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Python Certification Training for Data Science

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Python Certification Training for Data Science

Q: What if I miss a class?

You will never miss a lecture at edureka! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

Q: Who are the instructors?

All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

Q: What if I have more queries?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co

Have Queries? Ask us 1833 833 0587

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Edureka’s Data Science using Python programming certification course enables you to learn data science concepts from scratch. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science. Edureka's Python Certification Training course is also a gateway towards your Data Science career.

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60 days of Cloud Lab access

Why should you take Python?

Python is the preferred language for new technologies such as Data Science and Machine Learning.

Data Scientist has been named the best job in America for 2018 with median base salary of $242,000 and 4,524 job openings - Forbes

According to the TIOBE index, Python is one of the most popular programming languages in the world

Instructor-led Python live online classes

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Python Course Curriculum

Introduction to Python

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.

Topics:

Overview of Python
The Companies using Python
Different Applications where Python is used
Discuss Python Scripts on UNIX/Windows
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the screen

Hands On/Demo:

Creating “Hello World” code
Variables
Demonstrating Conditional Statements
Demonstrating Loops

Skills:

Fundamentals of Python programming

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Sequences and File Operations

Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

Topics:

Python files I/O Functions
Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Hands On/Demo:

Tuple - properties, related operations, compared with a list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Skills:

File Operations using Python
Working with data types of Python

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Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

Topics:

Functions
Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways
Errors and Exception Handling
Handling Multiple Exceptions

Hands On/Demo:

Functions - Syntax, Arguments, Keyword Arguments, Return Values
Lambda - Features, Syntax, Options, Compared with the Functions
Sorting - Sequences, Dictionaries, Limitations of Sorting
Errors and Exceptions - Types of Issues, Remediation
Packages and Module - Modules, Import Options, sys Path

Skills:

Error and Exception management in Python
Working with functions in Python

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Introduction to NumPy, Pandas and Matplotlib

Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.

Topics:

NumPy - arrays
Operations on arrays
Indexing slicing and iterating
Reading and writing arrays on files
Pandas - data structures & index operations
Reading and Writing data from Excel/CSV formats into Pandas
matplotlib library
Grids, axes, plots
Markers, colours, fonts and styling
Types of plots - bar graphs, pie charts, histograms
Contour plots

Hands On/Demo:

NumPy library- Creating NumPy array, operations performed on NumPy array
Pandas library- Creating series and dataframes, Importing and exporting data
Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

Skills:

Probability Distributions in Python
Python for Data Visualization

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Data Manipulation

Learning Objective: Through this Module, you will understand in detail about Data Manipulation

Topics:

Basic Functionalities of a data object
Merging of Data objects
Concatenation of data objects
Types of Joins on data objects
Exploring a Dataset
Analysing a dataset

Hands On/Demo:

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
GroupBy operations
Aggregation
Concatenation
Merging
Joining

Skills:

Python in Data Manipulation

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Introduction to Machine Learning with Python

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.

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
Gradient descent

Hands On/Demo:

Linear Regression – Boston Dataset

Skills:

Machine Learning concepts
Machine Learning types
Linear Regression Implementation

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Supervised Learning - I

Learning Objectives: In this module, 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?

Hands On/Demo:

Implementation of Logistic regression
Decision tree
Random forest

Skills:

Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output

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Dimensionality Reduction

Learning Objectives: In this 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 LDA model.

Topics:

Introduction to Dimensionality
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
LDA

Hands-On/Demo:

PCA
Scaling

Skills:

Implementing Dimensionality Reduction Technique

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Supervised Learning - II

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

Topics:

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/Demo:

Implementation of Naïve Bayes, SVM

Skills:

Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output

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Unsupervised Learning

Learning Objectives: In this module, you will 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/Demo:

Implementing K-means Clustering
Implementing Hierarchical Clustering

Skills:

Unsupervised Learning
Implementation of Clustering – various types

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Association Rules Mining and Recommendation Systems

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.

Topics:

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How does Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering

Hands-On/Demo:

Apriori Algorithm
Market Basket Analysis

Skills:

Data Mining using python
Recommender Systems using python

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Reinforcement Learning

Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.

Topics:

What is Reinforcement Learning
Why Reinforcement Learning
Elements of Reinforcement Learning
Exploration vs Exploitation dilemma
Epsilon Greedy Algorithm
Markov Decision Process (MDP)
Q values and V values
Q – Learning
α values

Hands-On/Demo:

Calculating Reward
Discounted Reward
Calculating Optimal quantities
Implementing Q Learning
Setting up an Optimal Action

Skills:

Implement Reinforcement Learning using python
Developing Q Learning model in python

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Time Series Analysis

Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.

Topics:

What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity
ACF & PACF

Hands on/Demo:

Checking Stationarity
Converting a non-stationary data to stationary
Implementing Dickey-Fuller Test
Plot ACF and PACF
Generating the ARIMA plot
TSA Forecasting

Skills:

TSA in Python

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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/Demo:

Cross-Validation
AdaBoost

Skills:

Model Selection
Boosting algorithm using python

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Python Course Description

About the Python Certification Course

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.

Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.

Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.

Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.

Why Learn Python?

It's continued to be a favourite 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 runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.

What are the objectives of our Python Certification Course?

After completing this Data Science Certification training, you will be able to:

Programmatically download and analyze data
Learn techniques to deal with different types of data – ordinal, categorical, encoding
Learn data visualization
Using I python notebooks, master the art of presenting step by step data analysis
Gain insight into the 'Roles' played by a Machine Learning Engineer
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and its related concepts
Perform Text Mining and Sentimental analysis
Gain expertise to handle business in future, living the present

Who should go for this Python Data Science Certification Course?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

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

What are the prerequisites for this Python Course?

The pre-requisites for edureka's Python course include the basic understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the course.

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Module 1: Introduction to Python

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Python for Data Science Certification

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Reviews

5000 Total number of reviews

4.57 Aggregate review score

80% Course completion rate

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Kirti Sutar

Before enrolling to the course, we can ask 'n' number of doubts & I ensure you will get apt answers from the staff. Another thing which I liked about Edureka is, the batches are available in very convenient time slots. For Example : If one can devote daily 1-2 hours, can go for a daily batch. Never the less, professionals can go for a weekend batch. Also trainers are with excellent background from industry & contain good amount of experience. All the queries & doubts asked during as well as after the session are clarified, which helps to understand the concept in much deeper way.

Sudhiranjan Sarma Analytics and Information management, Cognizant

My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheerÃ¢â¬Â¦

Baskar Raj

Python & Kafka was excellent by Gyanender Verma. All the concepts was covered with no compromise. Instructor was very well determined and Focussed with Clear Examples. This reduces my effort of reading the books and can start working immediately in the ongoing projects.

Eric Arnaud PhD candidate in computer engineering speciality applied cryptography at Korea University of Technology and Education

I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop and after that since I'm still hungry I will take the Python class and so on because for me Edureka is the place to learn, people are really kind, every question receives the right answer. Thank you Edureka to make me a Data Scientist.

Sairaam Varadarajan Senior Data Scientist at Dexcom

I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka!". Instructors are knowledgeable and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry.

Uma Uppin Data Engineer, Facebook

I took courses on Big data and Python and found them very useful. Some of the instructors focused on getting the core concepts right which .Customer service is excellent and very responsive.

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Python Course Project

How will I execute practical’s in Edureka's Python Certification Course?

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

What is CloudLab?

CloudLab is a cloud-based Jupyter Notebook which is pre-installed with Python packages on the cloud-lab environment. It is offered by Edureka as a part of Python Certification Course where you can execute all the in-class demos and work on real-life projects in a fluent manner.

You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

Which case studies will be a part of this Python Certification Course ?

This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are few case studies, which are part of this course:

Case Study 1: Maple Leaves Ltd is a start-up company which makes herbs from different types of plants and its leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of plant family. They have asked us to automate this process and remove any manual intervention from this process.

You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference.

Case Study 2: BookRent is the largest online and offline book rental chain in India. The company charges a fixed fee per month plus rental per book. So, the company makes more money when user rents more books.

You as an ML expert and must model recommendation engine so that user gets a recommendation of books based on the behavior of similar users. This will ensure that users are renting books based on their individual taste.

The company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User Based Vs Item Based

Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with random forest classifier.

Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy.

Case Study 4: Principal component analysis using scikit learn.

Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy.

Using scikit learn perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label.

Case Study 5: Read the datafile “letterCG.data” and set all the numerical attributes as features. Split the data in to train and test sets.

Fit a sequence of AdaBoostClassifier with varying number of weak learners ranging from 1 to 16, keeping the max_depth as 1. Plot the accuracy on the test set against the number of weak learners, using decision tree classifier as the base classifier.

Which kind of projects will be a part of this Python Certification Course?

Project #1:

Industry: Social Media

Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters.

Actions to be performed:

Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest.

Project #2:

Industry: FMCG

Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.

Actions to be performed:

You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components which explain the max variance.

View project details

Python Training Features

Instructor-led Live Sessions

42 Hours of Online Live Instructor-led Classes. Weekend class: 14 sessions of 3 hours each and Weekday class : 21 sessions of 2 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of Data Science with Python.

Assignments

Every class will be followed by practical assignments which aggregates to minimum 60 hours.

Lifetime Access

Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.

24 x 7 Expert Support

Lifetime access to our 24x7 online support team who will resolve all your technical queries, through ticket based tracking system.

Certification

Towards the end of the course, you will be working on a project. Edureka certifies you as a Data Scientist with proficiency in Python based on the project.

Forum

Access to the global community forum for all our users that further facilitates learning through peer interaction and knowledge sharing.

