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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

Skills Covered

Probability & Statistics

Machine Learning


Creating a data model

Data Manipulation

Visualizing data

OOPS in Python

Pandas, NumPy, & Scikit-Learn

Time Series Forecasting

Python Integration with Spark

Full Stack Data Science Course Modules

Python - Fundamentals

  • Installation
  • Python — Syntax
  • Python — Variables and Datatypes
  • Python — Numbers
  • Strings
  • Sequences
  • List
  • Tuples
  • Ranges
  • Dictionary
  • Sets
  • Operators
  • If.. Else.. Statements
  • For Loop

  • Python MySQL

  • Environment Setup
  • Database Connection
  • Creating a New Database
  • Creating Tables
  • Inset Operation
  • While Loop
  • Break
  • Continue
  • Pass
  • Date & Time @ Functions
  • Packages and modules @ Reading a File
  • Writing into File
  • Class & Objects
  • Python — Exceptions
  • Regular Exp
  • Mathematics
  • Read Operation
  • Update Operation
  • Join Operation
  • Performing Transactions

  • Numpy

  • ndarray
  • Array Creation
  • Data Type Objects
  • Data type Object (dtype) in NumPy
  • Indexing
  • Basic Slicing and Advanced Indexing
  • Iterating Over Array
  • Binary Operations

  • Pandas DataFrame

  • Creating a Pandas DataFrame
  • Dealing with Rows and Columns in Pandas Data Frame O Indexing and Selecting Data with Pandas
  • Boolean Indexing in Pandas
  • Conversion Functions in Pandas Data Frame
  • Iterating over rows and columns in Pandas Data Frame O Working with Missing Data in Pandas
  • Working With Text Data
  • Working with Dates and Times
  • Merging, Joining and Concatenating
  • Mathematical Function @ String Operations
  • Linear Algebra
  • Sorting, Searching and Counting
  • Set 1 (Introduction)
  • Set 2 (Advanced)
  • Multiplication of two Matrices in Single line using Numpy in Python

  • Data visualization using Bokeh
  • Exploratory Data Analysis in Python
  • Data visualization with different Charts in Python
  • Data Analysis and Visualization with Python
  • Math operations for Data analysis

  • Object-Oriented Concepts

  • Object and Members
  • Data Hiding and Ob|ect Printing
  • Inheritance, examples of an oblect, subclass and super
  • Polymorphism in Python
  • Class and static variable in Python
  • Class method and static method in Python
  • Changing class members
  • Constructors in Python
  • Destructors in Python
  • First-class function
  • str() vs repr()
  • str() vs vpr()
  • Metaprogrammingvvithmetaclasses
  • Class and instance attribute @ Reflection
  • Barrier objects
  • Timer oblects
  • Garbage collection

  • Functions

  • Functions in Python
  • class method vs static method in Python
  • Write an empty function in Python — pass statement
  • Yield instead of Return
  • Return Multiple Values
  • Partial Functions in Python
  • First Class functions in Python
  • Precision Handling
  • *args and **kwargs
  • Python closures
  • Function Decorators
  • Decorators in Python
  • Decorators with parameters in Python
  • Memoization using decorators in Python
  • Python bit functions on int (bit length, to bytes and from bytes)

  • Descriptive Statistics

  • Five Number summary (mix, max, 1St Quartile, Ord Quartile and Median)
  • Measures of central tendency
  • Measures of Dispersion
  • Data Analysis and Visualization
  • Retail Case Study - Data Analysis

  • Inferential Statistics

  • Introduction
  • Probability Theory, Probability
  • Distribution
  • Binomial distribution
  • Poisson Distribution
  • Normal Distribution
  • Case Study — Binomial & Poisson
  • distribution

  • Inferential Statistics Part - 2

  • Hypothesis Testing Overview
  • One Sample T test, Two independent sample T test 0 One Way Anova , Two way Anova with Replication
  • Two way Anova without replication
  • Case Study — Hypothesis Testing
  • Case Study — Anova with and without replication

  • Overview of certificate programme in ML & AI

  • Introduction to six modules of the programme
  • Programme Objectives & Learning outcomes
  • Evaluation of the courses (Quizzes/Assignments/Tests)
  • ML&AI in today's world
  • A real life ML&AI project and value of it to the business

  • Introduction to Regression

  • Introduction to Supervised Learning
  • Regression vs. Classification
  • Linear and Polynomial Regression
  • Applications and Case Study for the module
  • Overview of Model Building for Linear Regression

