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With Eduranz Online Python for Data Science Course, you can learn & master the concepts of Python along with Data science from scratch to the advanced level. This Python for Data Science Certification will also help you master important Python programming concepts such as data types, data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, Matplotlib, which are important for data science. Moreover, this Data Science with Python Course will also cover the topics in Machine Learning with essential concepts like Supervised Learning, Unsupervised Learning and a lot more. Python for Data Science Certification Training is also a gateway to the Data Science and Machine Learning careers, currently the highest paying jobs.

Course Syllabus

Section 1: Introduction
Introduction to Software Training FREE 00:40:00
Object Oriented Design Patterns 00:35:00
Software Testing 00:30:00
Section 2: Advanced Computing
Introduction to Azure Data factory FREE 00:25:00
Algorithm analysis 00:45:00
Multi Threading in Softwares 00:40:00
Managing Software Testing 00:20:00
Lecture 1 – Python Programing Demo 00:07:00

About Course

With Eduranz Python for Data Science Certification Training, you can learn & master the concepts of Python and Data Science from the very scratch to the advanced level. This Data Science with Python Training will help you grasp all the essential concepts in python from understanding data types to OOPs, from extracting data to analyzing it and visualizing it. You will also understand the concepts of Machine learning and be performing hands-on with different machine learning algorithms like Supervised Learning and Unsupervised Learning.

• In this Data Science with Python Course, you will learn about python objects and data types
• Python statements, control flow, and functions
• Working with Object-Oriented Programming concepts in Python (OOP concept)
• Modules, packages, errors, and exception handling in Python
• Decorators and generators in python
• Mathematical computation using NumPy
• Scientific computing using SciPy
• Data manipulation using Pandas
• Data visualization using MatplotLib and Seaborn
• Machine learning with Python


• Freshers or Graduates
• Anyone willing to have a career in Python
• If you are working in different programming language (regardless of your experience)
• Data Analysts or Business Analysts
• BI Managers or Analytics Professionals
• Application or Web Developers
• Software Engineers or ETL Developers
• Big Data Analysts


No prerequisites are required for this Python for Data Science Certification, but the basics of any programming language will come in handy for you.


• Python is the most versatile, flexible and user-friendly language.
• No language has every made this level of transition for becoming this popular and majorly used for almost every domain like Data Analysis, Data Visualization, Machine Learning, Data Science, Finance, Web Development and a lot more.
• Analytics India Magazine reported that the landscape of Data Science is projected to double its size by the year of 2025 (in 2019 it was 3.03 billion). So, this is surely the right time to make a career in Data Science.
• Data Scientists are the highest paying professionals in the industry.
• This course is designed and structured by industry experts, based on industry requirements. Upon completion of the program an industry recognized certificate is awarded. To learn more, check out the feature section.


According to Indeed, average salary of a Data Scientist is Rs. 8,13,533 per year, in India, which ranges from Rs. 1,46,000 to Rs. 20,43,000 per year. On the other hand, in the US it is $124,220 per year

Eduranz follows a rigorous certification process. To become a certified Data Scientist, you must meet the following criteria:

Online Instructor-led Course

  • Successful completion of all projects, which will be evaluated by trainers
  • Scoring minimum 60% in the Data Science with Python Certification Training quiz conducted by Eduranz

Self-paced Course

  • Completing all course videos in our LMS
  • Scoring minimum 60% in the Python for Data Science Course quiz conducted by Eduranz


Data Scientists are responsible for extracting meaningful insights from data and interpret them to drive different goals. It requires expertise in tools and concepts from statistics to machine learning. The work cycle of a Data Scientist revolves around collecting, cleaning, and muging data before being able to process further


• Google
• Amazon
• Microsoft
• Walmart
• Procter & Gamble
• Infosys Technology Limited
• Springer Nature (Source: Indeed) and a lot more.

Syllabus

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

Lab-Exercise:
• 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

Lab-Exercise:
• 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

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

Lab-Exercise:
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

Here, we will cover all the 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

 

Lab-Exercise:

  • Write a Class
  • Writing a Python program and incorporating the OOP concepts in it
  • Creating a Bank Account using OOP concepts

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

Lab-Exercise:
• 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

 

Lab-Exercise:

  • Demo on Modules
  • Demo on Exception Handling
  • Running Tests with Unittest Library

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

Lab-Exercise:
• Demonstration on Decorators and Generators

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 Transportation
• Universal Array Function
• Array Processing
• Array Input and Output

Lab-Exercise:
• Importing NumPy Modules
• Creating and Initializing NumPy Arrays of different dimensions
• Working with arange in NumPy arrays
• Perform arithmetic operation on NumPy Arrays
• Create 3 Dimensional NumPy array

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.

Lab-Exercise:
• Working with SciPy Cluster and Lining
• Import SciPy by applying the Bayes phrase to the specified notes.

