector Machine

●       Implementation of Naïve Bayes, SVM

Chapter 5: Dimensionality Reduction

Learning Objectives:

In this Data Science with Python Training module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing an LDA model.

Topics:

●       Introduction to Dimensionality

●       Why Dimensionality Reduction

●       PCA

●       Factor Analysis

●       Scaling dimensional model

●       LDA

Hands-on Labs

●       PCA

●       Scaling

Chapter 5: Unsupervised Learning

Learning Objectives:

Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Topics:

●       What is Clustering & its Use Cases?

●       What is K-means Clustering?

●       How does K-means algorithm work?

●       How to do optimal clustering

●       What is C-means Clustering?

●       What is Hierarchical Clustering?

●       How Hierarchical Clustering works?

Hands-on Labs

●       Implementing K-means Clustering

●       Implementing C-means Clustering

●       Implementing Hierarchical Clustering

Chapter 6: Model Selection and Boosting

Learning Objectives:

In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.

Topics:

●       What is Model Selection?

●       The need for Model Selection

●       Cross-Validation

●       What is Boosting?

●       How Boosting Algorithms work?

●       Types of Boosting Algorithms

●       Adaptive Boosting

Hands-on Labs

●       Cross Validation

●       AdaBoost

Module Projects

Module 5: Natural Language processing

Duration 3 weeks

About the Course

This Python NLP course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the critical concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases f NLP in the python programming language to enhance your learning experience.

Why learn Natural Language Processing or NLP?

Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users.

NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.

Course Outline

Chapter 4: Introduction to Text Mining and NLP

Goal:

In this module, you will learn about text mining and the ways of extracting and reading data from some common file types including NLTK corpora

Topics:

●       Overview of Text Mining

●       Need of Text Mining

●       Natural Language Processing (NLP) in Text Mining

●       Applications of Text Mining

●       OS Module

●       Reading, Writing to text and word files

●       Setting the NLTK Environment

●       Accessing the NLTK Corpora

Hands-on Labs

●       No lab

Chapter 4: Extracting, Cleaning and Pre-processing Text

Learning Objectives:

This module will help you understand some ways of text extraction and cleaning using NLTK

Topics:

●       Tokenization

●       Frequency Distribution

●       Different Types of Tokenizers

●       Bigrams, Trigrams & Ngrams

●       Stemming

●       Lemmatization

●       Stopwords

●       POS Tagging

●       Named Entity Recognition

Hands-on Labs

●       No lab

Chapter 4: Analyzing Sentence Structure

Goal:

In this Module, you will learn how to analyze a sentence structure using a group of words to create phrases and sentences using NLP and the rules of English grammar

Topics:

●       Syntax Trees

●       Chunking

●       Chinking

●       Context Free Grammars (CFG)

●       Automating Text Paraphrasing

Hands-on Labs

●       No Lab

Chapter 4: Text Classification

Goal:

In this chapter, you will explore text classification, vectorization techniques and processing using scikit-learn.

Topics:

●       Machine Learning: Brush Up

●       Bag of Words

●       Count Vectorizer

●       Term Frequency (TF)

●       Inverse Document Frequency (IDF)

●       Converting text to features and labels

●       Multinomial Naive Bayes Classifier

●       Leveraging Confusion Matrix

Hands-on Labs

●       No Lab

Module Project

In this module, you will learn Sentiment Classification on Movie Rating Dataset

At the end of this module, you should be able to:

●       Implement all the text processing techniques starting with tokenization

●       Express your end-to-end work on Text Mining

●       Implement Machine Learning along with Text Processing

Data Science Capstone Project

Auto Insurance Case Study

Learning Objectives:

The capstone project will provide you with a business case. You will need to solve this by applying all the skills you’ve learned in the courses of the master’s program. This Capstone project will require you to apply the following skills

Data Exploration

●       Checking Data Size

●       Note the important features

Data Wrangling

●       Handling Imbalanced Data

●       MetaData Creation

●       Statistics on the Data

●       Identify Missing Variable

●       Rectify Missing Variable

●       One Hot Encoding

●       Scaling: Standard Scaler & Min Max Scaler

Data Exploration

●       Data Visualization

Machine Learning

●       PCA

●       Logistic Regression

●       Generating F1 Score Metric

●       Linear SVC Classifier

●       XG Boost Classifier

●       AdaBoost Classifier