  • Mathematics Foundations

  • First and Second derivatives of multivariate functions
  • Maxima and Minima of univariate and Multivariate Functions
  • Convex Function, Necessary and sufficient condition for convexity of functions
  • Determinant & Inverse of Matrices, Solving Simultaneous Equations

  • Model Building using Least squares

  • Cost/Loss Function for linear regression
  • Convexity of the Cost/Loss Function
  • Optimizing Cost/Loss Function by Solving Normal Equations
  • Implementation in Python
  • Optimizing Cost/Loss Function by Gradient Descent (I)
  • Optimizing Cost/Loss Function by Gradient Descent (II)
  • Optimizing Cost/Loss Function by Stochastic Gradient Descent and Batch Gradient Descent
  • Implementation in Python (Gradient & Stochastic Gradient Descent Methods)

  • Model Accuracy & Selection

  • Measuring the Quality of Fit
  • Implementation in Python
  • Bias-Variance Decomposition
  • Training Data, Testing Data and Cross Validation Data
  • Polynomial Regression - Selecting the appropriate
  • degree of the polynomial O Implementation in Python

  • Overfitting

  • Introduction to Overfitting
  • Reasons for overfitting
  • Counters to control overfitting — Ridge Regression
  • Implementation in Python (Ridge)
  • Counters to control overfitting — Lasso Regression
  • Implementation in Python (Lasso)
  • Compare Ridge vs Lasso vs Model without
  • Regularization with a case study

  • lnterpretability of regression models

  • Statistics Foundations — Inferential Statistics and Hypothesis Testing, Significance tests, p-values
  • Statistics Foundations — Inferential Statistics and Hypothesis Testing, Significance tests, p-values
  • lnterpretability of regression model through coefficients of the model
  • Interpretability of the regression built for the Case Study
  • Discussion on regression for a real life business scenario

  • Overview of Feature Engineering

  • Introduction to Feature Engineering
  • Types of data and its sources
  • Data quality (Missing values, Noisy data)
  • Implementing a Scrapper using Python

  • Data Preprocessing

  • Aggregation and Sampling
  • Feature Creation
  • Discretization and Binariz ation
  • Data Transformation
  • Feature Subset Selection
  • Feature selection using Filter Methods
  • Feature selection using wrapper methods
  • Implementing Feature selection using Python
  • Similarities between attributes

  • Dimensionality Reduction

  • Statistics foundations (Variance, Covariance)
  • Introduction to Dimension reduction
  • Principal Component Analysis (PCA) using Minimum Variance formulation-1
  • Principal Component Analysis (PCA) using Minimum Variance formulation-2
  • Principal Component Analysis (PCA) using Minimum Variance formulation-3
  • Implementing PCA using Python
  • Industry talk on feature engineering for a problem domain

  • Data Visualization (Industry Expert)

  • Summary Statistics
  • Histograms
  • Bar Charts / Pie charts
  • Box / scatter plots
  • Contour plots
  • Heatmaps
  • Parallel Coordinates
  • TSNE
  • Industry talk on Visualization

  • Introduction to Classification
  • Overview of the Classification Module
  • Types of classification algorithms - Discriminant Functions,
  • Probabilistic Generative models and Probabilistic Discriminative
  • models, Tree based models
  • Classification Algorithms covered in the course and type of these algorithms
  • O Applications of classification and case study

  • Nearest-neighbour Methods

  • kNN Classifier
  • Measures of prediction accuracies of classifiers — precision, recall, AUC of ROC etc.
  • Finding optimal k
  • Python Implementation of kNN

  • Naive Bayes Classifier

  • Probability Foundations — Discrete & Continuous Random Variables, Conditional Independence, Bayes Theorem (1)
  • Probability Foundations — Discrete & Continuous Random Conditions Independence, Bayes Theorem (2)
  • Naive Bayes Classifier — Derivation
  • Python implementation of Naive Bayes Classifier O Naive Bayes Classifier is a generative model
  • Advantages of Nai”ve Bayes Classifier and when to use Naive Bayes Classifier*
  • Interpretability of Naive Bayes Classifier

