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خانه » فروشگاه » Python Data Analyst: 25 Days for A-Z Data Analysis in Python

Python Data Analyst: 25 Days for A-Z Data Analysis in Python

سرفصل های دوره

Master Python for A-Z Data Analysis and Become Pro Data Analyst with Basics to Hands-on Coding Exercises and Assignments


1 - Start Here MUST Follow the Instructions
  • 1 - Instructions to accomplish the course.html
  • 2 - Python cheatsheet for data analysis.html
  • 2 - python-data-analysis-sheet.zip
  • 3 - Resources.zip
  • 3 - Resources used in the course.html

    • 2 - Data Analysis and Its Application
  • 4 - 1.Un-DA.pdf
  • 4 - Understanding analyzing data
  • 5 - 4.Application-DA.pdf
  • 5 - Realworld application of data analysis

    • 3 - Data Analysis Tools Techniques and Methods
  • 6 - 9.Methods-of-DC.pdf
  • 6 - Various aspects of data cleaning
  • 7 - 20.Joining.pdf
  • 7 - Various aspects of Joining datasets
  • 8 - Methods of exploratory data analysis Part 1
  • 9 - Methods of exploratory data analysis Part 2
  • 10 - 12.13.14.Methods-of-EDA.pdf
  • 10 - Methods of exploratory data analysis Part 3

    • 4 - Statistical Analysis Methods and Techniques
  • 11 - 6.Sampling-methods.pdf
  • 11 - Population vs sample and its methods
  • 12 - 23.Types-of-Statistics.pdf
  • 12 - Types of statistical data analysis
  • 13 - A Recap on descriptive statistics methods
  • 14 - Inferential statistics Part 1 Ttests and ANOVA
  • 15 - 25.26.27.methods-of-ds-and-is.pdf
  • 15 - Inferential statistics Part 2 Relationships measures
  • 16 - 28.Lin-reg.pdf
  • 16 - Inferential statistics Part 3 Linear regression

    • 5 - Clarifying the Concept of Hypothesis Testing
  • 17 - 36.Hypothesis-Testing.pdf
  • 17 - Hypothesis testing for inferential statistics
  • 18 - 37.Selecting-Appropriate-Statistical-Test.pdf
  • 18 - Selecting statistical test and assumption testing
  • 19 - 38.CSP.pdf
  • 19 - Confidence level significance level pvalue
  • 20 - 39.Making-Decision-and-Conclusion.pdf
  • 20 - Making decision and conclusion on findings
  • 21 - 40.Example-HT.pdf
  • 21 - AZ statistical analysis and hypothesis testing

    • 6 - Data Transformation and Visualisation Methods
  • 22 - Techniques for data transformation Part 1
  • 23 - 17.18.Mthods-of-DTP.pdf
  • 23 - Techniques for data transformation Part 2
  • 24 - Several methods of data visualization Part 1
  • 25 - Several methods of data visualization Part 2
  • 26 - 45.46.47.Data-Visualization-Methods.pdf
  • 26 - Several methods of data visualization Part 3

    • 7 - Data Modeling with Machine Learning Model
  • 27 - 41.ML-in-Data-Analysis.pdf
  • 27 - Importance of ML in data analytics
  • 28 - 42.Types-of-Machine-Learning.pdf
  • 28 - Widely used machine learning models
  • 29 - 43.steps-in-ML.pdf
  • 29 - Steps in developing machine learning model

    • 8 - Setting Up Python and Jupyter Notebook
  • 30 - Installing Python and Jupyter Notebook Mac.html
  • 30 - Mac.pdf
  • 31 - Installing Python and Jupyter Notebook Windows.html
  • 31 - Windows.pdf
  • 32 - More alternative methods Check the article.html

    • 9 - Starting with Variables to Data Types
  • 33 - Getting started with first python code
  • 34 - Assigning variable names correctly
  • 35 - Various data types and data structures
  • 36 - Converting and casting data types
  • 37 - Starting with Variables to Data Types.html
  • 37 - starting-with-variables-to-data-types.zip

    • 10 - Various Operators in Python Programming
  • 38 - Arithmetic operators
  • 39 - Comparison operators
  • 40 - Logical operators and or not
  • 41 - Operators in Python Programming.html
  • 41 - operators-in-python-programming.zip

