Data Science in Python

Learn with hands-on examples in 40hours

About the Course

Acquiring knowledge and industry ready in Data Analytics tools and techniques for data manipulation and cleaning of data using Python.

Course in Brief

Python 3.x (Any Platform) with Pycharm and Jupyter Notebook

Python crash course, 

Interactive learning with Jupyter notebooks,

Data analysis, Beautiful soup, PySpark,
Machine learning, data visualization, web scraping,

Computer Vision & Open CV

Prerequisites for this course

No prior programming language knowledge,

Comfortable with variables (dependent and independent), linear/quadratic equations, graphs of functions, histograms, probability and statistics.

Basic Computer H/W and S/W Knowledge

Desired to Learn
Learners’ must have a system with any 64-bit OS, preferably Windows 10

Course Level: Intermediate

Mode of delivery: Online (English)

Course Fee: Rs 14500.00

Instructor: From Inductry

Support: Online Text Chat with the Instructor

Certification and Job Assistance

Associated Courses:
Deep Learning
Python Programming
Machine Learning

Course content

Python Crash Course & Libraries for Data Science

1. Download, Install Python in various platforms and Setting the environmental variables
- IDEs & Settings
- Basic Tokens (Data Types, Operators, Input/Output,     Control Structure)

2. Data Structure using the container types (String, List, Set, Tuple, Dictionary)

3. Working with Functions / Modules / Libraries

4. Working with CSV
 - Understand Data with Descriptive Statistics.
 - Understand Data with Visualization.

5. Mathematical Computing with python(NumPy)

6. Data Manipulation with Pandas

7. Python for Data Visualization-Matplotlib

8. Python for Data Visualization – Tableau

Data Science

9. Beautiful soup

10. PySpark

11. Machine Learning with Project Overview and Project Solutions
     - Linear and Logistic Regression
     - Decision Tree
     - Random Forest
     - K-Nearest Neighbour,
     - SVM
     - K-Mean

12. Principal Component Analysis

13. Computer Vision & Open CV 
      1. Images Manipulations 
      2. Image Segmentation
      3. Object Detection
      4. Face, People and Car Detection
      5. Face Analysis and Fulters
      6. Machine Learning in Computer Vision
      7.  Motion Analysis & Object Tracking 

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