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|Section 1: Introduction|
|Introduction to Software Training||FREE||00:40:00|
|Object Oriented Design Patterns||00:35:00|
|Section 2: Advanced Computing|
|Introduction to Azure Data factory||FREE||00:25:00|
|Multi Threading in Softwares||00:40:00|
|Managing Software Testing||00:20:00|
|Lecture 1 – Python Programing Demo||00:07:00|
Python is one of the leading, flexible, and powerful open source languages that is easy to learn and use, and offers a powerful library for data manipulation and analysis. For more than a decade, Python has been used in scientific computing and high-volume fields such as finance, oil and gas, physics and signal processing.
Eduranz’s Python for Data Science Course not only focuses on the basics of Python, statistics, and machine learning but also helps to gain experience in applied data science at the Python scale. This Data Science with Python training is a step by step guide for Python with Data Science with broad hands. This Python for Data Science course is filled with a variety of problem and activity scenarios and assignments that will allow you to gain direct experience in dealing with predictive modeling problems that require or require machine learning in Python. From statistical basics such as center, and mode to explore functions such as data analysis, regression, classification, grouping, Naive Bayes, cross-validation, label coding, random greening, decision making, and support vector machines with examples and supporting practices.
In addition, you will learn to improve machine learning, which is an important aspect of artificial intelligence. You can train your machine using real-world scenarios using machine learning algorithms.
The Python for Data Science course also covers basic and advanced Python concepts such as writing Python scripts, sorting, and operating Python files. They use libraries like Pandas, Numpy, Matplotlib, Scikit, and main concepts such as Python Machine Learning, scripts, and sequencing.
- Programmers, Developers, Technical Leads, Architects
- Developers aspiring to be a ‘Machine Learning Engineer’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘Python’ professionals who want to design automatic predictive models
The pre-requisites for Eduranz Python for Data Science course require the basic understanding of Computer Programming Languages. Proficiency in any programming language will come in handy for you.
In this Chapter, we will start from the very basic and understand the relevance of python followed by its setup, syntax, basic commands and a lot more, check below to know more:
• Introduction to Python
• Features of Python
• Advantages of using Python compared to other programming languages
• Types of Companies using Python
• Installation of Python on Windows, Mac and Linux distributions for Anaconda Python Deployment of Python IDE
• Basic commands of Python
• Installing Python Anaconda for Windows, Linux, and Mac
• Writing a “Hello World Program”
This Chapter will cover all the concepts in Data Types and Objects with their respective hands-on and practical sessions, you will learn how to work with data types, number, strings, lists, tuples and dictionaries, you can check the below topics to know more:
• Understanding Python Data Types and Numbers
• Performing Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
o Understanding all the basic operators in Python
o Comparison Operators in Python
o Chaining Comparison Operators with Logical Operators
• Working with strings like indexing and slicing a string, formatting, etc
• Working with Lists like creating a basic and multiple list, adding elements & more
• Covering the essential concepts of Tuples
• Sets & Boolean in Python
• Dictionaries in python like creating and adding elements to a Dictionary
• Adding, Subtracting, Multiplying and Dividing numbers using arithmetic operations
• Creating a list with multiple distinct and duplicate elements
• Accessing and removing the elements from a list
• Slicing a list
• Creation and Concatenation of Tuples
• Slicing of Tuples
• Demonstration of Set and Boolean operations
• Demonstration on Python Dictionaries
Diving deep into Data Structure & Data Types
- Data frames
- Importing Data from various sources
- Database Input: Connecting to database
- Exporting Data to various formats
- Viewing Data: Viewing partial data and full data
- Variable & Value Labels: Date Values
Case study: We will go through a Case Study on HR Analytics
In this Python Chapter, we will go through all the basic concepts of Python Statements like if else, for loops, while loops, Control flow and all the essential Functions in Python, check the below topics to know more about this chapter:
• If Elif and Else Statements
• For loops in Python
• While loops & some useful operators in Python (range vs xrange on python)
• List Comprehensions in Python
• Chaining comparison in python
• Else with for and Switch Case in Python
• Using iteration in python
• Iterators in Python
• Iterators function
• Intro to Python functions
o Types of Python Functions
• Defining a Function in Python
o Rules for naming python function (identifier)
• Python Function Parameters
• Python Return Statement and calling a function
• Function Arguments
• What is the Python Function Argument?
