Advanced Data Science with Python

From Leading Data Science Training Providers

Upskill with Big Data Learning Program
Contact for more details

Data Science with Python Training at Samatrix

Experienced and qualified faculty

Experienced Faculty

Classroom sessions conducted by IIT, IIM alumni with 40 years of industry experience

Focus on concepts with practicals

Focus on Concepts

Emphasis on concepts of Data Analysis with Python, statistics, linear algebra, machine learning and neural networks

Instructor led classroom sessions

80+ hour of instructor-led classroom sessions. Additional 120+ hours for projects and assignments

Virtual classroom sessions

Virtual Classroom

Option of attending the session from home using video conference. Join live classroom and participate in discussion

Gain Proficiency in

with Samatrix.

Know about the upcoming batches

We have designed the program for beginners, who do not have any prior knowledge in Data Science, Machine Learning and Deep Learning.  The concepts of machine learning, deep learning and data science are explained in an easy way to help you learn the Data Science, Machine Learning, Deep Learning with Python

Advance Big Data Science with Python

Engaging, yet rigorous learning program in data science, data analytics, data analysis, statistical methods, and machine learning using Python

Samatrix offers an advanced course in Data Science with Python. The course is designed for research students, working professional and freshers who want to build a career in data science. The course helps build the required skills to pursue a career as a data scientist. From the advance data science with python course, you will learn the technologies such as data science, python, machine learning, hadoop, and spark.

Our flagship course “Advance Data Science with Python” has been carefully designed by industry experts and academic scholars to acquire knowledge and skills in linear algebra, statistics, probability distributions, hypothesis testing, machine learning concepts, map reduce, pig, hive, pregel, and zookeeper. You will get an opportunity to learn python, numpy, maplotlib, seaborn, pandas, scikit-learn, hadoop, scala, and spark.

Our case study based learning pedagogy ensures that the learner is able to focus on real-time problems and implement the learning to solve the problems. We have the right balance between practicals and theory. At the end of the course, the learner is eligible for placement assistance. The placement assistance is very useful for learners looking for a change in career path and begin a new career in data science.

What you will learn?

Key concepts of Python programming, Python libraries, statistical analysis techniques and machine learning models such as regression, classification, support vector machines, decision trees, and neural networks. Moreover, you will get an opportunity to work on 4 projects that will help not only in consolidating the concepts learned but also the experience on how to solve real-life machine learning problems.

Course Curriculum

Explore one of the most comprehensive course curricula for data science with python program.

