Data Science Course & Training | Online with Python Certification

Data Science using Python Training

Students need to learn python before they gain career-building skills in data science with python. There is no prior coding experience and it is expected to find success as a data scientist. Learning python assists you to manipulate, importing, cleaning, and visualizing data in the data science process. Once you complete data science with python certification , you become entitled to receive jobs as researchers. Enroll & Get Certified now!

  • 40 Hours Instructor­ led Online Training
  • Authorized Digital Learning Materials
  • Lifetime Free Content Access
  • Flexible Schedule Learn Anytime, Anywhere.
  • Training Completion Certificate
  • 24x7 After Course Support

Program Calendar

  • Available Dates
    Live Virtual Training
    • cal.png08 June, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png15 June, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png22 June, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
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Course Overview

Aspirants need to go through interactive exercises, obtain hands-on with the most popular Python libraries, incorporating NumPy, pandas, Matplotlib, etc. the training assist you in work-on on real-world datasets. This enables participants to study the arithmetical and machine learning processes to know about the decision trees and apply natural language processing (NLP).

Begin the learning process, enhance your Python skills, and start the venture to turn out a convinced data scientist.

Data Science is a scientific method that helps in learning a variety of data patterns delivered by users. The scientific methodology applies technologies such as Python and R to understand the data.

Python for data scientist

Master the fundamentals of data analysis in Python. Develop your ability set by learning scientific computing.

Usually, data science is a structural design that uses scientific computing, algorithm, framework, and haul out the insight knowledge base. The data scientists study the data received in structured or unstructured data format. After scrutinizing the data, the scientists deliver implausible solutions to the corporations so that establishments could take accurate data-driven decisions.

Python is general-reason programming that is turning out to be more recognized for data science. Globally, businesses are applying Python to crop insights from their data and increase a competitive edge. However, differentiating wifromormer Python tutorials, the data science with Python course aims at Python purposely for data science.

In the Python course, candidates will study the most useful paths to store and influence data, and require data science tools to start conducting their own anes.

Begin the journey in data science with Python training by enrolling at Mildain Solutions.

Why advise Python with Data Science?

Participants registering in data science with a Python course will recognize that Python is a swift, chronological, and open-source programming language. This is not difficult to apply. One of the chief inspiring keys of using code in data analysis is that it reindeer collaborations (collabs). Candidates in training certification learn to replicate it. It incorporates counts, submissions, or identifies mistakes in scrutiny.

How data science with Python certification is achieving its way.

Carrying out the training in data science enables candidates to discover quick jobs, a better data community, and a high salary. Python delivers a faster presentation, has skilled features, constituent better essential features compared to big data solutions, web frameworks, and unit testing.

The industry is searching for participants who can examine data interacting with Python tools. This incorporates store data.

How Python is recognizing industries.

Data science being an obtainable category in analyzing data, Python in data science permits data scientists to interrelate with Python tools to examine and store data.

Top 10 reasons t0 use Python in Data Science:

Python is an active, free open source, high-level, and interpreted programming language. It renders the programming method of object-oriented and procedural-oriented programming.

  • Python is a dynamic language that does not require stating the uneven type.
  • The language that supports object-orientation - One of the essential features of python is Object-Oriented programming. Python renders concepts of objects encapsulation, and classes, etc.
  • Graphical User Interface Programming Support - Graphical User interfaces are developed using a module like PyQt4, PyQt5, wxPython, and Tk.
  • Efficient coding - Python is easy to learn as compared to other languages such as C, C#, JavaScript, Java, etc. It is an efficient high-level programming language, which requires just a few hours for the students to learn to code
  • Accessible to all - Python language is an open-source high-level language that can be downloaded and accessed without paying a cent. Aspirants can access its source code.
  • Portable language - Python is a portable language. For instance, if you have developed a code for Mac, the code will run on Linux and UNIX as well.
  • Language – High level - Python is a flexible language that allows developers to avoid remembering the system architecture or managing the memory.
  • Extensible feature - Python is an extensible language that allows developers to write Python code in the C/C++ language.
  • Interpreted Language - Python can easily be integrated with other languages like C, C++, etc.
  • Large Standard Library - Python delivers a rich set of purposes and modules to write your code.

