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Data Science With Python Course Hands-On Data Science

سرفصل های دوره

1 - Introduction
  • 1 - Download and Install Anaconda Windows
  • 1 - anaconda linux installation.zip
  • 1 - anaconda mac os installation.zip
  • 1 - anaconda windows installation.zip
  • 2 - Download and Install Anaconda Ubuntu Linux
  • 3 - Overview Of Jupyter Notebook
  • 3 - jupyter notebook documentation.zip
  • 4 - Notes About Course
  • 5 - Course FAQ.html
  • 6 - Join Online Classroom.html

  • 2 - Python crash course
  • 7 - Introduction Python
  • 7 - Python-Crash-Course.zip
  • 8 - Python Number String Variable
  • 9 - Python List tuples Dictionary Set
  • 10 - Python Ifelse Looping
  • 11 - Python Function Lambda Map
  • 12 - Python Exercise.html

  • 3 - Data analysis with Numpy
  • 13 - Introduction Numpy Numerica Python
  • 13 - Numpy.zip
  • 14 - Numpy array
  • 15 - Numpy array operations
  • 16 - Indexing Slicing Numpy array
  • 16 - visualize numpy.zip
  • 17 - Numpy Exercise.html

  • 4 - Data analysis with Pandas
  • 18 - Introduction Pandas
  • 18 - Pandas.zip
  • 19 - Pandas Introduction to Series
  • 20 - Pandas Introduction to Dataframe
  • 21 - Dataframe Index Multiindex
  • 22 - Handling Missing Data dropna fillna
  • 23 - Grouping data
  • 24 - Read Write csv html excel file
  • 25 - Visualization of data with pandas

  • 5 - Data Visulization with Matplotlib
  • 26 - Introduction
  • 26 - MatPlotLib.zip
  • 27 - Why Visualization.html
  • 28 - MatplotLib Basic plotting Plotting terminology
  • 29 - MatplotLib Subplots
  • 30 - Matplotlib Special plot

  • 6 - Data visualization plotly
  • 31 - Plotly introduction
  • 31 - plotly.zip
  • 32 - Basic plotting plotly
  • 33 - Exercise Extend Basic Plot.html
  • 34 - Plotly scatter and line chart
  • 35 - Plotly Bar chart
  • 36 - Exercise Extend Bar Chart.html
  • 37 - Plotly Bubble chart
  • 38 - Plotly Histogram and Distribution plot

  • 7 - Data visualization with Tableau
  • 39 - Introduction to Tableau and Installation
  • 40 - Insight 1
  • 40 - countries-of-the-world.csv
  • 41 - Insight 2
  • 42 - Load Data in Tableau
  • 42 - countries-of-the-world.csv
  • 43 - Save Tableau Worksheet

  • 8 - Introduction to Data
  • 44 - Introduction to Data Continuous and Discrete Data
  • 45 - Nominal and Ordinal Data

  • 9 - Importing Data in python
  • 46 - Importing-Data.zip
  • 46 - Introduction
  • 47 - Reading Plain text file
  • 47 - news.zip
  • 48 - Reading csv file
  • 48 - mnist.csv
  • 48 - titanic.csv
  • 49 - ExcelTest.xlsx
  • 49 - MatlabTest.zip
  • 49 - Reading Excel and m Matlab file
  • 50 - Read Sqlite Database
  • 50 - SqliteTestDb.zip
  • 51 - Fetch Data from Remote file
  • 52 - Fetch Data from Facebook API

  • 10 - Data Preprocessing
  • 53 - Data-Preprocesing.zip
  • 53 - Introduction
  • 54 - Data.csv
  • 54 - Reading Data
  • 55 - Handling Missing Data
  • 56 - Categorical Data
  • 57 - Splitting Data in Training and Testing Set
  • 58 - Normalize Data

  • 11 - Web Scraping
  • 59 - Introduction Web Scraping
  • 59 - Web-Scraping.zip
  • 60 - What is Web Scraping
  • 61 - Web Scraping Process
  • 62 - Search Element by TagName and TagByClass
  • 63 - How to use developer tools in browser
  • 64 - Practical Activity.html

  • 12 - Exploratory Data analysis
  • 65 - EDA of pima indian diabetes dataset
  • 66 - Visualize pima indian diabetes dataset

  • 13 - Data transformation and Scaling Data
  • 67 - Introduction
  • 68 - Rescale data Standardize data
  • 69 - Normalize Data Binarize Data
  • 70 - Practical Activity.html