    • 11 - Dealing with Data Structures
  • 42 - Lists creation indexing slicing modifying
  • 43 - Sets unique elements operations
  • 44 - Dictionaries keyvalue pairs methods
  • 45 - Several data structures.html
  • 45 - dealing-with-data-structures.zip

    • 12 - Conditionals Looping and Functions
  • 46 - Conditional statements if elif else
  • 47 - Nested logical expressions in conditions
  • 48 - Looping structures for loops while loops
  • 49 - Defining creating and calling functions
  • 50 - Conditions loops and functions.html
  • 50 - conditionals-looping-and-functions.zip

    • 13 - Sequential Cleaning and Modifying Data
  • 51 - 1.loading-data.pdf
  • 51 - Preparing notebook and loading data
  • 52 - 2.identify-missing-values.pdf
  • 52 - Identifying missing or null values
  • 53 - 3.imputing-missing-values.pdf
  • 53 - Method of missing value imputation
  • 54 - 4.checking-data-types.pdf
  • 54 - Exploring data types in a dataframe
  • 55 - 5.removing-inconsistent-value.pdf
  • 55 - Dealing with inconsistent values
  • 56 - 6.assigning-data-type.pdf
  • 56 - Assigning correct data types
  • 57 - 7.dealing-with-duplicates.pdf
  • 57 - Dealing with duplicated values
  • 58 - Sequential data cleaning and modifying.html
  • 58 - data-loading-and-cleaning.zip

    • 14 - Various Aspects of Data Manipulation
  • 59 - 8.sorting-data.pdf
  • 59 - Sorting data by column and order
  • 60 - 9.boolean-filtering.pdf
  • 60 - Filtering data with boolean indexing
  • 61 - 10.query.pdf
  • 61 - Query method for precise filtering
  • 62 - 11.is-in.pdf
  • 62 - Filtering data with isin method
  • 63 - 12.loc-and-iloc.pdf
  • 63 - Slicing dataframe with loc and iloc
  • 64 - 13.combining-conditions.pdf
  • 64 - Filtering data for many conditions
  • 65 - Various aspects of data manipulation.html
  • 65 - data-sorting-and-filtering.zip

    • 15 - Merging and Concatenating Dataframes
  • 66 - 14.joining-data.pdf
  • 66 - Joining dataframes horizontally
  • 67 - 15.concatenating-data.pdf
  • 67 - Concatenate dataframes vertically
  • 68 - Merging and concatenating dataframes.html
  • 68 - merging-and-joining-dataframes.zip

    • 16 - Applied Exploratory Data Analysis Methods
  • 69 - 16.value-counts.pdf
  • 69 - Frequency and percentage analysis
  • 70 - 17.descriptive.pdf
  • 70 - Descriptive statistics and analysis
  • 71 - 18.group-by.pdf
  • 71 - Group by data analysis method
  • 72 - 19.pivot-table.pdf
  • 72 - Pivot table analysis all in one
  • 73 - 20.crosstab.pdf
  • 73 - Crosstabulation analysis method
  • 74 - 21.correl.pdf
  • 74 - Correlation analysis for numeric data
  • 75 - Applied exploratory data analysis.html
  • 75 - applied-exploratory-data-analysis.zip

    • 17 - Exploring Data Visualisations Methods
  • 76 - 22.methods-used-in-visualisation.pdf
  • 76 - Understanding visualisation tools
  • 77 - 23.bar-chart.pdf
  • 77 - Getting started with bar charts
  • 78 - 24.stacked-or-clustered.pdf
  • 78 - Stacked and clustered bar charts
  • 79 - 25.pie-chart.pdf
  • 79 - Pie chart for percentage analysis
  • 80 - 26.line-plot.pdf
  • 80 - Line chart for grouping data analysis
  • 81 - 27.histogram.pdf
  • 81 - Exploring distribution with histogram
  • 82 - 28.scatterplot.pdf
  • 82 - Correlation analysis via scatterplot
  • 83 - 29.heatmap.pdf
  • 83 - Matrix visualisation with heatmap
  • 84 - 30.boxplot.pdf
  • 84 - Boxplot statistical visualisation method
  • 85 - Exploring data visualisations methods.html
  • 85 - data-visualisations.zip