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

Lab-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:
• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting

Also, we will draw and understand charts and diagrams using these libraries.
Lab-Exercise:
• Using Matplotlib to create pie charts, scatter plots, line graphs, and histograms
• Create Graphs and Charts using different libraries

Learn how to create beautiful and interactive data visualizations in Python using various libraries such as:
• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
Also, we will draw understand charts and diagrams using these libraries

Lab-Exercise:
• Using MatPlotLib to create pie charts, scatter plots, line graphs, and histograms
• Create Graphs and Charts using different libraries

Learn how to do the scraping through the websites efficiently:
• Introduction to web scraping in Python
• Various web scraping libraries
• beautifulsoup, Scrapy Python packages,
• Installation of beautifulsoup
• Installation of Python parser lxml
• Creating soup object with input HTML
• Searching of tree
• Full or partial parsing
• Output print
• Searching the tree

Hands-on Exercise:
• Installation of Beautiful soup and lxml Python parser
• Making a soup object with input HTML file
• Navigating using Py objects in soup tree


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)

Lab-Exercise:
• Working with
• Using SciKit Library with Random Forest algorithm for implementing Supervised Learning

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:
• Supervised Learning
• Unsupervised Learning

Hands-on Exercise:
• 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)
Hands-on Exercise:
• Working with Classification and Regression Algorithms
• Using SciKit Library with Random Forest algorithm for implementing Supervised Learning
• Xgboost

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,
Hands-on Exercise:
• 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

Projects

Project 1: Analyzing the Election Data
Problem Statement: Extract, load and Analyze the Poll from the Election and Donor Data
Industry: Government and Public
Description: Post the election, government has given your company a contract of doing the analysis on the Election and Donor Data. You as the data analyst are supposed to answer to a few questions by analyzing the aggregated poll data. Like how much votes are done and different aspects in it along with analyzing the average donations given to Democrats and Republican (more questions are asked during the project).
Topics Covered:
Concepts used in this project are as follows:
• Basic Python Data Types, Operations, Methods and Functions
• Python Data Analysis Libraries:
o NumpPy Array
o Pandas
• Data Visualization using any:
o Pandas
o Matplotlib
o Seaborn
o Cufflinks

Project 2: Analyzing the Stock Market
Problem Statement: Extract, Load and Analyze the Stock Market data, get the stock information, visualize the data and predict its future from the dataset.
Industry: Stock Market
Description: Andrew is a Data Analyst in a company named ValueAnalytics, he has been assigned a project to analyze the Stock Market from a data set of Technology Stocks, by using the different libraries, he has to extract the stock information and perform the visualization of different aspects, along with analyzing the risk of a stock from its past history.
Andrew need to perform the tasks in this format:
• Find the change in the Price of the stock over time
• Find the daily return of the stock on average
• Find the moving average of the various stocks
• Find the correlation between different stocks’ closing prices
• Find the correlation between different stocks’ daily returns
• Find the value we should put at risk by investing in a particular stock
• Attempt to predict future stock behavior
Topics Covered:
Concepts used in this project are as follows:
• Basic Python Data Types, Operations, Methods and Functions
• Python Data Analysis Libraries:
o NumpPy Array
o Pandas
• Data Visualization using any:
o Pandas
o Matplotlib
o Seaborn
o Cufflinks

Project 3: Twitter Sentiment Analysis
Problem Statement: As you are holding the position of Data Scientist in your current organization, you need to build a model to categorize words based on sentiments. This model should tell whether words detected are positive or negative.
Industry: Internet and Social Media
Description: Use the various Machine Learning models to analyze the words whether they are negative words or the positive words, using the sentiment analysis algorithm, build a model describing which word is negative and which one is positive.
Topic:
• Data Analysis
• Machine Learning
• Sentiment analysis
• Social Media Monitoring

Reviews

Amit Soni

Business Development Manager - Qatar Computer Services

Highly advisable to opt one

This course on Python will leave one owning all the information that is required to shape up and grow. From the Instructor to the support team has always made sure that every individual gets the entire volume of the subject. Structure of the course and its contents are perhaps well up to the industry- level requirements and highly advisable to opt one.

Rahul Pohankar

Senior Manager

Great learning experience

The Python course at Eduranz was better than any real-time classes I ever attended. The faculty was wonderful and always available. The classes were practical oriented and it has been a great learning experience.Thank you Eduranz Team!

Manoj Gera

Data Architect - Webster Bank

Thanks for Eduranz and their team!

I have been part of Eduranz family more than a year and I appreciate all the effort that eduranz make for bringing the required courses to make learning easier with affordable prices. I have been part of Python Programming, Python for Data Science. The courses were brilliantly designed to make the learners understand and gain confidence on what they are learning for. I got more than what I am looking for and gathered so much of information and confidence which makes me proceed in the direction which I wished to. Thanks for Eduranz and their team!!!

Ljiljana Spasovic Botha

Business Development Officer - SASLO

Great courses

Had a great learning session where the concepts are clear to understand and can solve the given assignments easily.

Lakhi Baug

VP - Gemalto

Awesome for building professional competence

Awesome platform to learn new skills which could be new languages or IT skills or marketing. There are hundreds of courses waiting for discovery by you.

I like Eduranz because it gives me a chance to get a certificate after completing a course. Then I can share it on social media and for example, apply for a better job. It helps a bit. Eduranz is awesome for building professional competence in the job market nowadays.

Certification

Features

Personal Mentor


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.

Lifetime Access


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.

Certification


Become a Certified Professional.

Job Assistance


Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.

FAQs

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.

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