Data Science Training

Data Science Training

MaxMunus's Data Science Training will help you learn the Python programming which is the basis for Data Science and Machine Learning. This training will cover Data Science with Python and will also explian Machine Learning in deep. Apart from Python the course also covers Data Science elements like Introduction to Statistics and Probability using Python, Acquiring Data from various sources like CSV, text, API, Web scraping etc.

The course also covers in detail topics like Numpy and Pandas where it covers shape manipulation, n-dimensional array, Series and Dataframes, Time series, Visualization with Matplotlib -plotting etc.

The course also covers Data Wrangling where it covers Cleaning up the Data, Dimensionality Reduction etc.

In Machine Learning part the course covers Linear Regression, Naive Bayes, Decision trees, K-means clustering, Tokenizing, Stemming, Lemmatizing etc.

This course also gives you an opportunity to do a hands on project on Data Science where you will use all the skills learned during this course. This project will increase your technical skills and gives you a lot of confidence in executing any project.

Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

                                                  Corporate Training

Course Information

Data Science Course Duration:  66 Hours

Data Science Training Timings:  Week days 1-2 Hours per day (or) Weekends: 2-3 Hours per day

Data Science Training Method:  Online/Classroom Training

Data Science Study Material:  Soft Copy




  • Either should have taken Beginner Python course or already knows Python programming

Recommended but not mandatory:

  • Linear Algebra- beginner level understanding of matrix operations. (covered as a part of syllabus)
  • Calculus- concept of differentiation
  • Statistics and Probability - good if you know. (covered as a part of syllabus)


Course Content

Introduction to Python programming

Data Science and Machine Learning

What is Data science - Intro to Data Science

Installing Python/Anaconda

Introduction to Statistics and Probability using Python

  • Individual Attributes of Data
  • Univariate analysis using mean, median, mode, variance
  • Correlation and Regression
  • hypotheses and Hypothesis Testing
  • Probability
  • Conditional Probability
  • Vectors
  • Matrices

Acquiring Data from various sources

  • CSV files
  • Text Files
  • Databases - Databases handling using python- MySQL and sqlite3
  • Using API
  • Web links - Web scraping
  • Excel Files

Python packages for Data Science


  • Introduction to Numpy
  • Array - creation and printing
  • n-dimensional array
  • shape manipulation
  • basic operations
  • Copy and views


  • Introduction
  • Series and Dataframes
  • Basic operations on dataframes- creation, manipulation, filtering,grouping
  • Time series
  • Visualization with Matplotlib -plotting

Data Wrangling

  • Exploring and Visualization
  • Cleaning up the Data
  • Handling Missing Data/Filtering
  • Dimensionality Reduction

Machine Learning

Supervised Learning

  • Linear Regression
  • Logistics Regression
  • K-nearest neighbors
  • SVM (Support Vector Machines)
  • Naive Bayes
  • Decision trees

Unsupervised Learning

  • K-means clustering

Natural Language Processing

  • Tokenizing
  • Word frequency
  • Stop Words
  • Stemming
  • Lemmatizing



There are many datasets available. The following are very popular projects among the beginners. However, if you have a project that you want do, you will get support from us. In either case, you can either do it alone or in group.

     1. Iris data set- predict the type of flower from the given dataset.

     2. Load Prediction: determine whether a loan will default, as well as the loss incurred if it does default. 

     3. Walmart sales prediction: 

a. Predict the sales across various departments in each store.

b. Predict the effect of markdowns on the sales during the holiday seasons. 

     4. Boston Housing: Predict the median value of occupied homes.

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