  • Logistic Regression

  • Significance of Sigmoid function and finding its derivative
  • Statistics Foundations — Maximum likelihood estimation
  • Cross entropy error function for logistic regression and its optimal solution
  • Logistic Regression is probabilistic discriminative model
  • Implementation of logistic Regression using Python
  • Decision boundary of logistic regression
  • Overfitting of logistic regression and counter measures p lnterpretability of logistic regression

  • Decision Tree

  • Decision Tree Representation
  • Entropy and Information Gain for an attribute
  • Search in Hypothesis space, ldc Algorithm for decision tree learning
  • Implementation of Decision Tree using Python
  • Prefer short hypothesis to longer ones, Occam's razor
  • Overfitting in Decision Tree
  • Reduced Error Pruning and Rule post pruning
  • Alternative measures for selecting attributes
  • Interpretability of Decision Tree

  • Introduction to Unsupervised Learning, Clustering
  • Unsupervised Learning - Introduction - Applications- Clustering as a unsupervised learning task - Defining clustering
  • Introducing Various ways to solve clustering problem (similarity based, density based, hierarchical, graph theoretic based) - Notion of quality of clustering
  • Overview of clustering algorithms

  • Case Study

  • Introducing the clustering case study(to be identified) to be used throughout the course for assignments -
  • overview of the data set to be used -
  • Exploring this data using Python
  • K-Means Algorithm

  • K-Means Algorithm
  • Discussion on Various Initializations, Standardizing Attributes (for eg- z-score) & Convergence
  • Demonstration in Python
  • Applications of using K-means with Images, videos, documents

  • K-Means - Variations

  • Online stochastic version of k-means (with sequential update)
  • -Discussions on quality of clustering/ convergence - Applications Mini-Batch K-Means - Discussions on quality of clustering/
  • convergence - Applications

  • Detecting Outliers

  • Outliers and Clustering - Overview.
  • Using K-means to detect outliers
  • Demonstration in Python

  • Math Fundamentals or EM Algorithm

  • Jensen's Inequality
  • KL Divergence
  • Mixtures of Gaussians (MoG) - Applications, modelled as MoG

  • EM Algorithm

  • Using Maximum Likelihood to estimate mixture densities - Issues
  • EM Algorithm for Gaussian mixtures

  • Clustering for Customer Segmentation

  • Derivation
  • Illustration of a problem using a mixture of two Gaussians, and Python
  • General Form of EM Algorithm and Applications
  • Relationship to K-Means Algorithm

  • Hierarchical Clustering

  • Introduction to hierarchical clustering
  • Agglomerative Clustering Vs Divisive Clustering
  • Distance Measures (Minimum distance, Maximum Distance, Mean Distance, Average Distance)
  • Algorithms
  • Single linkage, Complete Linkage algorithm
  • Demonstration in python
  • Discussion on Termination, efficiency, applications

  • Clustering for Anomaly Detection

  • Density based approach to clustering - Introduction
  • DBSCAN - Density, Density-reachability, Density-connectivity
  • DBSCAN Algorithm
  • Performance & scalability
  • Demonstration using Python
  • Density Based Clustering
  • Assessing Quality of Clustering Significance of Clustering - Interpreting/ summarizing Clusters by businesses
  • Cluster Validity Evaluation (measuring compactness, separation, cluster overlap, etc)
  • Stability of Results from clustering algorithms
  • Determining number of clusters

  • Association Rule Mining

  • Market Basket Analysis - Use cases
  • Terminologies / Measures - association rules, support, confidence, k-itemset, Frequent itemsets, closed item sets
  • Discussion on computational complexity in generating the itemsets

  • Apriori Algorithm

  • Algorithm
  • Generating Association Rules from frequent itemsets Efficiency Issues and few ways to address it.
  • Evaluating interestingness of patterns
  • Demonstration of Apriori algorithm using python for a practical use case

  • Time series Prediction and Markov Process

  • Introduction
  • Introduction to time series data
  • Time Series prediction applications (eg predicting stock prices, fraud detection, applications in text and speech processing) (discrete) Markov Processes - Overview and Terminologies

  • Hidden Markov Model

  • Introduction
  • Evaluation Problem - Given a model, evaluate the probability of observing the sequence -(forward-backward Procedure)
  • Finding most likely state sequence explaining time series data - Viterbi Algorithm
  • Learning Model parameters - An application of EM Algorithm
  • Case Study: Introduce a problem from an application domain- solution using HMM - Python Implementation / Demonstration