  • 14 - Moving towards Machine Learning
  • 71 - What is Machine Learning In Layman term
  • 71 - towards-ML.zip
  • 72 - Traditional system of computing vs Machine Learning
  • 73 - Formal Definition of Machine Learning
  • 74 - How Machine Learning system works
  • 75 - Different Types of Machine Learning system Supervised vs Unsupervised learning
  • 76 - Parametric vs Nonparametric machine learning system
  • 77 - Machine Learning system design and Scikit learn
  • 78 - Machine Learning application
  • 79 - Ask yourself to learn any machine learning algorithm

  • 15 - Feature selection for Machine Learning
  • 80 - Introduction to feature selection
  • 81 - Univariate feature selection
  • 82 - Recursive feature elimination
  • 83 - Principal component analysis
  • 84 - Remove feature with low variance
  • 85 - Tree based method for feature selection

  • 16 - K nearest neighbour
  • 86 - Section introduction
  • 87 - KNN algorithm Intitution
  • 88 - Choose K and distance metric
  • 89 - About KNN algorithm
  • 90 - Implement KNN from scratch.html
  • 90 - KNN.zip

  • 17 - Linear Regression
  • 91 - Introduction
  • 92 - Python Implementation Step 1
  • 93 - Python Implementation Step 2
  • 94 - Python Implementation Step 3

  • 18 - Logistic Regression
  • 95 - Introduction
  • 96 - Python Implementation Step 1
  • 97 - Python Implementation Step 2

  • 19 - Big Data analysis with Apache Spark PySpark Python
  • 98 - Introduction.html
  • 99 - What is Apache Spark
  • 100 - Introduction to Installation
  • 101 - Installation Part 1 and 2
  • 102 - Installation Part 3 and 4
  • 103 - Installation Instruction Windows.html
  • 104 - Spark Session
  • 105 - Import JSON data into Dataframe
  • 105 - student.zip
  • 106 - What next.html

  • 20 - Appendix
  • 107 - Create Python virtual environment 1
  • 108 - Create Python virtual environment 2
  • 109 - Conda Command I
  • 110 - Conda Command II
  • 111 - Python Numbers & Math operators
  • 112 - Python Variables and Datatypes
  • 113 - Python Dynamic Typing in Python
  • 114 - Python String
  • 115 - Python Boolean variable and conditional logic
  • 116 - Python Looping in Python

  • 21 - Data Science in Cloud
  • 117 - Data Science in Cloud 1
  • 118 - Data Science in Cloud 2 Microsoft Azure
  • 119 - Install tensorflow Keras and NLTK on Azure VM

  • 22 - Data Science other field
  • 120 - Data Science as Interdisciplinary field
  • 121 - Statistics & Probability
  • 122 - Mathematics
  • 123 - Visualization
  • 124 - Database and Computer Science
  • 125 - Big data Technology
  • 126 - Machine Learning
  • 127 - Deep Learning
  • 128 - Natural language Processing

  • 23 - Prerequisite for Machine Learning and Data Science
  • 129 - Welcome to Mathematics Prerequisite.html

  • 24 - Probability
  • 130 - Permutations
  • 130 - Permutation-and-Combination.pptx
  • 130 - Probability.pptx
  • 131 - Permutations Exercise
  • 132 - Combinations
  • 133 - Introduction to Probability
  • 134 - Union Intersection of complement of event
  • 135 - Independent and dependent event

  • 25 - Probability Puzzles
  • 136 - Probability interview question 1.html
  • 137 - Probability interview answer 1
  • 138 - Probability interview question 2.html
  • 139 - Probability interview answer 2
  • 140 - Probability interview question 3.html
  • 141 - Probability interview answer 3
  • 142 - Probability interview question 4.html
  • 143 - Probability interview answer 4
  • 144 - Probability interview question 5.html
  • 145 - Probability interview answer 5

  • 26 - Statistics
  • 146 - Measure of central tendency
  • 147 - Mean vs Median
  • 148 - Measure of Dispersion
  • 149 - Quartiles and Interquartile range
  • 150 - Correlation vs Causality
  • 151 - Covariance and Pearson correlation
  • 152 - Measure Statistical Parameter with Microsoft Excel
  • 152 - Measure-Statistics-with-Excel.xlsx

  • 27 - Bonus Special Offer
  • 153 - Discount for other courses.html
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    شناسه: 9030
    حجم: 4343 مگابایت
    مدت زمان: 941 دقیقه
    تاریخ انتشار: ۱۵ فروردین ۱۴۰۲
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