Data Science With Python Course : Hands-On Data Science

Numpy, Pandas, Matplotlib, Scikit-Learn, WebScraping, Data Science, Machine Learning, Pyspark, statistics, Data Science

What you'll learn
You will Learn one of the most in demand skill of 21st century Data Science
Add Data science skills : python, numpy, pandas, plotly, tableau, machine learning, statistics, probability in your resume
Apply linear regression and logistics regression on real dataset.
Crash course on python
Apply matrix operation with Numpy - Numerical python library
Visualize your data with mother of all visualisation library available in Python : MatplotLIb
Perform Data analysis, wrangling and cleaning with pandas library
Get hands on with interactive visualisation library Plotly
Getting start with data visualization tool, Tableau
Data Pre-processing technique - Missing data, Normalization, one hot encoding,
Importing data in Python from different sources, Files
Web Scraping to download web page and extract data
Data scaling and transformation
Exploratory Data analysis
Feature engineering process in Machine Learning system design
Machine learning theory
Apache spark installation : pyspark
Getting started with spark session
Mathey required for machine learning : Statistics, probability
Setup Data Science Virtual machine on Microsoft Azure Cloud
Requirements
Basic of Python programming
High school mathematics
Description
Welcome to Complete Ultimate course guide on Data Science and Machine learning with Python.Have you ever thought about How amazon gives you product recommendation, How Netflix and YouTube decides which movie or video you should watch next,Google translate translate one language to another, How Google knows what is there in your photo, How Android speech Recognition or Apple siri understand your speech signal with such high accuracy.If you would like algorithm or technology running behind that, This is first course to get started in this direction.==============================================This course has more than 100 - 5 star rating. What previous students have said: "This is a truly great course! It covers far more than it's written in its name: many data science libraries, frameworks, techniques, tips, starting from basics to advanced level topics. Thanks a lot! ""This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment.""learning valuable concepts and feeling great.Thanks for this course.""Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good""i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . it can be improved by including some more examples and real life data but overall i would suggest every beginner to have this course.""The instructor is so good, he helps you in all doubts within an average replying time of one hour. The content of the course and the way he delivers is great."==================================================Why Data Science Now?Data Scientist: The Sexiest Job of the 21st Century - By Harvard Business reviewThere is huge sortage of data scientist currently software industry is facing.The average data scientist today earns $130,000 a year by glassdoor.Want to join me for your journey towards becoming Data Scientist, Machine Learning Engineer.This course has more than 100+ HD - quality video lectures and is over 13+ hours in content.This is first introductory course to get started data analysis, Machine learning and towards AI algorithm implementationThis course will teach you - All Basic python library required for data analysis process.Python crash courseNumerical Python - NumpyPandas - data analysisMatplotlib for data visualizationPlotly and Business intelligence tool TableauImporting Data in Python from different sources like .csv, .tsv, .json, .html, web rest Facebook APIData Pre-Processing like normalization, train test split, Handling missing data Web Scraping with python BeautifulSoup - extract value from structured HTML DataExploratory data analysis on pima Indian diabetes datasetVisualization of Pima Indian diabetes datasetData transformation and Scaling Data - Rescale Data, Standardize Data, Binarize Data, normalise dataBasic introduction to What is Machine Learning, and Scikit learn overview Its type, and comparison with traditional system. Supervised learning vs Unsupervised LearningUnderstanding of regression, classification and clusteringFeature selection and feature elimination technique.And Many Machine learning algorithm yet to come. Data Science Prerequisite : Basics of Probability and statisticsSetup Data Science and Machine learning lab in Microsoft Azure CloudThis course is for beginner and some experienced programmer who want to make career in Data Science and Machine learning, AI.Prerequisite:basic knowledge in python programming (will be covered in python )High School mathematicsEnroll in this course, take look at brief curriculum of this course and take first step in wonderful world of Data.See you in field.Sincerely,Ankit Mistry

