Data Science with Python Training

Data Science & Machine Learning with Python


According to Harvard Business Review, Data Science is the sexiest job of the 21st century.

A recent report by Google concluded that since the last 18 months, the interest in Machine Learning has doubled.

Learn Data Science with Python Certification

Python programming, in the recent years, has become one of the most preferred languages in Data Science. And when it comes to building Machine Learning systems, Python provides an ideally powerful and flexible platform to build on. Through a comprehensive, hands-on approach, this course provides you the opportunity you need to experiment with a wide variety of Data Science and Machine Learning algorithms. We believe that a practical, hands-on approach is the key to meaningful learning and skills advancement. With this in mind, we integrate real-life exercises and activities throughout our trainings, with long-term retention of learning and development in mind.

Course Objectives

Designed with the industry's most in-demand skills in mind, this course provides a solid foundation in Data Science and Machine Learning with Python expertise, helping you to ensure a promising career ahead. Our Data Science & Machine Learning with Python course includes all of the following:

  • Introducing data science, with a focus on the job outlook and market requirements
  • Data Science Project Life Cycle
  • Basics of Statistics – Measures of Central Tendency and Measures of Dispersion
  • Discrete and Continuous Distribution Functions
  • Advanced Statistics Concepts – Sampling, Statistical Inference and Testing of Hypothesis
  • Introduction of Python Programming, Anaconda and Spyder
  • Installation and Configuration of Python
  • Control Structures and Data Structures in Python
  • Hands-on Applied Statistics Concepts using Python
  • Functions and Packages in Python
  • Graphics and Data Visualization Libraries in Python
  • Introduction to Machine Learning
  • Machine Learning Models and Case Studies with Python

Target Audience

  1. Software developers and programmers who want to reap the benefits of a lucrative Data Science and Machine Learning career
  2. Data Analysts or Financial Analysts from the non-IT industry who want to make a transition to the IT industry
  3. Individuals, students and corporate professionals who want to upgrade their technical skill set


Introduction to Data Science

  • Data Science Introduction
  • Data Science Toolkit
  • Job outlook
  • Prerequisite, Target Audience
  • Data Science Project Lifecycle – CRISP-DM Model

Basics of Statistics

  • Statistics Concepts
  • Random variable
  • Type of Random variables
  • Central Tendencies – Mean, Mode, Median, Probability, Probability Distribution
  • Random variables, PMF, PDF, CDF
  • Type of RV – Nominal, Ordinal, Interval, Ratio; Variance, Standard Deviation
  • Normal Distribution, Standard Normal Distribution
  • Binomial Distribution,
  • Poisson Distribution

Advanced Statistics

  • Sampling
  • Inferential Statistics
  • Sampling Distribution
  • Central Limit Theorem
  • Simulation
  • Null and Alternative Hypothesis
  • Hypothesis Testing
  • 1-tail test and 2-tail test, type I and Type II error
  • z test & t test

Python Programming for Data Science (Lab)

  • Introduction to Python, Anaconda & Spyder, Installation & Configuration
  • Data Structures in Python
    • List
    • Tuples
    • Array in NumPy
    • Matrices
    • Dataframe in Pandas
  • Control Structure & Functions – If-Else, For loop, While loop
  • Slicing, dicing & filter operations

Applied Statistics in Python (Lab)

  • Normal distribution
  • Simulation
  • Hypothesis testing
  • Other statistical concepts using Python

Graphics and Data Visualization, Exploratory Data Analysis in Python (Lab)

  • Graphics and Data Visualization libraries in Python
    • Plotly
    • Matplotlib
    • Seaborn
    • Other useful packages/functions in Python
  • Exploratory Data Analysis Exercise in Python

Machine Learning Concepts

  • Introduction to machine Learning
  • Supervised and Unsupervised ML, Parametric/Non parametric Machine Learning Algorithms
  • Machine Learning Models
    • Linear Regression
    • Logistic Regression
    • Classification & KNN
    • Decision trees
    • Random Forest
    • Clustering – K Means & hierarchical Clustering,
    • Time Series Analysis
    • ARIMA Models,
    • Support Vector Machine
  • Model Validation/Cross validation techniques, Parameter tuning

Real World Data Science & Machine Learning Case Studies in Python (Lab)

ML Case Studies on

  • Regression
  • Classification
  • Decision Tree
  • Random Forest
  • Clustering
  • Time Series Analysis

Course Features


Live and interactive online sessions with an industry-expert instructor


A technical team that's dedicated to answer your questions at any time, regardless of where you're located


We provide lifetime Learning Management System (LMS) access, which you can access from across the globe


We guarantee the best price for every course that aligns with the quality of our course deliverables


After you successfully complete the training program, you will be evaluated on parameters such as attendance in the sessions, objective examination scores, and other factors. Based on your overall performance with these parameters, you'll be certified by Cognatrix.



We proudly seek out and employ the best in the industry! Our class is run by certified industry and subject-matter experts with complete and comprehensive experience under their belts


To attend the live virtual training, a speed of at least 2 Mbps would be required


You'll have lifetime access to our Learning Management System (LMS), including class recordings, presentations, sample code, and projects. You'll also be able to view recordings of each session. We also have our technical support team ready to assist you with any questions you may have.