There are several libraries available in python for such as usual expressions, web browsers, and unit testing.

Learning Objectives

  • The Data Science with Python course will give you in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python. With this Data Science with Python course, you will learn to work with Python packages such as PROC SQL and various statistical procedures such as PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP, as well as advanced analytics techniques such as clustering, decision tree, and regression.
  • The Python for Data Science course is packed with real-life projects focused on customer segmentation, macro calls, attrition analysis, and retail analysis, as well as demos and case studies to give you practical experience in installing and working in the Python environment.
  • Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.


  • You learn to take the right decision depending on data analysis
  • Python with data science enhance the capabilities of decision making
  • Errors and latencies are curved smartly
  • Assist enterprises in improving the relationship with their customers
  • Data Science is the key to growing businesses

Additionally, the raw features of copious packages present advanced graphical competencies. The technology Python is used for preparing tailored-made graphs in the programming language.


Prerequisites: There are no prerequisites for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance.

Course Curriculum

  • Topic Covered:

    • Course Overview
    • Data Science Overview
      • Introduction to Data Science
      • Different Sectors Using Data Science
      • Purpose and Components of Python
    • Topic Covered:

      • Data Analytics Process
      • Knowledge Check
      • Exploratory Data Analysis(EDA)
      • EDA-Quantitative Technique
      • EDA – Graphical Technique
      • Data Analytics Conclusion or Predictions
      • Data Analytics Communication
      • Data Types for Plotting
      • Data Types and Plotting
    • Topic Covered:

      • Introduction to Statistics
      • Statistical and Non-statistical Analysis
      • Major Categories of Statistics
      • Statistical Analysis Considerations
      • Population and Sample
      • Statistical Analysis Process
      • Data Distribution
      • Dispersion
      • Histogram
      • Testing
      • Correlation and Inferential Statistics
    • Topic Covered:

      • Anaconda
      • Installation of Anaconda Python Distribution (contd.)
      • Data Types with Python
      • Basic Operators and Functions
    • Topic Covered:

      • Introduction to Numpy
      • Activity-Sequence it Right
      • Creating and Printing an ndarray
      • Knowledge Check
      • Class and Attributes of ndarray
      • Basic Operations
      • Activity-Slice It
      • Copy and Views
      • Mathematical Functions of Numpy
    • Topic Covered:

      • Introduction to SciPy
      • SciPy Sub Package – Integration and Optimization
      • SciPy sub package
      • Calculate Eigenvalues and Eigenvector
      • SciPy Sub Package – Statistics, Weave and IO
    • Topic Covered:

      • Introduction to Pandas
      • Understanding DataFrame
      • View and Select Data Demo
      • Missing Values
      • Data Operations
      • Knowledge Check
      • File Read and Write SupportPreview
      • Knowledge Check-Sequence it Right
      • Pandas Sql Operation
    • Topic Covered:

      • Machine Learning Approach
      • How it Works
      • Supervised Learning Model Considerations
      • Scikit-Learn
      • Supervised Learning Models – Linear Regression
      • Supervised Learning Models – Logistic Regression
      • Unsupervised Learning Models
      • Pipeline
      • Model Persistence and Evaluation
    • Topic Covered:

      • NLP Overview
      • NLP Applications
      • Knowledge check
      • NLP Libraries-Scikit
      • Extraction Considerations
      • Scikit Learn-Model Training and Grid Search
    • Topic Covered:

      • Introduction to Data Visualization
      • Knowledge Check
      • Line Properties
      • (x,y) Plot and Subplots
      • Knowledge Check
      • Types of Plots
    • Topic Covered:

      • Web Scraping and Parsing
      • Knowledge Check
      • Understanding and Searching the Tree
      • Navigating options
      • Navigating a Tree
      • Modifying the Tree
      • Parsing and Printing the Document
    • Topic Covered:

      • Why Big Data Solutions are Provided for Python
      • Hadoop Core Components
      • Python Integration with HDFS using Hadoop Streaming
      • Python Integration with Spark using PySpark
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    You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email. 1. Online ,By deposit the mildain bank account 2. Pay by cash team training center location
    Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.
    Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.
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    We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
    Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy
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