Overview

Section 1: Introduction

Lecture 1 Download and Install Anaconda - Windows

Lecture 2 Download and Install Anaconda - Ubuntu Linux

Lecture 3 Overview Of Jupyter Notebook

Lecture 4 Notes About Course

Lecture 5 Course FAQ

Lecture 6 Join Online Classroom

Section 2: Python crash course

Lecture 7 Introduction - Python

Lecture 8 Python - Number, String, Variable

Lecture 9 Python - List, tuples, Dictionary, Set

Lecture 10 Python - If/else, Looping

Lecture 11 Python - Function, Lambda, Map

Lecture 12 Python Exercise

Section 3: Data analysis with Numpy

Lecture 13 Introduction - Numpy - Numerica Python

Lecture 14 Numpy array

Lecture 15 Numpy array operations

Lecture 16 Indexing, Slicing - Numpy array

Lecture 17 Numpy Exercise

Section 4: Data analysis with Pandas

Lecture 18 Introduction - Pandas

Lecture 19 Pandas - Introduction to Series

Lecture 20 Pandas - Introduction to Dataframe

Lecture 21 Dataframe - Index, Multiindex

Lecture 22 Handling Missing Data - dropna, fillna

Lecture 23 Grouping data

Lecture 24 Read, Write .csv, .html, excel file

Lecture 25 Visualization of data with pandas

Section 5: Data Visulization with Matplotlib

Lecture 26 Introduction

Lecture 27 Why Visualization ?

Lecture 28 MatplotLib - Basic plotting, Plotting terminology

Lecture 29 MatplotLib - Subplots

Lecture 30 Matplotlib - Special plot

Section 6: Data visualization - plotly

Lecture 31 Plotly - introduction

Lecture 32 Basic plotting - plotly

Lecture 33 Exercise : Extend Basic Plot

Lecture 34 Plotly scatter and line chart

Lecture 35 Plotly - Bar chart

Lecture 36 Exercise : Extend Bar Chart

Lecture 37 Plotly - Bubble chart

Lecture 38 Plotly - Histogram and Distribution plot

Section 7: Data visualization with Tableau

Lecture 39 Introduction to Tableau and Installation

Lecture 40 Insight -1

Lecture 41 Insight - 2

Lecture 42 Load Data in Tableau

Lecture 43 Save Tableau Worksheet

Section 8: Introduction to Data

Lecture 44 Introduction to Data, Continuous and Discrete Data

Lecture 45 Nominal and Ordinal Data

Section 9: Importing Data in python

Lecture 46 Introduction

Lecture 47 Reading Plain text file

Lecture 48 Reading .csv file

Lecture 49 Reading Excel and .m Matlab file

Lecture 50 Read Sqlite Database

Lecture 51 Fetch Data from Remote file

Lecture 52 Fetch Data from Facebook API

Section 10: Data Preprocessing

Lecture 53 Introduction

Lecture 54 Reading Data

Lecture 55 Handling Missing Data

Lecture 56 Categorical Data

Lecture 57 Splitting Data in Training and Testing Set

Lecture 58 Normalize Data

Section 11: Web Scraping

Lecture 59 Introduction - Web Scraping

Lecture 60 What is Web Scraping

Lecture 61 Web Scraping Process

Lecture 62 Search Element by TagName and TagByClass

Lecture 63 How to use developer tools in browser.

Lecture 64 Practical Activity

Section 12: Exploratory Data analysis

Lecture 65 EDA of pima indian diabetes dataset

Lecture 66 Visualize pima indian diabetes dataset

Section 13: Data transformation and Scaling Data

Lecture 67 Introduction

Lecture 68 Rescale data - Standardize data

Lecture 69 Normalize Data - Binarize Data

Lecture 70 Practical Activity

Section 14: Moving towards Machine Learning

Lecture 71 What is Machine Learning - In Layman term

Lecture 72 Traditional system of computing vs Machine Learning

Lecture 73 Formal Definition of Machine Learning

Lecture 74 How Machine Learning system works

Lecture 75 Different Types of Machine Learning system- Supervised vs Unsupervised learning