  • Systems of Linear Equations
  • Row Reduction and Echelon Forms
  • Existence and Uniqueness
  • Vectors and Matrix Equations
  • Vector Matrix Products
  • Linear Independence
  • Matrix Multiplication
  • Transpose of Matrix
  • Inverse of Matrix
  • Vector Space, Null Space, Row Space
  • Eigenvalues and Eigenvectors
  • Orthogonality
  • Graphically Displaying Single Variable
  • Measures of Location
    • Mean
    • Median
  • Measures of Spread
    • Range
    • Variance
    • Standard Deviation
  • Displaying relationship – Bivariate Data
    • Scatterplot
    • Scatterplot Matrix
  • Measures of association of two or more variables
    • Covariance and Correlation
  • Probability
  • Joint Probability and independent events
  • Conditional probability
  • Bayes’ Theorem
  • Prior, Likelihood and Posterior
  • Discrete Random Variable
  • Probability Distribution of Discrete Random Variable
  • Binomial Distribution
  • Continuous Random Variables
  • Probability Distribution Function
  • Uniform Distribution
  • Normal Distribution
  • Beta Distribution
  • Point Estimation
  • Interval Estimation
  • Expectation Theory
  • Hypothesis Testing
    • Testing a one-sided Hypothesis
    • Testing a two-sided Hypothesis
  • Introduction to Python
  • Jupyter
  • Numpy
  • Matplotlib
  • Seaborn
  • Panda
  • Scikit Learn
  • What is Machine Learning
  • Machine Learning vs Computer Program
  • Define Machine Learning
  • Application of Machine Learning
  • Relation between variables
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  • Prediction
    • Dependent Variable vs Independent Variables
    • Reducible Error and Irreducible Error
    • Expected Value and Variance
  • Inference
    • Which Predictors are associated with Response?
    • Relationship between response and predictors
  • Learning Methods
    • Parametric Methods
    • Non Parametric Methods
  • Model Flexibility vs Interpretability
  • Model Accuracy and Selection
    • Quality of Fit
    • Bias – Variance Trade Off
    • Bayes Classifier
    • K-Nearest Neighbors
  • Basic Concepts
  • Construction of Regression Model
    • Selection of Predictor Variables
    • Functional Form of Regression Relations
    • Scope of Model
  • Uses of Regression Analysis
    • Description
    • Control
    • Prediction
    • Regression and Causality
  • Formal Statement of Model
  • Important Features of Model
  • Meaning of Regression Parameters
  • Steps in Regression Analysis
  • Estimation of Regression Function
    • Least Square Estimator
    • Estimating the Coefficients
    • Gradient Descent
    • Estimation of Variance Terms
  • Accuracy of Coefficients
  • Accuracy of Model
    • Residual Standard Error
    • R Square Statistics
  • Multiple Linear Regression
  • Estimating Regression Coefficients
  • Analysis
    • Relationship between Response and Predictor
    • Important Variables
    • Model Fit
    • Predictions
  • Qualitative Regression Models
  • Synergy / Interaction Effect
  • Polynomial Regression
  • Problems with Regression Models
    • Non-linearity of the data
    • Correlation of error terms
    • Non constant variance of error terms
    • Outliers
    • Leverage Points
    • Collinearity
  • Basic Concept with Example
  • Why not Linear Regression
  • Logistic Regression
    • Logistic Model
    • Estimating Regression Coefficients
    • Multiple Logistic Regressions
  • Linear Discriminant Analysis
  • Nearest Neighbour Methods
  • Cross Validation
  • Bootstrap
  • Choosing Optimal Model
    • F Test
    • Likelihood Ratio Test (LRT)
    • Akaike Information Criterion (AIC)
    • Bayes Information Criterion (BIC)
    • Adjusted R2
  • Subset Selection
    • Best Subset Selection
    • Forward Stepwise Selection
    • Backward Stepwise Selection
  • Ridge Regression
    • Ridge Regression vs Least Square
  • Lasso Regression
    • Variable Selection Property of Lasso
  • Singular Value Decomposition (SVD)
  • Principal Components
  • Principal Component Analysis (PCA)
  • Geometric Interpretation
  • Polynomial Regression
  • Step Functions
  • Regression Splines
  • Smoothing Splines
  • Local Regression
  • Generalized Additive Models
  • Construct the Tree
  • Regression Tree
  • Classification Tree
  • Impurity Functions
    • Entropy
    • Gini Index
    • Misclassification Rate
  • Tree Pruning
  • Advantages and Disadvantages of Trees
  • Bagging
  • Random Forests
  • Boosting
  • Maximal Margin Classifier
  • Support Vector Classifier
  • Support Vector Machine
  • Introduction to Neural Network
  • Perceptrons
    • NAND Gate
  • Sigmoid Neuron
  • Gradient Descent
  • Multilayer Neural Network
    • Architecture of Multilayer Network
  • Backward Propagation Algorithm
  • Cross Entropy Cost Function
  • Overfitting and Regularization
  • Weight Initialization
  • Meta-Heuristic Optimization
  • Simulated Annealing
  • Particle Swarm Optimization
  • Genetic Algorithms
  • Ant Colony Optimization
  • Differential Evolution
  • Genetic Programming
  • Introduction to TensorFlow
  • TensorFlow Basics
    • Computation Graphs
    • Graphs, Sessions and Fetches
    • Flowing Tensors
    • Variables, Placeholders, and Simple Optimization
  • Introduction to Keras
  • Introduction to Convolutional Neural Networks
  • Introduction to Recurrent Neural Networks (RNN)
  • Auto-Encoders
  • Generative Adversarial Networks
  • The Challenge of Unsupervised Learning
  • Principal Component Analysis
  • Clustering Methods
    • K-Mean Clustering
    • Hierarchical Clustering
    • Practical Issues in Clustering
  • Introduction to Hadoop
  • Apache Spark
  • MapReduce
  • Hadoop Distributed Filesystem
  • Pig and Hive
  • Pregel
  • Zookeeper

