Data science has become a great trend in computational and predictive statistical analysis. It’s also used by various organizations to make data-driven decisions. With all the challenges faced, Python has become an indispensable tool for the data science analyst and an important tool for any data scientist.

Vskills Certification Course in Data Science with Python will provide a means of transcending the theory of data science with the help of Python and many other integrated toolsets.

In this course, you will learn the following concepts in depth. 

  • Introduction to Data Science
  • Python Essentials for Data Science
  • Data ScienceToolBox
  • Importing & Cleaning Data
  • Data Visualization with Statistical Thinking
  • Introduction to Machine Learning with Scikit-Learn
  • Practice with Case Studies


* You will receive an online access to e-learning (videos), hard copy material is not applicable to this course.


Why should one take Data Science with Python Certification?

This Course is intended for Individuals wanting to understand a deeper level of data science using more advanced techniques and operations and individuals wanting to expand their knowledge of Python while learning data science; IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts etc.

Earning Vskills Data Science with Python Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.


Who will benefit from taking Data Science with Python Certification?

IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts etc.


Companies that hire Vskills Certified Data Science with Python Professional

Data Science with Python is one of the faster growing filed and are in great demand. Companies like KPMG, Accenture, TCS & Cognizant specializing in Data Science related activities are constantly looking for certified professionals.


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Table of Content


1. Introduction to Data Science

  • Introduction to data science
  • What is Big Data
  • Using Data Wisely
  • Application Examples
  • Data Science Process Flow


2. Python Essentials for Data Science

  • Installing python and text editor
  • Introduction to python
  • Python Objects
  • Exercise 1
  • Indexing & Slicing
  • Python lists, tuples & dictionaries
  • Mutable & Immutable Objects
  • User-defined Functions
  • Built-ins & Methods
  • Control Flow Tools
  • Modules, Libraries & Packages
  • Introduction to Jupyter Notebook
  • Essential Data Science Packages


3. Data ScienceToolBox - 1

  • Introduction to Numpy
  • Creating a Numpy Array
  • More Array Creation techniques
  • Exercise 1
  • Indexing, Slicing& Iterating with Numpy Arrays
  • Exercise 2
  • Introduction to Matplotlib
  • Plotting with the plot function
  • Histogram
  • Scatter Plot
  • Box Plot
  • Adding a Legend
  • Creating Subplots
  • Lambda Functions
  • List Comprehensions


4. Data Science ToolBox - 2

  • Introduction to Pandas
  • Pandas Series
  • Pandas DataFrames
  • Pandas DataFrames 2
  • Input-Output Tools
  • Start your first Case Study
  • Iterators
  • Generators


5. Importing & Cleaning Data

  • Importing Flat Files
  • Pickled Files
  • Importing from a URL
  • Using APIs & JSONs
  • Inspecting Data Types
  • Inspecting Outliers
  • Finding Correlation & Duplicates
  • Handling Missing Data
  • Regular Expressions


6. Data Visualization with Statistical Thinking

  • Introduction to Seaborn Package
  • SeabornDistplot
  • SeabornDistplot 2
  • SeabornJointplot
  • SeabornHexplot
  • SeabornPairplot
  • Seaborn Boxplot
  • SeabornViolinplot&Swarmplot
  • SeabornCountplot
  • Heatmap
  • Exercise – Studying the feature correlation
  • Feature Selection
  • Feature Scaling
  • Applying Feature Scaling & Feature Extraction
  • Feature Extraction using PCA


7.Introduction to Machine Learning with Scikit-Learn

  • Introduction to Machine Learning
  • Supervised & Unsupervised Machine Learning
  • Classification
  • Regression
  • Clustering
  • Scikit-Learn - Naïve Bayes Algorithm
  • Scikit-Learn - Decision trees
  • Scikit-Learn - Random Forest
  • Scikit-Learn - Linear Regression
  • Scikit-Learn - Logistic Regression
  • Scikit-Learn - Support Vector Machines
  • Scikit-Learn – kNN(K Nearest Neighbours)
  • Scikit-Learn – K Means


8. Practice with Case Studies

“Exam scheduling to be done through user account” / “Exam once scheduled cannot be cancelled”
Date of Examination
15-Dec-2018
16-Dec-2018
05-Jan-2019
06-Jan-2019
19-Jan-2019
20-Jan-2019
02-Feb-2019
03-Feb-2019
16-Feb-2019
17-Feb-2019
02-Mar-2019
03-Mar-2019
16-Mar-2019
17-Mar-2019
06-Apr-2019
07-Apr-2019
20-Apr-2019
21-Apr-2019
Examination Time
01:00 PM - 02:00 PM
02:30 PM - 03:30 PM
04:00 PM - 05:00 PM
05:30 PM - 06:30 PM
10:00 AM - 11:00 AM
11:30 AM - 12:30 PM

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Tags: Data Science, Python, data science with Python, Python for data science