    • 18 - Several Data Transformation Methods
  • 86 - 31.check-distribution.pdf
  • 86 - Investigating distribution of numeric data
  • 87 - 32.normality-test.pdf
  • 87 - Shapiro Wilk test of normality
  • 88 - 33.square-root-transformation.pdf
  • 88 - Starting with square root transformation
  • 89 - 34.log-transformation.pdf
  • 89 - Logarithmic transformation method
  • 90 - 35.boxcox-transformation.pdf
  • 90 - Boxcox power transformation method
  • 91 - 36.yeojohnson-transformation.pdf
  • 91 - YeoJohnson power transformation method
  • 92 - Practical data transformation methods.html
  • 92 - transformation-methods.zip

    • 19 - Statistical Tests and Hypothesis Testing
  • 93 - 37.one-sample-ttest.pdf
  • 93 - One sample ttest
  • 94 - 38.independent-sample-t-test.pdf
  • 94 - Independent sample ttest
  • 95 - 39.one-way-anova.pdf
  • 95 - One way Analysis of Variance
  • 96 - 40.chi-test-for-ind.pdf
  • 96 - Chi square test for independence
  • 97 - 41.pearson-correlation.pdf
  • 97 - Pearson correlation analysis
  • 98 - 42.linear-regression.pdf
  • 98 - Linear regression analysis
  • 99 - Statistical tests and hypothesis testing.html
  • 99 - statistical-tests-and-hypothesis-testing.zip

    • 20 - Exploring Feature Engineering Methods
  • 100 - 43.feature-generation.pdf
  • 100 - Generating new features
  • 101 - 44.datetime-data.pdf
  • 101 - Extracting day month and year
  • 102 - 45.feature-encoding.pdf
  • 102 - Encoding features LabelEncoder
  • 103 - 46.feature-binning.pdf
  • 103 - Categorizing numeric feature
  • 104 - 46.feature-mapping.pdf
  • 104 - Manual feature encoding
  • 105 - 47.creating-dummies.pdf
  • 105 - Converting features into dummy
  • 106 - Feature engineering methods.html
  • 106 - feature-engineering-methods.zip

    • 21 - Data Preprocessing for Machine Learning
  • 107 - 48.selecting-features.pdf
  • 107 - Selecting features and target
  • 108 - 49.standard-scaling.pdf
  • 108 - Scaling features StandardScaler
  • 109 - 50.minmax-scaler.pdf
  • 109 - Scaling features MinMaxScaler
  • 110 - 51.PCA.pdf
  • 110 - Dimensionality reduction with PCA
  • 111 - 52.train-test-split.pdf
  • 111 - Splitting into train and test set
  • 112 - Preprocessing for machine learning.html
  • 112 - preprocessing-for-machine-learning.zip

    • 22 - Predictive Analytics Regression Machine Learning
  • 113 - 1.LR-model-ML.pdf
  • 113 - Linear regression machine learning
  • 114 - 2.DTR-model-ML.pdf
  • 114 - Decision tree regressor machine learning
  • 115 - 3.RFR-model-ML.pdf
  • 115 - Random forest regressor machine learning
  • 116 - Regression machine learning.html
  • 116 - regression-machine-learning.zip

    • 23 - Predictive Analytics Classification Machine Learning
  • 117 - 4.LGR-model-ML.pdf
  • 117 - Logistic regression machine learning
  • 118 - 5.DTC-model-ML.pdf
  • 118 - Decision tree classification machine learning
  • 119 - 6.RFC-model-ML.pdf
  • 119 - Random forest classification machine learning
  • 120 - Classification machine learning.html
  • 120 - classification-machine-learning.zip

    • 24 - Data Segmentation with KMeans Clustering
  • 121 - Calculating within cluster sum of squares
  • 122 - 7.selecting-best-k.pdf
  • 122 - Selecting optimal number of clusters
  • 123 - 8.developing-k-means.pdf
  • 123 - Application of KMeans machine learning
  • 124 - Data segmentation with KMeans clustering.html
  • 124 - data-segmentation-with-kmeans-clustering.zip

    • 25 - Final Project Sports Data Analytics
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