  • Document vectorization and Parts of Speech Tagging
  • Introduction to Text Mining p Binary term incidence matrix
  • Information Retrieval Pipeline O Inverted Index Construction
  • Merge Algorithm and Query Optimization
  • Tolerant Retrieval using Normalization, Query expansion, Stemming, Lemmatization, Wild card query using K-Gram index
  • Ranked Retrieval using TF-IDF and Cosine score
  • Introduction to Part of speech tagging
  • Part of speech tagging using HMM-1 p Implementing POS Tagging in Python

  • Topic modelling using LDA

  • Mathematical foundations for LDA: Multinomial and Dirichlet distributions-1
  • Mathematical foundations for LDA:
  • Multinomial and Dirichlet distributions-2
  • Intuition behind LDA
  • LDA Generative model
  • Probabilistic Graphical Models
  • Latent Dirichlet Allocation
  • Implementing LDA in Python

  • Introduction to Sentiment Analysis

  • Sentiment Analysis
  • Subjectivity Analysis
  • Topic Extraction
  • Product Reviews
  • Opinion Retrieval and Spam
  • Opinion Summarization
  • Implementing Sentiment Analysis in Python

  • Recommender Systems

  • Introduction to Recommender Systems
  • Collaborative filtering
  • User based Collaborative filtering
  • Item based Collaborative filtering
  • Matrix factorization using Singular Value Decomposition
  • Latent Factor Models
  • Metrics used for evaluating Recommender Systems
  • Implementing Recommender System in Python
  • Industry talk on application of Recommender Systems

  • NLP (Natural Language Processlng}

  • Introduction to NLP
  • Stop Words
  • Tokenization
  • Stemming and lemmatization
  • Bag of Words Model
  • Word Vectorizer
  • TF-IDF
  • 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
  • Time series analysis
  • LSTMs with attention mechanism

  • Artificial Neural Network

  • Introduction and Background
  • Discrimination power of single neuron
  • Training a single perceptron (delta rule)
  • Multilayer Neural Networks
  • Activation functions and Loss functions
  • Backpropogation -1
  • Backpropogation -2

  • DeepLearning

  • Introduction to end to end learning
  • Abstractions of features using deep layers Hyper parameter tuning
  • Regularization for Deep Learning
  • Dropout
  • Tensor flow Installation
  • Tensor flow Installation 2.0
  • Tensor flow Installation 1.6 with vir tual environment Tensor flow 2.0 function
  • Tensor flow 2.0 neural network creation Tensor flow 1.6 functions
  • Tensor flow 1.6 neural network and its functions Keras Introduction
  • Keras in - depth with neural network creation Mini project in Tensor flow

  • Advance computer vision

  • SCNN
  • Masked R- CNN
  • Xception
  • SENet Facene't
  • Im plement ing a ResNet — 3 4 CN N using Keras Pretra ined Models from Keras
  • Pretra ined Models for Transfer Learning
  • BERT
  • CNN Architectures
  • LeNet- 5
  • AIex Net
  • oog ieNet
  • VGGNet
  • Res Nz•t
  • SSD
  • SSD lite, C N N, RCN N, Faster RCNN Faster R CNN
  • Fast RCNN

  • Computer Vision

    Capstone Projects

  • Computer Vision Project
  • Traffic Surveillance System O Object identification
  • Object tracking
  • Object c\assWcation
  • Tensorflow object detection
  • Image to text processing
  • Speech to speech analysis
  • Vision based attendance system

  • Architecture of RNN

  • Unfolding of RNN
  • Training RNN
  • LSTM (1)
  • LSTM (2) and its applications
  • Ur dercomplcte AutocncodcrS (1)
  • RegularizetlAutoer coders (2)
  • Variational autoen coders
  • Manifold leari›lng with Autoeiuoders
  • Appllcatlons of Autoencoders
  • Boltzmann Machine
  • Restricted Boltzmann Machine
  • Oeep Belief Machines
  • GAN
  • Applications of GAN


    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 Full Stack Data Science 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.

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    Data Scientist

    DeepNeuron Testimonials


    • What is the objective of our Full Stack Data Science 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 Full Stack Data Science orchestration tools. With Deepneuron Certified Full Stack Data Science 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 Full Stack Data Science Course from Deepneuron?

      Deepneuron Full Stack Data Science 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 Full Stack Data Science Course accomplishment?

      After the completion of the Full Stack Data Science 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 Full Stack Data Science 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
      Full Stack Data Science 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:

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      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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