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Key Features



About The Program

  1. Importance and introduction to Python and its installation
  2. Study OOP, expressions, data types, looping, etc.
  3. functions, operations and class defining
  4. Get to know the Machine Learning algorithms in Python
  5. Course completion certificate from Deepneuron
  6. Be equipped for the Python professional certification
  • BI Managers and Project Managers
  • Software Developers and ETL Professionals
  • Analytics Professionals
  • Big Data Professionals
  • Those who wish to have a career in Python
  • You don’t need any specific knowledge to learn Python. A basic knowledge of programming can help you.
Deepneuron follows a rigorous certification process. To become a certified Python Programmer,
you must fulfill the following criteria:

  • Online Instructor-led Course
  • Successful completion of all projects, which will be evaluated by trainers
  • Scoring a minimum of 60 percent in the Python quiz conducted by Deepneuron
  • Self-paced Course
  • Completing all the course videos in our LMS
  • Scoring a minimum of 60 percent in the Python quiz conducted by Deepneuron

Data Science With Python Course Modules

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.


Overview of Python
The Companies using Python/div>
Different Applications where Python is used Windows
Discuss Python Scripts on UNIX/Windows
Values, Types, Variables
Operands and Expressions
Lists, Ranges & Tuples in Python
Conditional Statements
Python Dictionaries and Sets
Command Line Arguments
Writing to the screen

Hands On/Demo:

Creating “Hello World" code
Demonstrating Conditional Statements
Demonstrating Loops


Fundamentals of Python programming

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.


Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts/div>
Standard Libraries
Modules Used in Python
The Import Statements
Slicing with Negative Numbers
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


Error and Exception management in Python
Working with functions in Python

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


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
Using and, or, in Conditions


Python in Data Manipulation

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


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


Machine Learning concepts
Machine Learning types
Linear Regression Implementation

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


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


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

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.


Introduction to Dimensionality
Why Dimensionality Reduction
Factor Analysis
Scaling dimensional model




Implementing Dimensionality Reduction Technique

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


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


Implementation of Naïve Bayes, SVM


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

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.


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?


Implementing K-means Clustering
Implementing Hierarchical Clustering


Unsupervised Learning
Implementation of Clustering - various types

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


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


Apriori Algorithm
Market Basket Analysis


Data Mining using python
Recommender Systems using python

Introduction to end to end learning
Abstractions of features using deep layers
Hyper parameter tuning
Regularization for Deep Learning
TensorFlow Installation
Tensorflow Installation 2.0
Tensorflow Installation 1.6 with virtual environment
TensorFlow 2.0 function
Tensorflow 2.0 neural network creation
Tensorflow 1.6 functions
Tensorflow 1.6 neural network and its functions
Keras Introduction
Keras in-depth with neural network creation
Mini project in Tensorflow

Introduction to NLP
Stop Words o Tokenization
Stemming and lemmatization
Bag of Words Model
Word Vectorizer
POS Tagging
Named Entity Recognition
Introduction to Sequential data
RNNs and its mechanisms
Vanishing & Exploding gradients in RNNS
LSTMs - Long short-term memory
GRUS - Gated recurrent unit
LSTMs Applications o Time series analysis
LSTMs with attention mechanism

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.


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

Hands on/Demo:

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


TSA in Python

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.


What is Model Selection?
The need for Model Selection
What is Boosting?
How Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting

Hands on/Demo:



Model Selection
Boosting algorithm using python

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


Python files I/O Functions
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


File Operations using Python
Working with data types of Python

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.


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


Probability Distributions in Python
Python for Data Visualization


Earn your certificate

Our Python program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Data Science with Python Certification Training Course Certificate

The knowledge and Python skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.

Data Scientist

DeepNeuron Testimonials


  • What is the objective of our Python Regular Certification?

    The objective of the CKA training is to provide extensive coverage on the design and deployment of containers and orchestral tools for applications with special references to Python container orchestration tools. With Deepneuron Certified Python Administrator course, we ensure hands-on practical experience on creating containers files for Python which will help you to master Python with confidence.

  • Why should I learn Python from Deepneuron?

    Deepneuron Python certification is for freshers, experienced individuals who are ready to dive for a real-time case study based industry-specific carriculum specialized curated from professional subject matter experts (SMEs). At Deepneuron, you will explore live projects based on conceptual learnings with details explanations for each and every module for upscaling your current career statistics with advancement.

  • What can I expect after the Python Course accomplishment?

    After the completion of the Python online course, you will gain expert knowledge in Kubernetes and will be a proficient player to tap Pythontools at a depth level. Moreover, you will be part of the Deepneuron community to leverage the knowledge-base shared by the members around the globe. You will also earn an industry-recognized Python certification from Deepneuron.

  • Does Deepneuron offer job assistance?

    Deepneuron actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

  • Is it possible to switch from self-paced training to instructor-led training?

    You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

  • Does the job assistance program guarantee me a Job?

    Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

  • *For which all courses will I get certificates from IBM?

    Following are the list of courses for which you will get IBM certificates:

    • R Programming for Data Science
    • Python for Data Science

  • How do I earn the Master’s certificate?

    Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.


    Course completion certificate

    Data Science and Statistics Fundamentals Required 85% of Online Self-paced completion
    Data Science with R Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with SAS Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project
    Data Science with Python Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project
    Machine Learning and Tableau Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project
    Big Data Hadoop and Spark Developer Required 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project
    Capstone Project Required Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

  • How do I enroll for the Data Scientist course?

    You can enroll in this Data Science training on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • I am not able to access the online Data Science courses. Who can help me?

    Yes, we do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support. portal.

  • Who are the instructors and how are they selected?

    All of our highly qualified Data Science trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

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