Data Science for Python

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Course Overview

Python is the most popular programming language for Data Science as on Today. Python is powerful , easy to learn and flexible tool for coding Data Science and Machine Learning algorithms. In recent years, Python has evolved immensely with respect to Data Science sphere, with a huge community around Python creating quite a few power data science and analytics packages such as Pandas, Numpy, Scikit Learn, Scipy and more. As a result, analyzing data, modeling machine learning algorithms with Python has never been easier.

This course “Python for Data Science”, is designed for candidates with or without programming skills, with basics of Data importing, Data mugging and coding Machine Learning algorithms along with effective programming techniques. This also includes Python Data Science challenges kit, enabling the candidates to not only understand Python core concepts but also gain practical mastery over Python for Data Science, which is very much in demand in Today’s Data Science job opportunities.


  • Resume & Interviews Preparation Support
  • Hands on Experience on Project.
  • 100 % Placement Assistance
  • Multiple Flexible Batches
  • Missed Sessions Covered
  • Practice Course Material

At the end of Data Science for Python Training Course, Participants will be able to:

  • Gathering input and manipulating input/output
  • Building reusable Functions with parameters and return values
  • Decisions and repetition using conditional statements and loops

Course Duration

  • Weekends: 2 Months (50-60 hours)

Prerequisites :

  • Basic Programming is recommended
  • Basic Statistics knowledge is recommended

Who Should Attend?

  • Candidates wanted to pursue Data Science career, with basic or no programming skills
  • Seasoned convential programmer aiming to gain basic machine learning coding skills
  • Job seekers, pursuing career as Data Science Developer
  • Professionals, who’s job involves Data Science and Python.


1.1 Introduction

  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Analytics Methodology & problem solving framework
  • Why Python for data science?

1.2 Core Python

  • Python philosophy
  • Fundamentals
  • Python Data Types and Variables
  • Structures and Conditional Statements
  • Importing Packages
  • Running python programs
  • Variables and Data structures
  • Control flow
  • Lists and Tuples
  • Dictionaries
  • Introduction to Python Libraries
    • Numpy
    • Pandas
    • Matplotlib
    • IPython and Jupyter
    • Scikit-learn

1.3 Functions and File Handling

  • Functions Modules
  • Recursive Functions
  • File handling
  • I/O Operations

1.4 Debugging and Bug Fixing

  • Writing algorithms
  • Using data structures
  • Using Control Flow & Functions
  • Creating An Algorithm
  • Debug the codes
  • How to Fix the Bugs
  • Understand the Console

1.5 Numpy

  • Introduction of Numpy
  • Numpy Array
  • Numpy array indexing
  • Numpy Operations
  • Numpy Exercise

1.6 Pandas

  • Introduction of Pandas
  • Series
  • DataFrames
  • Missing Data, Groupby
  • Merging Joining and Concatenating
  • Operations
  • Data Input and Output

1.7 Data Visualization – Matplotlib

  • Introduction of Matplotlib
  • Matplotlib Part 1
  • Matplotlib Part 2

1.8 Data Visualization – Seaborn 

  • Introduction of Seaborn
  • Distribution Plot
  • Categorical Plot
  • Matrix Plot
  • Grids
  • Regression Plot

1.9 Data Visualization – Plotly and cufflinks

  • Introduction.

1.10 Data Visualization – Geographical Plotting, Folium and Bokeh

  • Introduction
  • Choropleth Maps
  • Choropleth Exercise
  • Folium and Bokeh Exercise

1.11 Machine Learning– Using Python

  • Linear and Logistic Regression using Python
  • K Nearest Neighbour
  • Random Forest and Decision Tree


It might depend on every individual company. But, widely these are the roles anyone can expect after successful completion of Data Science with Python Training in Pune.

  • Data Scientist
  • Business Analytics Experts
  • Support Engineer
  • Statistical Programmer Specialist

Because of Python’s extensibility and general purpose nature, it was inevitable as its popularity exploded that someone would eventually start using it for data analytics. As a jack of all trades, Python is not especially well-suited to statistical analysis, but in many cases organizations already heavily invested in the language saw advantages to standardizing on it and extending it to that purpose

Classes are held on weekends. You can check available schedules and choose the batch timings which are convenient for you.