Lecture 76 Parametric vs Non-parametric machine learning system

Lecture 77 Machine Learning system design and Scikit learn

Lecture 78 Machine Learning application

Lecture 79 Ask yourself to learn any machine learning algorithm

Section 15: Feature selection for Machine Learning

Lecture 80 Introduction to feature selection

Lecture 81 Univariate feature selection

Lecture 82 Recursive feature elimination

Lecture 83 Principal component analysis

Lecture 84 Remove feature with low variance

Lecture 85 Tree based method for feature selection

Section 16: K nearest neighbour

Lecture 86 Section introduction

Lecture 87 KNN algorithm - Intitution

Lecture 88 Choose K and distance metric

Lecture 89 About KNN algorithm

Lecture 90 Implement KNN from scratch

Section 17: Linear Regression

Lecture 91 Introduction

Lecture 92 Python Implementation - Step 1

Lecture 93 Python Implementation - Step 2

Lecture 94 Python Implementation - Step 3

Section 18: Logistic Regression

Lecture 95 Introduction

Lecture 96 Python Implementation - Step 1

Lecture 97 Python Implementation - Step 2

Section 19: Big Data analysis with Apache Spark - PySpark Python

Lecture 98 Introduction

Lecture 99 What is Apache Spark

Lecture 100 Introduction to Installation

Lecture 101 Installation Part - 1 and 2

Lecture 102 Installation Part - 3 and 4

Lecture 103 Installation Instruction Windows

Lecture 104 Spark Session

Lecture 105 Import JSON data into Dataframe

Lecture 106 What next?

Section 20: -------- Appendix -------

Lecture 107 Create Python virtual environment -1

Lecture 108 Create Python virtual environment -2

Lecture 109 Conda Command - I

Lecture 110 Conda Command - II

Lecture 111 Python : Numbers & Math operators

Lecture 112 Python : Variables and Datatypes

Lecture 113 Python : Dynamic Typing in Python

Lecture 114 Python : String

Lecture 115 Python : Boolean variable and conditional logic

Lecture 116 Python : Looping in Python

Section 21: Data Science in Cloud

Lecture 117 Data Science in Cloud -1

Lecture 118 Data Science in Cloud - 2 (Microsoft Azure)

Lecture 119 Install tensorflow, Keras and NLTK on Azure VM

Section 22: Data Science other field

Lecture 120 Data Science as Interdisciplinary field.

Lecture 121 Statistics & Probability

Lecture 122 Mathematics

Lecture 123 Visualization

Lecture 124 Database and Computer Science

Lecture 125 Big data Technology

Lecture 126 Machine Learning

Lecture 127 Deep Learning

Lecture 128 Natural language Processing

Section 23: Prerequisite for Machine Learning and Data Science

Lecture 129 Welcome to Mathematics Prerequisite

Section 24: -------- Probability --------

Lecture 130 Permutations

Lecture 131 Permutations Exercise

Lecture 132 Combinations

Lecture 133 Introduction to Probability

Lecture 134 Union, Intersection of complement of event

Lecture 135 Independent and dependent event

Section 25: -------- Probability Puzzles --------

Lecture 136 Probability interview question - 1

Lecture 137 Probability interview answer - 1

Lecture 138 Probability interview question - 2

Lecture 139 Probability interview answer - 2

Lecture 140 Probability interview question - 3

Lecture 141 Probability interview answer - 3

Lecture 142 Probability interview question - 4

Lecture 143 Probability interview answer - 4

Lecture 144 Probability interview question - 5

Lecture 145 Probability interview answer - 5

Section 26: -------- Statistics --------

Lecture 146 Measure of central tendency

Lecture 147 Mean vs Median

Lecture 148 Measure of Dispersion

Lecture 149 Quartiles and Interquartile range

Lecture 150 Correlation vs Causality

Lecture 151 Co-variance and Pearson correlation

Lecture 152 Measure Statistical Parameter with Microsoft Excel

Section 27: Bonus Special Offer

Lecture 153 Discount for other courses

Anyone who is interested in DataScience,Anyone who wants to learn - How to analyze data,Those who want to make career in Data analytics, Machine learning, DataScience

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