Data Science with Python- A to Z

This program proposes ample training on the most desired Data Science and Machine Learning skills also touching base with the human intelligence.

Beginner 0(0 Ratings) 2 Students enrolled
Created by AMSPARKS Team Data Science Last updated Wed, 12-Jan-2022 English
What will i learn?
  • Practical hands-on learning
  • Technical mentorship
  • Career Coaching
  • Placement Support

Curriculum for this course
57 Lessons 16:47:17 Hours
Welcome
3 Lessons 01:01:11 Hours
  • Welcome
  • Orientation 01:01:11
  • Orientation PDF
  • ENVIRONMENTAL SETUP
  • Environmental Set Up - Video 01:01:46
  • Git Basics
  • GIT BRANCH
  • GIT AND GITHUB INTRO
  • ASSIGNMENT-1: GIT
  • Git Basics 00:00:00
  • Jupyter shortcut keys
  • Python-Variables, Datatypes and Operators
  • python Decision Making
  • Python -Iterative statements
  • Python- User Input
  • Python strings
  • Python Basics- Video 01:28:17
  • Role of Statistics in Data Science
  • Population and Sample
  • Types of Data in Statistics
  • Measures of Central Tendency
  • Quantiles and Skewness
  • Measures Of Dispersion
  • Scatter Diagram
  • Youtube Link for Lecture
  • Application of Statistics in DS 02:02:14
  • Python Data Structure 00:30:56
  • Python Functions 00:30:55
  • Python Functions Lambda Map Filter Reduce 00:42:27
  • Python OOPS 00:32:46
  • Numpy & Pandas 01:02:04
  • Numpy & Pandas EDA 00:44:13
  • Visualization EDA 01:50:00
  • SQL-CREATE DB,TABLE AND CONSTRAINTS
  • sql select
  • SQL 1 00:41:21
  • SQL 2 00:41:22
  • SQL 3 Joins 00:37:02
  • SQL 4 Views 00:50:34
  • SQL 5 TRIGGERS 00:38:03
  • Web Scrapping 00:36:06
  • Flipkart Web Scraping 00:26:24
  • Data Cleaning and Summarising
  • Overview
  • Assessment Learning Outcomes
  • Assessment Details
  • Task 1: Data Preparation
  • Task 1: Data Preparation (Contd.)
  • Task 2: Report
  • Assessment Criteria
  • Assessment Criteria (Contd.)
  • Submission Format
  • Academic Integrity and Plagiarism
  • Dataset & Dataset_Information
  • Mini Project 1 00:19:02
  • GitBash Navigation Mini Project 00:30:34
  • Iframe1
Requirements
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Description

Section 1- Describes what data science, its applications, process and its environment setup.

Section 2- Gains hands on practice with python basics like operators, variables, conditional statements, python data structures, OOPS, iterators, decorators, file handling, modules needed for data science.

Section 3- This Introduces you to the mathematical concepts that needed for the course like statistics-descriptive, inferential, probability and distributions, hypothesis testing and A/B testing.

Section 4-Gains a hands on practice with python numpy, pandas, matplotlib and seaborn which are needed for data analytics to import datasets, analyze and visualize.

Section 5 and 6- Learn about scikit-learn, EDA , feature Engineering, machine learning models, evaluation metrics in a detailed manner.

Section 7- It covers Deep learning concepts, NLP concepts, web scraping, JSON, api, and Big data, SQl and NO SQL.

Section 8- Capstone Project- Students synthesize and apply knowledge which they gained throughout the course in data science programming for working with datasets and get the conclusion of the datasets.

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Includes:
  • 16:47:17 Hours On demand videos
  • 57 Lessons
  • Full lifetime access
  • Access on mobile and tv