o Types of Python Function Arguments
o Default Argument in Python
o Python Keyword Arguments
o Python Arbitrary Arguments
• Python Built-In Functions with Syntax and Examples
• Lambda Expressions, Map, and Filter Functions
Along with covering basics to advanced topics in this chapter, we will also cover the Lab-exercises and practical for all the above-mentioned topics based on an industrial-based case studies such as:
• Write a Python Function with or without the parameters
• Demo on If Else Statements and Iterators Functions
• Demo on Simple Boolean and Simple Math Functions
• Demo on create an object and write a for loop to print all odd numbers
• Demo on smaller or a greater number
• Use Lambda Expression to Map and Filter the Functions
essential concepts of OOP, where in we will start from basics to the advanced level while able to write smooth codes using the concepts of OOP, we will cover the topics like:
• Intro to Object-Oriented Programming and its need
• Attributes, Class Keywords,
• Class Object Attributes
• Methods in Python
• Data Hiding and Object Printing
• Constructors and Destructors in Python
• Class and static variable in python
• Class method and static method in python
• Inheritance, Encapsulation, Polymorphism & Abstraction
• Special Methods – Magic Method
• Write a Class
• Writing a Python program and incorporating the OOP concepts in it
Creating a Bank Account using OOP concepts
In this part of the chapter, we will understand the concepts of Modules, Packages and deep-dive into some of the common errors in Python, along with the concepts of Exception Handling, topics as shown below:
• Intro to Modules
• Working on PyPi using pip Install: Installing external packages and modules
• Numeric, Logarithmic, Power, Trigonometric and Angular functions in Python
• Understanding Python Errors and Exceptions
• Syntax Errors in Python
• Handling Exceptions in Python
• Raising Exceptions
• User-defined Exceptions
• Unit Testing in Python
• Demo on Modules
• Demo on Exception Handling
• Running Tests with Unittest Library
- Understanding more about Analytics World
- Data Science Vs Data Analytics Vs Machine Learning Vs Artificial Intelligence Vs Business Analysis
- Analytics keywords and their definitions
- Business Objectives
- Key driving factors in Analytics world
Case-Study: Case Study on how Predictive Analysis is helpful for Sales Industry
Chapter where you will learn all the essential topics of Decorators and Generators in Python such as:
• Understanding Decorators in Python
• Syntax of Decorators and Working with them
• Understanding Generators in Python
• Working with Generators
• Demonstration on Decorators and Generators
- Performing Data Preparation steps
- Outlier treatment
- Flat Liners
- Missing values
- Dummy Creation
- Variable Reduction
- Data Alignment and fine tuning
Upon the completion of Python concepts successfully, we will proceed towards the advanced concepts in Python, starting off with NumPy, a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on arrays.
This Chapter will cover the topics as shown below:
• Intro to NumPy
• Creating Arrays in NumPy
• Using Arrays and Scalars
• Indexing NumPy Arrays
• Numpy Array Manipulation
• Array Transpotation
• Universal Array Function
• Array Processing
• Array Input and Output
• Importing NumPy Modules
• Creating and Initializing NumPy Arrays of different dimensions
• Working with arange in Numpy arrays
• Perform arithematic operation on NumPy Arrays
• Create 3 Dimensional NumPy array
Scientific computing using SciPy
In this Chapter, we will learn Python’s very reliable library used for Scientific and technical computing, which is SciPy, we will cover the concepts as shown below:
• Introduction to SciPy
• Its Functions Building NumPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages and SciPy with Bayesian Theory.
• Working with SciPy Cluster and Lining
• Import SciPy by applying the Bayes phrase to the specified notes.
Implementing Decision Tree model in R
This Chapter will cover Pandas, a Python library used for the Data Manipulation, the topics to be covered are as shown below:
• What is data manipulation?
• Use panda libraries to manipulate data
• Dependency of NumPy library libraries
• Pandas Series objects,
• Panda data frames
• Load and process data with pandas
• Combining data objects
• Merging, and various types of data object attachments.
• Record & clean notes, edit notes, visualize notes
Hands-on Exercise – Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.
Learn how to create beautiful and interactive data visualizations in Python using various libraries such as:
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
Also, we will draw understand charts and diagrams using these libraries
• Using MatPlotLib to create pie charts, scatter plots, line graphs, and histograms
• Create Graphs and Charts using different libraries
We will start off this chapter by revising the previous concepts in data analysis such as Pandas, MatplotLib, Numpy and SciPy. Then, we will move onto the topics shown below:
• Understanding of Machine Learning
• Understanding SciKit Learn
• Need of Machine Learning
• Types in Machine Learning
• Machine Learning Worklfow
• Understanding SciKit Learn!