Python Training FAQs

What if I miss a class?

You will never miss a lecture at edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.

Who are the instructors?

All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

What if I have more queries?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co

Read course FAQ

More about Python

How to become a certified Python programmer?

Edureka’s Python Certification training will help you in getting certified as a Python developer and get relevant jobs in this field. Our instructor led sessions combined with industry relevant course materials will make sure that your knowledge is up to date.

How to learn Python easily?

A structured way of learning would make understanding Python a lot easier. We have curated an exhaustive list of blogs and tutorials that will help you get started with Python language. After this, our Mastering Python certification training will help you completely master the language and launch your career in it.

What are few Python project ideas which even beginners can try?

Python is easy and simple to learn. With dedication, anyone can learn Python from scratch and use it to create interesting projects as well. Some example of simple Python projects for beginners to work on includes: Calculator, Web Scraping and experimenting with Youtube_DL.

What is average salary of a Python developer?

On an average Python developers earn anywhere between $80,000 to $130,000. This number varies depending on the project and the experience held.

Why learn Python? What are its benefits?

Learning Python can be highly beneficial for any IT professional. Python developers are in high demand in the job market. From web design to machine learning, Python has reach in many different verticals, which makes it an easy choice for beginners to learn and master.

How much time do I need to learn Python to get a job?

Python as a programming language is widely used across various domains. To truly master the language, we offer a 6 weeks training program which is followed by live projects for our learners to work on. These 6 weeks of training is enough to understand the concepts. After that, the more you practice, the better you get.

How should beginners get started with learning Python?

Anyone looking to start out with a career in IT or grow further in it can think about learning Python. For beginners, we have compiled an extensive list of blogs and video tutorials on Youtube that will help you get started. Once you are done with the basic concepts, you can think about taking up our Certification program and mastering Python.

How to learn Python online? How is it better than offline training?

Learning pedagogy has evolved a lot with the advent of technology. These changes and advancements have made it possible to increase your efficiency while you learn. While the traditional classroom based training has proven to be successful, with online learning learners have flexibility in terms of schedule. Apart from this, they can visit the study material anytime from anywhere and brush up on concepts with ease. Learning does not stop once the classes are over, which is why we also provide a 24x7 support system to help you with your doubts even after your class ends.

What are few Python career opportunities and paths I can explore once I learn Python?

Learning Python can help open up lots of opportunities for you. As a programming language, Python is widely used in many various industries. From web Designing to Scrapping, from Machine Learning to Data Science. Python developers are in huge demand in the job market and learning Python can help you apply to many such industries.

What are the skills needed for a Python developer?

Learning Python does not require you to have any pre requisite skill-set. Anyone with a strong logical thinking and willingness to learn can master this programming language. This is largely down to the ease and simplicity that Python offers. A skilled Python developer should have in-depth knowledge and practical experience in one or more of the various frameworks in Python.

Why should beginners get started with learning Python?

Python is one of the programming languages that are in great demand in the job market. With its ease of use and simplicity, Python is being widely implemented in various industries. Learning Python will help open up a lot of opportunities for developers looking for a change or anyone looking to get started with a career in IT.

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Structure your learning and get a certificate to prove it.

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Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.

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Python Certification Training for Data Science

Why should you take Python?

Instructor-led Python live online classes

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Python Course Curriculum

Introduction to Python

Sequences and File Operations

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Introduction to NumPy, Pandas and Matplotlib

Data Manipulation

Introduction to Machine Learning with Python

Supervised Learning - I

Dimensionality Reduction

Supervised Learning - II

Unsupervised Learning

Association Rules Mining and Recommendation Systems

Reinforcement Learning

Time Series Analysis

Model Selection and Boosting

Python Course Description

About the Python Certification Course

Why Learn Python?

What are the objectives of our Python Certification Course?

Who should go for this Python Data Science Certification Course?

What are the prerequisites for this Python Course?

Python for Data Science Certification

Reviews

Python Course Project

How will I execute practical’s in Edureka's Python Certification Course?

What is CloudLab?

Which case studies will be a part of this Python Certification Course ?

Which kind of projects will be a part of this Python Certification Course?

Python Training Features

Instructor-led Live Sessions

Real-life Case Studies

Assignments

Lifetime Access

24 x 7 Expert Support

Certification

Forum

Python Training FAQs

What if I miss a class?

Will I get placement assistance?

Can I attend a demo session before enrollment?

Who are the instructors?

What if I have more queries?

More about Python

How to become a certified Python programmer?

How to learn Python easily?

What are few Python project ideas which even beginners can try?

What is average salary of a Python developer?

Why learn Python? What are its benefits?

How much time do I need to learn Python to get a job?

How should beginners get started with learning Python?

How to learn Python online? How is it better than offline training?

What are few Python career opportunities and paths I can explore once I learn Python?

What are the skills needed for a Python developer?

Why should beginners get started with learning Python?

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