Key Learning Areas

The learning areas include hands on expertise in Machine Learning, Deep Learning, Data Science, Hadoop and other tools such as Spark, Pig, and Hive. The program focuses on building expertise in  data science, machine learning and deep learning concepts. Even if you do not have any expertise in data science, machine learning, and deep learning, you will learn faster from our classes.

Machine Learnng
Linear Regression, Classification, Statistical models
Deep Learning
Neural Networks using Tensor Flow and Keras
Data Science
Hadoop, Spark, Scala, Pig, and Hive

Admission Process

Any candidate who qualifies the minimum eligibility criteria can apply for the course


Selection Process

1. Fill Application Form: Apply online using the application form
2. Application Review: Admission committee review the applications submitted
3. Personal Interview: The Admission committee can invite the applicant for a personal interview.
4. Admission Offer: The selected candidates would be communicated about their success in admission process

Join the training program

Learn the latest technology and be part of a revolution. 

Training Program Fee Rs 85000 + GST

Registration Fee Rs 20000

Placement Assistance

Samatrix offers 100% placement assistance to all the candidates who complete the program requirements successfully

Samatrix has a dedicated placement assistance team. Under our placement assistance program, we help the learners, who enroll for the program, introduce to our 100+ hiring partners. 

On successful completion of the training requirements, the learner becomes eligible for the training assistance program. Our training assistance team work closely with the learners to understand their career goals and provide mentorship.

We prepare the learners for application process and interviews by conducting resume review workshops, mock HR and technical interviews with industry experts.

Frequently asked questions

If you have any question about the program, you can fill the contact form or go through the list of FAQ

Samatrix is not only the institute. Samatrix is a technology consulting company based at Gurgaon,Bangalore. Samatrix Consulting led by IIT, IIM, Intel, HP alumni with deep industry expertise. It focuses on solving real business problems and developing the ecosystem through skill development in cutting-edge technologies. It cater to finance, insurance, travel, logistics, media, entertainment and e-commerce domains. is committed to providing innovative solutions and workforce development in cutting-edge technologies to industries, corporates, higher educational institutions, and universities. It serves technical, business schools, universities, and corporates by providing quality education to students and faculty in artificial intelligence, machine learning, data science, data visualization, blockchain, augmented analytics, Internet of Things (IoT), cloud computing, and virtual reality

The training program has been designed for both fresher and professionals with industry experience. If the learner has the willingness to learn and dedication to spend time, he/she will certainly benefit from the program.


This is an instructor-led classroom face to face training program. Face to face classroom training helps learners focus on the training program and provides an opportunity to interact with the trainer and other participants.

After successful completion of training program and projects, will issue a certificate of completion of training

Several industry leaders are hiring partners of Our team would help all the learners in job placement. Majority of our trainees have been successfully placed.

The curriculum of Artificial Intelligence Machine Learning training program is very rigorous. This is 135+ hours of instructor led program. The program will start at a designated time on weekends over 4 months. During the course, you will have access to one of the best study material that includes:

  1. Powerpoint presentations
  2. Recording of the sessions
  3. Case study with dataset
  4. Any other relevant study material on the topic

Each learner will have his personal userid through which he can access the study material through the learning management system. Each learner will be provided the access to the LMS for 1 year including course duration