• Machine Learning Use-Cases
• A brief understanding of various ML Algorithms:
o Supervised Learning
o Unsupervised Learning
• Working with Machine Learning Algorithms
While diving more into Machine Learning, in this chapter, we will understand one of its essential types that is Supervised Learning. We will cover the topics as shown below:
• What exactly is Supervised learning?
• Understanding the Classification and Regression Algorithms
• What is linear regression and how to do calculations in Linear Regression?
• Understanding Linear regression in Python
• Understanding Logistics regression
• Working with Supports vector machine
• xgboost (standalone step)
• Working with
• Using SciKit Library with Random Forest algorithm for implementing Supervised Learning
After learning the concepts of Supervised Learning, this chapter will cover the yet another type of Machine Learning, which is Unsupervised Learning, below are the topics which we are going to cover:
Introduction to Unsupervised Learning
Looking into the Use Cases of Unsupervised Learning Understanding Clustering,
Types of Clustering – Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
Understanding K-Means Clustering and its algorithm
Stepwise calculation of k-means algorithm
Running k-means with SciKit Library
Understanding association mining rule
Market basket analysis
Working with association rule mining measures covering support, trust, lift, and apriori Algorithm,
• Demo on Unsupervised Learning
• Demo on Algorithms in the SciKit Learn package for applying machine learning techniques, and training the network model
• Demo on Apriori
This is an advanced topic where we will integrate spark with python for performing cluster-computing. PySpark, a tool of apache, lightning-fast cluster computing technology, designed for fast computation)
Introduction to Pyspark with PySpark, need for a Python Spark, Fundamentals of PySark, Pyspark in Industry, Installing PySpark, Fundamentals of PySpark, Excellence Mapreduce, Use of PySpark Demo and PySpark.
Hand Exercise: Demonstration of contours and conditional statements related to tuple operations, properties, lists, etc., List operations, related properties, set properties, related operations, dictionary operations, related properties
Project 1: Analysis of the Python Name Template
Challenge: How to analyze trends and the most popular baby names
Topic: In this Python project, you are working with the United States Social Security Administration (SSA), which provides data on the frequency of baby names from 1880 to 2016. This project requires data analysis by considering various methods. You will see the most common names, identify name trends, and find the most popular names for a particular year.
Data analysis with the Pandas Library
Provide data frame manipulation
Land for bars and boxes with Matplotlib
Project 2: – Python Web Scraping for Science
In this project, you will be introduced to the web crawling process in Python. This includes installing Beautiful Soup, scraping libraries on the Web, editing general data and web page formats, exploring main object types, navigating strings, finding search trees, navigation options, Analyzer, Search Trees, and searching CSS. Class arguments, lists, functions, and keywords.
Project 3: Predict customer churn at telecommunications companies
Industry – Telecommunications
Task Issues – To maximize the profitability of your telecommunications company by reducing cooling rates
Topic: In this project, you work with telecommunications company customer records. This note contains telephone client subscription data. Each column contains a telephone number, call minutes at different times, fees paid, account life, and whether the customer has canceled some services by unsubscribing. The aim is to predict whether the customer will eventually swell or not.
Expand the Scikit-Learn ML library
Develop code with the Jupyter Notebook
Create a model using the execution matrix
At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.
24/7 Tech Adviser Support
Lifetime 24/7 Technical and Non Technical Support from team Eduranz.
Get Lifetime opportunity to access and attend the live sessions multiple times.
Assignments & Quizzes
Every module will be followed by certification based assessment and quiz.
Become a Certified Professional.
Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.
No one misses any lecture at Eduranz, because you will be provided with the recorded sessions of the class on your LMS withn 24 hours and despite that, you can also attend any different live session to cover up the missed topic and aks your doubts from the trainer.
Live Virtual Classes or Online Classes: With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time.Independent study online: In this mode, you will receive videos with lectures and can continue the course as you wish.
Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months of intensive industry experience.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.
Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing training. We also don’t limit the number of tickets you can collect when solving questions and doubts.
Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.
Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV.Is it possible to switch from independent training to teacher-led training?In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.
After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; You will receive an industrial recognized certificate by Eduranz. This certification is recognized by Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.
Our job assistance program will help you reach the job that you have been seeking. In our support program, we help you by sharing your resume with the companies that we have tie ups with, along with helping you in resume building, getting your prepared for the interviews through mock sessions by our industry experts (from various companies like IBM, Microsoft, Accenture, Delloite etc.), also by providing you with mock interview questions and exhaustive session by Eduranz. However, Eduranz is not a recruitment or job agency, we do not guarantee you a job, we simply direct your resumes to different companies, then after that, entire process is handled by the employer and company and the result is totally based on the employer’s decision.